• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
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    • 专利摘要:
    The present invention relates to systems and methods for tracking, over time, data recorded in systems. The techniques described herein include the ability to detect and classify system activities and produce indicators of normal system operation and anomaly detection. Systems and methods according to the present invention may represent activities taking place in the controlled system in such a way that the temporal characteristics of the activities can be recorded and used for detecting, classifying and / or detecting anomalies, This can be particularly useful when dealing with complex systems and / or activities.
    公开号:FR3037679A1
    申请号:FR1655821
    申请日:2016-06-22
    公开日:2016-12-23
    发明作者:David Stephen Hardwick；Johan Fredrik Markus Svensen；Honor Powrie
    申请人:GE Aviation Systems Ltd；
    IPC主号:
    专利说明:

    [0001] BACKGROUND OF THE INVENTION The present invention relates generally to state tracking systems and methods and, more particularly, to systems and methods for tracking and tracking the state of the present invention. methods for verifying and detecting anomalies using a mixture of hidden Markov Models. Many systems can benefit from status tracking in which the operational situation of one or more organs of a system and / or the entire system can be actively monitored. In particular, the status monitoring may include a verification of the proper functioning of the organ / organs or the system and / or a detection of a malfunction of the organ / organs or the system. Examples of systems that can benefit from state tracking include aeronautical aircraft systems, oil and gas prospecting and / or extraction systems (eg oil rigs). , industrial gas turbines and many other complex systems. The detection of abnormal activity within a system can have many advantages including, for example, the rapid identification of organs that require maintenance for the system to return to proper operation, the prevention of system failures. downstream, the cost reduction related to periods of unavailability of systems, etc. More generally, state tracking can enable the system operator to better manage system equipment and devices. However, in many systems there are a large number of complex and different organs for which state tracking poses many difficulties. For example, an oil rig may include one or more well blocking blocks (BOPs) that may be used, for example, to plug, control and / or control oil and / or gas wells to provide a water well. prevent rashes. In some cases, BOPs may be submerged under water or placed elsewhere, in places that are difficult to observe. Each BOP may ordinarily have a number of different members (eg, jaws, annular shutters, etc.). Similarly, each BOP can usually be used to perform different tasks or activities. Thus, the monitoring of states during the operation of various BOP members to perform various activities faces great difficulties, especially for submerged or otherwise difficult to observe BOPs. In another example, an aeronautical system such as an aircraft engine also has a large number of organs that serve to perform successively different operations or activities. Enormous amounts of data describing operating states of an aircraft can be collected using various sensors or other feedback mechanisms of the aircraft. For example, full flight data may be collected from commercial aircraft engines for analysis in order to ensure proper operation of the aircraft. However, interpreting and summarizing this huge amount of data can be a cumbersome, time-consuming and error-prone task. Thus, improved systems and methods for tracking states of complex systems are desirable. Aspects and advantages of embodiments of the present invention will be partially set forth in the description below or may be learned from the description or practice of the embodiments.
    [0002] A first exemplary aspect of the present invention relates to a state tracking system for monitoring states in an oil or gas prospecting or extraction system that includes one or more block (s) for blanking wells. The state tracking system includes one or more hydrophones (s) which receive / receive acoustic signals caused by the operation of the well block (s) and, from the acoustic signals, produce / produce a series of acoustic data indicating operating states in the well block (s). The state tracking system comprises a verification and anomaly detection unit implemented by one or more processor (s). The verification and anomaly detection unit uses a Hidden Markov Model Mixture to: check the operation of the well block (s) based on the acoustic data; and / or determining, from the acoustic data, that an anomaly has occurred in the well block (s). Another exemplary aspect of the present invention relates to a computerized method for performing state tracking for a system. The method comprises obtaining, by one or more computer device (s), a series of system data indicating operating states in one or more members of the system. The method includes introducing, by the at least one computer device (s), at least a portion of the system data series into a mixture of hidden Markov Models. The method comprises receiving, by the at least one computer device (s), at least one rating and at least one aptitude score from the Hidden Markov Pattern Mix. The method comprises determining, by the at least one computer device, at least in part according to the rating (s) and the skill rating (s), an operational situation of the organ (s) ( s) of the system. The operational situation indicates whether an anomaly has occurred in the organ (s) of the system.
    [0003] Yet another example of an aspect of the present invention relates to a computerized method for performing verification and anomaly detection. The method comprises receiving, by one or more computer device (s), a series of system data. The method comprises extracting one or more element (s) by the computer device (s) from the system data series. The method includes determining, by the computing device (s), a class prediction and / or a proficiency score for the system data set using a mixture. Hidden Markov Models. The method includes determining, by the computer device (s), that an abnormality has occurred based on the class prediction and / or the aptitude score. Variations and modifications can be made to these exemplary aspects of the present invention. These characters, aspects and advantages, and others, of various embodiments will become more clearly apparent from the following description and appended claims. The accompanying drawings, which form an integral part of this specification, illustrate embodiments of the present invention and, together with the description, serve to explain the corresponding principles. The invention will be better understood from the detailed study of some embodiments taken by way of nonlimiting examples and illustrated by the appended drawings in which: FIG. 1A represents a schematic diagram of an exemplary system of FIG. monitoring operating states of a system for prospecting and / or extracting oil and gas according to exemplary embodiments of the present invention; Fig. 1F3 shows an exemplary flowchart of an exemplary state tracking system according to exemplary embodiments of the present invention; FIG. 2 represents a block diagram of an exemplary state tracking system according to exemplary embodiments of the present invention; FIG. 3 represents a flowchart of an exemplary method for state tracking according to exemplary embodiments of the present invention; Figure 4 shows a block diagram of an exemplary networked environment according to exemplary embodiments of the present invention; and FIG. 5 is a block diagram of a computer system or operating environment according to exemplary embodiments of the present invention. We will now consider in detail embodiments of the invention, one or more example (s) is / are illustrated in the drawings. Each example is presented as a non-limiting explanation of the invention. In fact, those skilled in the art will understand that various modifications and variations can be made to the present invention without departing from the scope and spirit of the invention. For example, details illustrated or described in the context of one embodiment may be used with another embodiment to provide yet another embodiment. It is therefore understood that the present invention covers such modifications and variations as falling within the scope of the appended claims and their equivalents. Examples of aspects of the present invention relate to systems and methods that utilize a mixture of hidden Markov Models for state tracking. In particular, aspects of the present invention relate to the creation of a probabilistic Mix of Hidden Markov Models (CMMs) from a determined data set collected in a system to be monitored. Other aspects of the present invention relate to the use of MMMC to perform state tracking for the system. More particularly, it is possible to collect a series of data indicating operating states in one or more organs of the system. For example, the data set may comprise data from various types of sensors, data collection devices, or other feedback devices that control states in the organ (s) or for the entire system. A plurality of elements can be extracted from the data series. The data series can be fully or partially marked. For example, the tagging of data may be done manually, during data collection, by competent persons and / or known factual information. The data set can be used to teach MMMC in a process generally called learning. After being trained, the resulting MMMC can be used for verification, classification and / or anomaly detection. In particular, new, unmarked data collected in the same system can be introduced into the MMMC. In response to the introduction of the new data, the MMMC can deliver at least one class prediction and / or at least one aptitude score during a process generally called prediction. In some implementations, elements are extracted from the new data before the introduction of these data into the MMMC. In some implementations, class prediction or grading may identify a particular activity, action, or operation most closely resembled the entered data (e.g., matches elements from matching training data). to this activity or operation). In addition, in some implementations, the aptitude score may indicate confidence in the class prediction or may be some other measure indicating the degree of resemblance of the inputted data with the activity or operation identified by the prediction. of class. The class prediction (s) and / or proficiency note (s) issued by the MMMC can / can be used to verify the proper functioning of the part of the controlled system (eg the part where data collected or data collected). For example, in some implementations, the MMMC may issue a single rating and / or a single score that simply indicates whether the entered data is classified as indicating normal operation of the system or is classified as indicating abnormal operation. of the system. For example, in some embodiments, a single proficiency score delivered by the MMMC may be compared with a threshold value. A proficiency score above the threshold value may indicate that the system is operating correctly, while a score below the threshold may indicate that the system is not functioning properly (eg, an abnormality occurred). In some embodiments, the particular threshold value used may depend on the class prediction made by the MMMC. In other implementations, the MMMC can deliver multiple class predictions and / or proficiency scores. For example, in some implementations, each Hidden Markov Model (CMM) included in the MMMC may deliver a class prediction and a corresponding aptitude score for the entered data set. The class prediction with the highest corresponding fitness score can be selected and used as a prediction provided globally by the MMMC. Thus, the prediction provided by the MMMC can be the safest prediction provided by any of the MMCs included in the MMMMC. In another example, in some implementations, the multiple rankings / notes provided by, the MMMC can respectively identify, over time, multiple potential activities to which the data entered correspond. In particular, multiple rankings / ratings may identify, over time, a sequence of activities / operations. In particular, a tracking system can switch from one activity to another during its operation. For example, during a period of operation, an aircraft may have multiple activities (eg short, long, etc.) and each activity may consist of a number of its own activities or sub-activities. (eg a taxi, take-off, climb, etc.) that take place in a particular order. Similarly, the closure of an exemplary annular BOP may consist of a number of activities or sub-activities with different characteristics which, again, may take place in a particular order. Thus, in certain implementations, the MMMC can deliver a plurality of rankings and a plurality of aptitude scores respectively associated with the plurality of rankings. The plurality of rankings can identify a chronological order of different activities undergone or performed by the system (as demonstrated by the inputted data). The respective aptitude score for each ranking may indicate a confidence that the activity identified by the corresponding ranking has been performed without abnormality. In this way, in certain implementations, if the totality of the aptitude scores for a series of rankings are respectively greater than a plurality of threshold values, it can then be assumed that the whole sequence of activities identified is is unwound within normal operating parameter limits. If, on the other hand, one (or more) of the proficiency scores for the 5 series of rankings is lower than its respective confidence score, an anomaly can then be detected with respect to the activity identified by the classification to which this score corresponds. 'aptitude. As a result, aspects of the present invention can be used to perform state tracking, including anomaly detection, for complex systems 113 that successively, repeatedly, switch from one state or activity to another. 'other. On the other hand, in certain implementations in which each Hidden Markov Model (CMM) included in the MMMC delivers a class prediction and a corresponding aptitude score, the chronological order, described above, of different activities predicted MMMC can be identified by selecting, for any particular time segment or part of the entered data, the class prediction that has the highest corresponding score on the MMMC. Thus, the safest class prediction for each segment of the input data can be used to be delivered by the MMMC, giving a chronological order of predictions that respectively identify the sequence of activities. In an exemplary application of the present invention, aspects of the present invention may be applied to perform state tracking for one or more well block (BOP) blocks of a survey system or oil and gas extraction. In a particular example, hydrophones can be used to collect acoustic data that describes acoustic signals resulting from the operation of the BOPs. In some implementations, the acoustic data may be appropriately transformed and / or partially marked by competent persons. Then, the transformed and / or marked data can be used for learning a CMMM with a structure derived from knowledge about the data and the system and activities of the BOPs. The MMMC that has undergone learning can then be used for activity prediction and anomaly detection based on new data provided by the hydrophones and transformed in the same manner as the training data.
    [0004] In another exemplary application, aspects of the present invention may be applied to perform state tracking for one or more aeronautical system (s) such as aircraft engines. For example, data relating to a complete flight may be introduced into a training MMMC in order to receive predictions (eg verification or anomaly detection) concerning the operational situation of various aeronautical systems. As noted above, such use of MMMC may be particularly advantageous for state tracking for systems that go through a chronological sequence of activities such as taxiing, take-off, climb, etc., as described further. high. In addition, aspects of the present invention are based in part on the basic concepts of probability theory and thus create a clear framework for modifying or extending models. For example, aspects of the present invention allow incorporation of data from new sensors or combination with other (probabilistic) models. In addition, aspects of the present invention provide a commercial advantage by providing a reasoned way of dealing with the uncertainty of models which, without any necessity, are constructed from noisy data. For example, aspects of the present invention allow the association of different costs with different types of activity classification errors, which can be combined with probabilistic predictions of the model to derive decision-making strategies which are 5 expects them to be optimal over time. Although exemplary aspects of the present invention are discussed with reference to blow prevention systems and / or aeronautical systems, the subject described herein may be used with or applied to other systems, vehicles, machines. , industrial or mechanical equipment, or components without departing from the scope of the present invention. Referring now to the figures, examples of aspects of the present invention will be discussed in more detail. Fig. 1A is a block diagram of an exemplary system 10 for monitoring operating states in an oil and gas prospecting and extraction system according to exemplary embodiments of the present invention. invention. For example, the oil and gas prospecting and extraction system may be an oil rig.
    [0005] The oil and gas prospecting and extraction system may include one or more well blocking blocks (BOPs), which may be used, for example, to plug, control and / or control oil wells. and / or gas to prevent breakouts. In some cases, the BOPs 22 may be submerged underwater or placed elsewhere in hard to observe locations. Each BOP 22 may ordinarily comprise a number of different members (eg jaws, annular plugs, etc.). Likewise, each BOP 22 can normally be used to perform a number of different tasks or activities.
    [0006] The operation of the BOPs 22 may generate or otherwise result in acoustic signals 24. For the purposes of the present description, the acoustic signals 24 may comprise any signal that is propagated mechanically in a medium. By way of non-limiting examples, the acoustic signals 24 may comprise a sound wave propagated in a fluid medium such as a gas or water, vibrations caused to propagate in a solid medium and / or some combination of these. The acoustic signals 24 may be perceptible by man or not perceptible by man. The system 10 includes a state tracking system 30 which controls states in the oil and gas prospecting and extraction system. The state tracking system 30 may include one or more hydrophones 32 and an abnormality verification and detection component 34. The hydrophones 32 may control submarine installations (eg BOP 22) and deliver acoustic data relating to the operation of members (eg jaws, annular shutters, etc.). In particular, the hydrophones 32 can receive the acoustic signals 24 and transform the acoustic signals 24 into acoustic data (eg a digital electronic signal or an analog electronic signal). The data transmitted by the hydrophones 32 may be provided to the abnormality check and detection component (VDA component) 34. The VDA component 34 can detect and classify activities occurring in the BOPs 22 based on a application of a mixture of hidden Markov Models to acoustic data. The VDA 34 component may communicate alarms and / or display results to a user. In particular, the VDA component 34 can produce indicators of normal system operation and / or anomaly detection.
    [0007] The VDA component 34 may be the same or similar to the VDA component 204 which will be discussed in more detail with reference to FIG. 2. Although BOPs 22 are shown in FIG. 1A, the state tracking system 30 can be used to control states for other - different - bodies of the oil and gas prospecting and extraction system, in addition to or in place of the BOPs 22. In addition, although hydrophones 32 are shown in FIG. 1A, other data collection devices may be used in addition to or in place of the hydrophones 32.
    [0008] Figure 1B shows an exemplary flowchart of an exemplary state tracking system 100 according to exemplary embodiments of the present invention. The state tracking system 100, as shown, includes a training part 101 and a prediction part 102.
    [0009] The learning part 101 comprises a series of system data 103 which serves for the extraction 104 of elements. System data 103 can be obtained, acquired, or received from a collection of data collection devices. For example, the data collection devices may include, by way of no limitation, a series of sensors, one or more imagers, etc. Extraction 104 of elements can isolate, obtain or extract one or more useful values or characteristics or a series of criteria, rules or element extraction parameters. Parameter extraction parameters can be pre-set, learned or adjusted dynamically. The extracted elements may be provided to a Hidden Markov Model Mix (CMMT) for learning 106. In addition, a series of system data markers 105 may be provided to facilitate the MMCT training 106. For example, a first one of the set of marks 105 may identify an activity or operation associated with a first extracted item or a first set of items pertaining to a particular case or example. One or more MMCs included in the MMMC may / may be trained to recognize the operation from the first extracted element and the first mark. As examples, the MMMC training 106 may implement a Baum-Welch technique (which may also be called the Forward-Backward technique and / or Expect-Maximize algorithm) for learning MMCs.
    [0010] Learning 101 may be temporary or continuous. For example, learning 101 may take place during setup or installation of state tracking system 100. In addition or alternatively, learning 101 may continue during routine operation (e.g. during the prediction 102) of the state tracking system 100 to improve the prediction 102. The prediction part 102 includes new system data 108. Like the system data 103 used in the training part 101, the new system data 108 may be received from the same set of data collection devices or an expanded set of data collection devices. The new system data 108 is provided for the extraction 110 of elements. Element extraction 110 may employ a series of refined parameters during learning part 101 for element extraction 104. The extracted elements are provided (eg, introduced) in the MMMC for prediction 112, the MMMC including MMCs that have been learned during the learning part 101. The prediction 112 can generate, produce or deliver a prediction of Class 114 and / or a pass mark 116. The class prediction 114 and the aptitude score 116 may indicate or verify normal operation of a system associated with the new system data 108. Alternatively, the class prediction 114 and / or the aptitude score 116 may detect an abnormality in the operation of the associated system. For example, if an operation has an aptitude score 116 that does not conform to a predetermined threshold, this may indicate a malfunction. In another example, if the MMMC delivers an uncertain rank in which all possible rankings are given a low pass score (indicating that they are similarly not likely), this may indicate a malfunction. In other implementations, prediction 112 may deliver multiple class 114 predictions and / or aptitude scores 116. In particular, in some implementations, multiple class predictions 114 may respectively identify multiple activities. potential to which the new system data correspond. For example, the multiple rankings / notes 114/116 can identify, over time, a sequence of activities / operations. Thus, in some implementations, the plurality of rankings 114 may identify a chronological order of different activities that the system has undergone or is performing (as demonstrated by the new system data 108). The respective aptitude score 116 for each ranking 114 may indicate a confidence that the activity identified by the corresponding ranking 114 has been executed without abnormality. In this way, in some implementations, if the totality of the aptitude scores 116 respectively exceed a plurality of threshold values, it can then be assumed that the whole sequence of activities identified has taken place within the limits of the parameters of normal running.
    [0011] On the other hand, if one (or more) of the aptitude scores 116 is lower than its respective confidence score, then an anomaly can be detected with respect to the activity identified by the rank 114 corresponding to that proficiency score. . In this way, the prediction part 102 can be used to perform state tracking, including anomaly detection, for complex systems that, over time, move multiple times from one situation or activity to another. another. The learning part 101 (including element extraction 104 and MMMC training 106) may be executed or implemented by one or more computing devices, which include one or more processors executing instructions stored on a medium. durable computer-readable. For example, in some implementations, element extraction 104 and MMMC training 106 correspond to or include computer logic used to provide desired functions. Thus, element extraction 104 and MMMC training 106 may each be implemented in hardware, application specific circuitry, microprograms, and / or software controlling a versatile processor. In one embodiment, the element extraction 104 and the MMMC training 106 each correspond to files of a program code stored in a storage device, loaded into memory and executed by a processor, or may be implemented using computer program products, for example, computer-executable instructions, which are stored on a computer-readable storage medium such as a random access memory, a hard disk, or optical or magnetic media. Similarly, the prediction part 102 (including the element extraction 110 and the MMMC prediction 112) can be executed or implemented by the computer device (s), which comprises / comprises one or more processors executing instructions stored on a durable computer-readable medium. The computer device (s) that implement / implement the prediction part 102 may be identical, different or have common points with respect to the device (s) computer (s) implementing the learning part 101. For example, in some implementations, the extraction 110 of elements and the prediction 112 by the MMMC correspond to or still include a computer logic used to provide desired functions. Thus, element extraction 110 and MMMC prediction 112 may each be implemented in hardware, application specific circuits, microprograms, and / or software controlling a versatile processor. In one embodiment, element extraction 110 and prediction 112 by the MMMC each correspond to program code files stored in a storage device, loaded into memory and executed by a processor, or may be implemented using computer program products, e.g. computer executable instructions, which are stored on a computer-readable storage medium such as a random access memory, a hard disk, or optical or magnetic media. Referring now to Figure 2, there is illustrated an exemplary state tracking system 200 according to various aspects described herein. The status tracking system 200 may include a set of data collection devices 202 and an abnormality verification and detection component 204. The collection device set 202 may comprise N data collection devices, N being an integer. The collection devices 202 may comprise, by way of no limitation, a set of sensors. For example, the data collection devices 202 may include passive acoustic systems that utilize hydrophones. The hydrophones may control subsea installations (eg, well-closing blocks (BOPs)) and provide acoustic data relating to the operation of members (eg, jaws, annular obturators, etc.). .). The data from the data collection devices 202 may be provided to the VDA component 204 (VDA component). The VDA component 204 comprises an input component 206, a training component 208 , an extraction component 210 of elements and a prediction component 212 by the MMMC. The component of VDA 204 may also include one or more processors (not shown) and a memory (not shown). The processor (s) may be any suitable processing device (eg a processor core, a microprocessor, an ASIC, a user programmable gate array (FPGA), a controller, a microcontroller, etc.) and may / may be a single processor or a plurality of cooperating processors. The memory may include one or more computer-readable medium (s), such as a RAM, ROM, EEPROM, EPROM, flash memory devices, memory sticks, magnetic discs, etc., and combinations thereof. The memory may store instructions that are executed by the processor to perform operations. Referring again to Figure 2, the input component 206 obtains, acquires or receives data from the data collection devices 202. As explained, the data may include, for example, acoustic data relating to the operation of the data. BOP organs. In addition, the data may include training data and / or actual operating data. In addition, the input component 206 can realize or implement any necessary or desirable treatment. Learning component 208 may teach MMCs at least in part according to a set of training data. The learning component 208 may further include a brand component 209. The brand component 209 may receive or manage tags that facilitate learning of the MMCs. For example, a series of marks may be created by a technician to identify an operation included in the data for learning. According to brands and data, a set of MMCs can be trained to recognize, identify or classify an operation. The element extraction component 210 may isolate, obtain or extract data one or more value (s) or element (s) of interest. The item extraction component 210 may comprise a parameter component 211 that determines, receives, or manages a set of criteria, rules, or item retrieval parameters. For example, the parameters may be entered or entered into the parameter component 211 by a user, a professional, or a technician. In addition or alternatively, the parameters can be dynamically learned or selected by the parameter component 211. The element extraction component 210 can use the criteria, rules or parameters to identify the elements and parameters. extract them from the data. The MMMC Prediction component 212 applies, operates, or uses MMCs that have been trained (eg, using the training component 208) for the verification and detection of operating anomalies in the elements. extracts. The MMMC prediction component may include a class 214 component and a fitness component 216. Returning to a previous example, an MMC included in the MMMC can identify and verify an extracted element such as an annular opening of a BOP. In addition or according to another possibility, if none of the MMCs can reliably or satisfactorily identify an operation associated with an extracted element (eg all classes receive a low score as well), the prediction component 212 MMMC can then determine that the transaction is an anomaly. The class component 214 may indicate whether an operation belongs to a known class or still perform a class prediction and the aptitude component 216 determines and provides a note about the suitability of a potential MMC identifying the operation. For example, the aptitude component 216 may provide a score as a value indicative of a likelihood of proper identification of the operation by an MMC. If the note does not meet a predetermined threshold, the MMMC predictor 212 may determine that the operation is an anomaly. The results from the prediction component 212 by 20 MMMC can be provided to a user 220 and / or used to trigger an alarm 218. For example, if it is determined that an operation associated with a BOP is an anomaly, the alarm 218 can be triggered to warn, alert or notify staff. In addition or alternatively, the results can be provided to the user 220, for example via a computer interface. The VDA component 204 (comprising the input component 206, the training component 208, the mark component 209, the element extraction component 210, the parameter component 211, the prediction component 212 by MMMC, the class 214 component and the capability component 216) may or may also include computer logic for performing the desired functions. Thus, each of these components can be implemented in hardware, application specific circuitry, microprograms and / or software controlling a versatile processor. In one embodiment, each of these components corresponds to files of a program code stored in a storage device, loaded into memory and executed by a processor, or may be implemented using computer program products, by example computer-executable instructions, which are stored on a computer-readable storage medium such as a RAM, a hard disk or optical or magnetic media. Figure 3 shows a flowchart of an exemplary method 300 for state tracking according to exemplary embodiments of the present invention. In 302, the state tracking system obtains from the system to control a series of training data. For example, the training data set may comprise data transmitted by various types of sensors or other feedback devices that control states in for the entire system. In certain implementations in one or more organs and work, a plurality of elements can be extracted, in 302, from the series of data. For example, one or more interesting values or other elements may be isolated, obtained or extracted from a set of criteria, rules or element extraction parameters. Element extraction parameters can be pre-set, learned, or adjusted dynamically.
    [0012] In some implementations, the training data set may also be partially or fully 302 marked at 302. For example, the data tagging may be done manually, during the data collection, by competent persons and / or based on known factual information.
    [0013] In 304, the state tracking system submits a Hidden Markov Model Mix (CMMT) to learning using the training data. As examples, the MMMC training 106 may implement a Baum-Welch technique (which may also be called the Forward-Backward technique and / or the Expect-Maximize algorithm) for teaching MMCs. At 306, the state tracking system obtains a series of new system data. For example, like the system data obtained at 302, the new system data obtained at 306 can be received from the same set of data collection devices or from an expanded set of data collection devices. In some implementations, the new system data may be produced for the extraction of elements 306. The extraction of elements may employ a set of refined parameters during the training 304.
    [0014] In 308, the state tracking system introduces at least a portion of the system data set into the MMMC. In 310, the status tracking system receives a rating and / or at least one aptitude rating issued by the MMMC. In some implementations, the ranking may identify a particular activity, action, or operation that most resembles the introduced system data set. On the other hand, in some implementations, the aptitude score may indicate a trust in the ranking or other measure which indicates the degree of similarity of the introduced system data set with the activity or operation identified by the ranking. .
    [0015] In 312, the status tracking system determines an operational situation of the system to be controlled at least in part according to the classification and / or the score of the received certificate (s). By way of example, in some implementations, the MMMC may issue, at 310, a single rating and / or aptitude score (s) simply indicating whether the entered data is classified as indicating a normal operation of the system. or are classified as indicating abnormal operation of the system. For example, in some implementations, to determine the operational situation at 312, the status tracking system can compare with a threshold value the unique fitness score delivered by the MMMC. A proficiency score above the threshold may indicate that the system is functioning properly, while a proficiency score below the threshold may indicate that the system is not functioning properly (eg, an abnormality occurred). In some implementations, the particular threshold value used may depend on the class prediction made by the MMMC. In other implementations, the MMMC can deliver multiple class predictions and / or 310 suitability scores. By way of example, in some implementations, each Hidden Markov Model (MMC) included in the MMMC can deliver a class prediction and a corresponding aptitude score for the entered data set. The class prediction which has the highest corresponding fitness score can be selected at 312 to be used to determine the operational situation of the system (eg by comparison with a threshold value). Thus, the prediction delivered by the MMMC may be the safest prediction made by any of the MMCs included in the MMMC.
    [0016] In another example, in certain implementations, the multiple rankings / ratings issued by the MMMC may respectively identify, over time, multiple potential activities to which the inputted data correspond. In particular, multiple rankings / ratings may indicate a chronological order of activities / operations.
    [0017] Thus, in some implementations, at 310, the MM.4C may output a plurality of rankings and a plurality of aptitude scores respectively associated with the plurality of rankings. The plurality of rankings can identify a chronological order of different activities performed or executed by the system (as demonstrated by the inputted data). The respective aptitude score for each ranking may indicate a confidence in the fact that the activity identified by the corresponding ranking has been executed without abnormality. In this way, in some implementations, to determine the operational situation of the system at 312, the status tracking system can respectively compare the plurality of aptitude scores with a plurality of threshold values. If all of the aptitude scores for a series of smit rankings, respectively, are greater than the threshold value, then it can be assumed that all of the identified activities have occurred within the limits of normal operating parameters. If, on the other hand, one (or more) of the aptitude scores for the classification series is lower than its respective confidence score, an anomaly may then be detected in the activity identified by the ranking. 'aptitude. In this way, aspects of the present invention can be used to perform state tracking, including anomaly detection, for complex systems which, in time, pass several times a situation or activity. to another.
    [0018] On the other hand, in some implementations in which the each Hidden Markov Model (MMC) included in the MM NIC 3037679 provides a class prediction and a corresponding aptitude score at 310, the chronological order, described more the top, of different activities predicted by the MMMC can be identified at 312 by selecting, for any particular chronological segment or part (e) of data entered, the class prediction which has the highest correspondence score issued by the MMIVIC. Thus, the safest class prediction for each segment of the inputted data can serve as a prediction delivered by the MMMC, giving a chronological order of predictions which respectively identify the sequence of activities. The chronological order of the predictions may be analyzed for anomaly detection as described above (eg by comparing the fitness scores from the selected predictions with respective threshold values).
    [0019] Figure 4 illustrates in schematic form an exemplary networked or distributed computing environment. The distributed computing environment includes computer objects 1510, 1512, etc. and computer objects or devices 1520, 1522, 1524, 1526, 1528, etc. which may include programs, methods, data memories, programmable logic, etc., represented by applications 1530, 1532, 1534, 1536, 1538, and data memory (s) 1540. Computer objects 1510, 1512, etc. and computer objects or devices 1520, 1522, 1524, 1526, 1528, etc. may include different devices such as Personal Digital Assistants (PDAs), audio / video devices, mobile phones, MP3 players, personal computers, laptops, etc. Each computer object 1510, 1512, etc., and computing objects or devices 1520, 1522, 1524, 1526, 1528, etc., may communicate, directly or indirectly, with one or more other computing objects 1510, 1512, etc., and computer objects or devices 1520, 1522, 1524, 1526, 1528, etc., through the communication network 1550. Although the communication network 1550 is shown as a single element on Figure 4, it may include other computer objects and computing devices that provide services for the system of Figure 4, and / or it may represent multiple interconnected networks, not shown. Each computer object 1510, 1512, etc. or computer object or device 1520, 1522, 1524, 1526, 1528, etc. may also contain an application such as applications 1530, 1532, 1534, 1536, 1538, which could use an API, or other object, software, firmware and / or hardware, allowing communication with or implementation of Dynamic code creation and memory management techniques for COM objects according to various embodiments of the present invention. There are all kinds of systems, components, and network configurations that support distributed computing environments. For example, computer systems can be connected to each other by wired or wireless systems, local area networks or wide area networks. At present, many networks are coupled to the Internet, which is a widely distributed computing infrastructure and encompasses many different networks, although any network infrastructure can be used for example communications reaching systems for dynamic code creation and memory management for COM objects described in various embodiments. Thus, it is possible to use a large number of network topologies and network infrastructures, such as client / server, peer-to-peer or hybrid architectures. The "client" is a member of a class or group who uses the services of another class or group to which he or she is not related. A client may be a process, i.e. in short, a series of instructions or tasks that require service provided by another program or process. The customer process uses the requested service without having to "know" any practical details about the other program or the service itself. In a client / server architecture, particularly in a networked system, a client is typically a computer that accesses shared network resources provided by another computer, eg a server. In the illustration of Figure 4, by way of non-limiting example, computer objects or devices 1520, 1522, 1524, 1526, 1528, etc. can be considered as customers and computer objects 1510, 15 1512, etc. can be considered as servers where computer objects 1510, 1512, etc. acting as servers providing data services, including receiving data from computer objects or devices 1520, 1522, 1-524, 1526, 152 $, etc., storing data, processing data, transmitting data; data to customer computer objects or devices 1520, 1522, 1524, 1526, 1528, etc., although, depending on the circumstances, any computer may be considered to be a client, a server, or both. A server is usually a remote computer system accessible via a remote or local network such as the Internet or radio network infrastructures. The client process may be active in a first computer system and the server process may be active in a second computer system, communicating with each other via a communication means, thereby providing distribution of functions and allows multiple clients to benefit from the capabilities of the server's information pool. Any software objects used in application of the techniques described herein may be present in isolation or be distributed among multiple devices or computing objects.
    [0020] In a networked environment in which the communication network 1550 or the bus is the Internet, for example, computer objects 1510, 1512, etc. may be web servers with which other computer objects or devices 1520, 1522, 1524, 1526, 1528, etc. communicate using any of a number of known protocols, such as the hypertext transfer protocol (http). Computer objects 1510, 1 5 12, etc. Serving as servers may also serve as clients, eg computer objects or devices 1520, 1522, 1524, 1526, 1528, etc., which may be characteristic of a distributed computing environment. The techniques described herein may advantageously be applied to any device or system for performing state tracking described herein. Therefore, it is contemplated that all kinds of computer devices and handheld, portable and other computer objects will be used in the various embodiments. The remote multi-user computer described hereinafter with reference to FIG. 5 is therefore only one example of a computing device. Although not essential, the embodiments may be partially implemented using an operating system for use by a service designer for a device or object, and / or included in an application software that serves to execute one or more functional aspects of the various embodiments described herein. The software may be described in the general context of computer executable instructions, such as program modules, executed by one or more computers such as workstations, servers, or other client devices. Those skilled in the art will understand that computer systems have various configurations and various protocols can be used to communicate data, so no particular configuration or protocol should be considered as limiting. Figure 5 illustrates an example of a suitable computer system environment 1600 in which one or more aspects of the embodiments described herein can be implemented, although, as noted above, the computer system environment 1600 does not An example of a suitable computer environment is not intended to indicate a limitation as to the extent of its use or functionality. The computer system environment 1600 is also not to be construed as having any dependency or requirement with respect to any component or combination of components illustrated in the computer system environment example 1600. Considering the 5, an example of a remote device 20 for implementing one or more embodiments comprises a multi-purpose computer device in the form of a computer 1610. The components of the computer 1610 may include, by way of no limitation, a unit 1620, a system memory 1630 and a system bus 1621 which couples to the central unit 1620 various system components including the system memory. The computer 1610 typically includes a variety of computer-readable media and may be any existing media accessible to the computer 1610. The system memory 1630 may include computer storage media in the form of volatile and / or volatile memory such as a ROM (ROM) and / or random access memory (RAM). By way of non-limiting example, the system memory 1630 may also include an operating system, application programs, other program modules and program data. In another example, computer 1610 may also include various other media (not shown), which may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD ROM, a versatile digital disc (DVD) or other optical storage disc, magnetic cassettes, magnetic tape, magnetic storage disc or other magnetic storage devices, or other physical and / or non-material media temporarily used to store desired information. A user can enter instructions and information into the computer 1610 using 1640 input devices. A display or other type of display device is also connected to the system bus 1621 through an interface such as the interface. In addition to a display, the computers may include other output peripherals such as speakers and a printer, which may be connected via the 1650 output interface. The 1610 may operate in a remote environment. network or distributed using logical connections, such as network interfaces 1660, to one or more other remote computers such as the remote computer 1670. The remote computer 1670 may be a personal computer, a server, a router , a network PC, a peer device or other common network node, or any other remote bearer or media consumption device, and may include any or all of the elements described above with respect to the computer 1610. The logical connections shown in FIG. 5 include a network 1671 such as a local area network (LAN) or a wide area network ( WAN), but may also include other networks / buses. These networked environments are common in homes, offices, corporate computer networks, intranets and the Internet. The technology discussed here refers to servers, databases, software applications and other computer-based systems, as well as actions taken and information sent to and from those systems. One of ordinary skill in the art will appreciate that the flexibility inherent in computer-based systems allows for a wide variety of configurations, combinations and possible divisions of tasks and functions between and among components. For example, the processes executed by servers and discussed here may be implemented using a single server or multiple servers operating in combination. Databases and applications can be implemented on a single system or distributed across multiple systems. The distributed components can operate in series or in parallel.
    [0021] Although specific elements of various embodiments may be shown in some drawings and not others, it is only for convenience. According to the principles of the present invention, any element of a design may be cited and / or claimed in combination with any element of any other design. The written description uses examples to present the invention, including the best solution, as well as to enable any person skilled in the art to put the invention into practice, including making and using any devices and systems and the perform all 30 processes that are part of it. The patentable scope of the invention is defined by the claims and may include other examples which will be apparent to those skilled in the art. It is understood that these other examples are within the scope of the claims if they include constituent elements that do not conflict with the wording of the claims, or if they include equivalent constituent elements with minor differences from each other. the wording of the claims. In this way, the invention is not limited to any particular embodiment or implementation, but can instead be interpreted, as to its extent, its spirit and scope, according to the appended claims.
    权利要求:
    Claims (20)
    [0001]
    REVENDICATIONS1. A status tracking system (30, 100, 200) for monitoring states in an oil and gas prospecting or extraction system (20) that includes one or more well-closing block (s) (22) ), the status tracking system (30, 100, 200) comprising: one or more hydrophone (s) (32) which: receives / receives acoustic signals (24) caused by the operation of the block (s) well shutter (22); and produces / produces, according to the acoustic signals (24), a series of acoustic data indicative of operating states in the well block (s) (22); and an abnormality checking and detection member (34) implemented by one or more processor (s), the abnormality checking and detection member (34) using a Hidden Markov Model Mixture for: verifies / verifies the operation of the well block (s) (22) from the acoustic data; and determines / determines, from the acoustic data, that an anomaly has occurred in the well block (s) (22).
    [0002]
    The state tracking system (30, 100, 200) according to claim 1, further comprising: an extraction component (210) of elements implemented by one or more processors, the extraction component ( 210) of elements extracting one or more elements of the acoustic data from a series of parameters.
    [0003]
    The state tracking system (30, 100, 200) according to claim 1, further comprising: a training component (208) implemented by one or more processors, the training component (208); ) providing training for a plurality of Hidden Markov Models for inclusion in the Hidden Markov Pattern Mix. 5
    [0004]
    A state tracking system (30, 100, 200) according to claim 1, wherein the abnormality checking and detecting member (34) triggers an alarm (218) in response to a determination of what an anomaly has occurred.
    [0005]
    A computerized method (300) for performing state tracking for a system (20), the method (300) comprising: obtaining (302), by one or more computer device (s) ( 1520, 1522, 1524, 1526, 1528), a series of system data (103, 108) indicating operating states in one or more members of the system (20); Introducing (308) into a mixture of hidden Markov Models, by the computer device (s) (1520, 1522, 1524, 1526, 1528), at least part of the series of system data (103, 108); the reception (310), by the computer device (s) 20 (1520, 1522, 1524, 1526, 1528), of at least one classification (114) and at least one aptitude score (116) issued by the Hidden Markov Model Mix; the determination (312) by the computer device (s) (1520, 1522, 1524, 1526, 1528), at least in part according to the classification (s) (114) and the / the proficiency note (s) (116), of an operational situation of the organ (s) of the system (20), the operational situation indicating whether an anomaly has occurred in the organ (s) of the system (20).
    [0006]
    The computerized method (300) of claim 5, wherein the determination (312) by the computer device (s) (1520, 1522, 1524, 1526, 1528), at least in part 3037679 according to the classification (s) (114) and the proficiency note (s) (116), the operational situation of the organ (s) of the system (20) comprises: the comparison, by the computer device (s) 5 (1520, 1522, 1524, 1526, 1528), the aptitude score (116) with a threshold value; in response to a determination that the proficiency score (116) is greater than the threshold value, the determination by the computing device (s) (1520, 1522, 1524, 1526, 1528), an activity identified by the ranking (114) has proceeded without abnormality in the system (20); and in response to a determination that the proficiency score (116) is below the threshold value, the determination by the computing device (s) (1520, 1522, 1524, 1526, 1528) since an anomaly has occurred in the organ (s) of the system (20).
    [0007]
    7. computerized method (300) according to claim 5, wherein obtaining (302), by the / the computer device (s) (1520, 1522, 1524, 1526, 1528), the data series system (103, 108) comprises obtaining, by the at least one computer device (s) (1520, 1522, 1524, 1526, 1528), a series of acoustic data indicative of operating states in a or a plurality of well shutter blocks (22) of a petroleum drilling system, the acoustic data set being collected by one or more hydrophones (32). 25
    [0008]
    8. computerized method (300) according to claim 5, wherein obtaining (302), by the / the computer device (s) (1520, 1522, 1524, 1526, 1528), the data series system (103, 108) includes obtaining, by the computer device (s) (1520, 1522, 1524, 1526, 1528), a series of aeronautical data relating to a complete flight, indicating operating states in one or more members of an aircraft engine.
    [0009]
    The computerized method (300) according to claim 5, wherein obtaining (302), by the at least one computer device (s) (1520, 1522, 1524, 1526, 1528), the series of system data (103, 108) comprises obtaining, by the at least one computer device (s) (1520, 1522, 1524, 1526, 1528), the system data series (103, 108) collected during the different passages, in time, of the system (20) from one activity to another. 10
    [0010]
    The computerized method (300) according to claim 9, wherein the receiving (310), by the computer device (s) (1520, 1522, 1524, 1526, 1528), of the classification (s) ) (114) and the proficiency note (s) (116) comprises the receipt by the computer device (s) (1520, 1522, 1524, 1526, 1528) of a plurality of rankings (114) and a plurality of aptitude scores (116) respectively associated with the plurality of rankings (114) issued by the Hidden Markov Pattern Mix, each of the various rankings (114) identifying the one, respectively, of the plurality of different activities, and the aptitude score (116) for each ranking (114) indicating that the activity identified by the corresponding ranking (114) has been executed without abnormality.
    [0011]
    The computerized method (300) of claim 9, further comprising: obtaining, by the at least one computing device (1520, 1522, 1524, 1526, 1528), a plurality of threshold values respectively for the plurality of rankings (114); determining (312), by the computer device (s) (1520, 1522, 1524, 1526, 1528), the operative situation of the organ (s) of the system (20) comprising: 3037679 Comparing, by the computer device (s) (1520, 1522, 1524, 1526, 1528), each of the different aptitude scores (116) with the respective threshold value for the classification (114) to which this aptitude score corresponds (116); In response to a determination that all proficiency scores (116) are greater than their respective threshold values, the determination by the computing device (s) (1520, 1522, 1524, 1526, 1528), that the activities identified by the rankings (114) proceeded without abnormality in the system 10 (20); and in response to a determination that the proficiency note (s) (116) is / are lower than their respective threshold values, determining that an anomaly has occurred in the organ (s) of the system (20) during the activity (s) 15 respectively identified by the classification (s) (114) to which / to which correspond / correspond this aptitude note (s) (116).
    [0012]
    The computerized method (300) of claim 5, further comprising, prior to insertion (308), the computer device (s) (1520, 1522, 1524, 1526, 1528), at least the part of the system data series (103, 108) in the Hidden Markov Model Mix: learning (304), by the computer device (s) (1520, 1522, 1524) , 1526, 1528) of the Hidden Markov Model Mix using a series of training data, the training data set being marked.
    [0013]
    Computerized method (300) for performing verification and anomaly detection, the method comprising: receiving (310), by one or more computer device (s) (1520, 1522, 1524, 1526, 1528), a series of system data (103, 108); The extraction (104, 110) of one or more element (s) by the computer device (s) (1520, 1522, 1524, 1526, 1528), from the series of system data (103, 108); determining (312), by the computer device (s) (1520, 1522, 1524, 1526, 1528), a class prediction (114) and / or a proficiency score (116) for the system data series (103, 108) using a mixture of hidden Markov Models; and determining (312), by the computer device (s) (1520, 1522, 1524, 1526, 1528), that an abnormality has occurred from the class prediction (114) and / or the aptitude score (116).
    [0014]
    The method (300) of claim 13, further comprising: triggering by the computer device (s) (1520, 1522, 1524, 1526, 1528) an alarm (218) depending on the anomaly.
    [0015]
    A method (300) according to claim 13, wherein the receiving (310), by the computer device (s) (1520, 1522, 1524, 1526, 1528), of the system data series (103, 108) comprises receiving, by the computer device (s) (1520, 1522, 1524, 1526, 1528), the series of system data (103, 108) provided by one or more sensors .
    [0016]
    16. The method (300) of claim 15, wherein the receiving (310), by the computer device (s) (1520, 1522, 1524, 1526, 1528), of the system data series. (103, 108) provided by one or more sensors comprises receiving, by the computer device (s) (1520, 1522, 1524, 1526, 1528), from the system data series (103, 108) provided by one or more hydrophones (32). 3037679 39
    [0017]
    The method (300) of claim 16, wherein the receiving (310), by the at least one computer device (s) (1520, 1522, 1524, 1526, 1528), of the system data series ( 103, 108) provided by one or more hydrophones (32) comprises receiving, by the computer device (s) (1520, 1522, 1524, 1526, 1528), a series of acoustic data provided. by one or more hydrophones (32).
    [0018]
    18. The method (300) according to claim 17, wherein the reception (310), by the computer device (s) (1520, 1522, 1524, 1526, 1528), of the acoustic data series. provided by one or more hydrophones (32) comprises the reception by the computer device (s) (1520, 1522, 1524, 1526, 1528) of the series of acoustic data associated with the operation of the a well shutter block (22). 15
    [0019]
    The method (300) of claim 13, further comprising: providing to a user, by the at least one computing device (s) (1520, 1522, 1524, 1526, 1528), prediction (114 ) of class and / or of the aptitude score (116). 20
    [0020]
    20. Method (300) according to claim 13, wherein the extraction, by the computer device (s) (1520, 1522, 1524, 1526, 1528), of the / elements from the series. system data set (103, 108) comprises extracting the elements, by the computer device (s) (1520, 1522, 1524, 1526, 1528), from a set of extraction parameters elements.
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    同族专利:
    公开号 | 公开日
    GB2541510B|2017-11-29|
    GB201610889D0|2016-08-03|
    GB2541510A|2017-02-22|
    BR102016014574A2|2016-12-27|
    JP2017021790A|2017-01-26|
    CA2933805A1|2016-12-22|
    US20160371600A1|2016-12-22|
    GB201510957D0|2015-08-05|
    FR3037679B1|2019-12-20|
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    法律状态:
    2017-06-27| PLFP| Fee payment|Year of fee payment: 2 |
    2018-06-26| PLFP| Fee payment|Year of fee payment: 3 |
    2019-05-22| PLFP| Fee payment|Year of fee payment: 4 |
    2020-05-20| PLFP| Fee payment|Year of fee payment: 5 |
    优先权:
    申请号 | 申请日 | 专利标题
    GBGB1510957.2A|GB201510957D0|2015-06-22|2015-06-22|Systems and Methods For Verification And Anomaly Detection|
    GB15109572|2015-06-22|
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