• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    The present invention relates to the technical field of railway devices and provides a method and apparatus for monitoring damage to needle blades. The apparatus includes sensors, a monitoring extension, a monitoring host and a data center, the sensors being installed on needle blades, and transmitting the acquired characteristics to the monitoring extension, the monitoring extension processing the characteristics and then transmitting a processing result to the monitoring host, the monitoring host retreating the result of the processing and then performing a designated distribution, and the data center managing the entire apparatus and controlling a client terminal according to the data re -traitées.
    公开号:FR3025166A1
    申请号:FR1558089
    申请日:2015-09-01
    公开日:2016-03-04
    发明作者:Pengxiang Wang；Dayong Qin；Yunhua Hou；Bin Huang
    申请人:SICHUAN SOUTHWEST JIAOTONG UNIVERSITY RAILWAY DEV CO Ltd；
    IPC主号:
    专利说明:

    [0001] Field of the Invention The present invention relates to the technical field of railway devices, and provides a method and apparatus for monitoring damage to needle blades. Technical Background High-speed needle blades are critical devices that affect safety in high-speed rail infrastructure, which is still a safety link on railway lines and makes maintenance difficult. railway lines. This is why a high-speed needle blade monitoring system is necessary and urgent for the maintenance of high-speed railways. The conventional detection of damage to the rails is mainly carried out by means of railway line monitoring circuits and rail damage inspection vehicles, with which the principle of detecting damage on the rails. said railway line monitoring circuitry is such that, when rails are damaged, the characteristic parameters of the railway line monitoring circuits change, and the damage (such as a break) likely to occur are assessed accordingly; and a principle of track damage inspection vehicles is to detect whether or not damage such as cracks and breakage is occurring at the rail level by means of ultrasonic wave bounce. However, the prior art has the following technical problems: because the railroad track monitoring circuits and the large rail inspection vehicles can not effectively recognize the rapidly developing microcracks. rails, blind zones ("blind zones" in English) exist in the prior art. SUMMARY The object of the present invention is to provide a method and apparatus for monitoring damage to a needle blade so as to solve the technical problems that rapidly developing microcracks can not be recognized. effectively, and blind areas exist when railway monitoring circuits and large inspection vehicles are used to detect rail damage in the prior art. In one aspect, the present invention provides a needle blade damage monitor, which includes sensors, a monitoring extension, a monitoring host, and a data center. The sensors are installed on the needle blades and transmit the acquired characteristics to the monitoring extension; The monitoring extension processes the features to obtain processed data, and transmits the processed data to the monitoring host; the monitoring host classifies the processed data and stores the processed data; and the data center manages the entire apparatus, and more specifically receives and retrieves the data transmitted by the monitoring host, and controls a client terminal according to the re-processed data; the monitoring extension comprises a signal preprocessing module, a local energy data analysis module, and a self-adaptive approximation module. According to a particular embodiment, the monitoring extension comprises a signal preprocessing module, a local energy data analysis module, and a self-adaptive approximation module. According to a particular embodiment, the signal preprocessing module is used to receive the characteristics acquired by the sensors, and to perform charge amplification, hardware filtering, analog / digital conversion and software filtering according to the characteristics, in order to obtain a treatment result. According to a particular embodiment, the characteristics are more specifically the voltage, temperature and humidity data, or the oscillation data. According to a particular embodiment, the local energy data analysis module is used to perform a time domain analysis and a frequency domain analysis according to the processing result of the signal preprocessing module, and to obtain initial characteristic parameters according to a result of the analysis.
    [0002] According to a particular embodiment, the self-adaptive approximation module comprises a blade damage database and performs a self-adaptive approximation of the initial characteristic parameters, according to the blade damage database, in order to obtain damage data.
    [0003] According to a particular embodiment, the monitoring extension further comprises a power supply module and a data sending module; the power module provides power to each module of the monitoring extension; and the sending data module sends the processed damage data to the monitoring host. According to a particular embodiment, the data center manages the maintenance of the pointer blade damage monitoring apparatus, and provides data to the client terminal. According to a particular embodiment, the data center 25 comprises a host communication module, a data loading module, a data interrogation module and a user communication module, the host communication module receiving data from the data communication module. from the monitoring host, the data loading module acquiring the damage data according to the received data, and the data querying module requesting the type of the damage data; thus, the user communication module is triggered to transmit alarm information to the client terminal, and an external alarm device is triggered after the client terminal has received the alarm information.
    [0004] According to a particular embodiment, the client terminal triggers alarms in response to the rail damage events and the failure events of the components of the device. In another aspect, the present invention further provides a method of monitoring damage to needle blades, which comprises the steps of: pretreating the acquired characteristics to obtain a treatment result; performing a local energy data analysis according to the result of the processing, and obtaining initial characteristic parameters according to the result of the analysis; performing a self-adaptive approximation to the initial characteristic parameters, according to a rail damage database, and obtaining damage data according to the result of the treatment; and classification and storage of damage data, and control of a client terminal according to the damage data. In adopting the above-mentioned technical solution, the present invention has the following advantages: 1. As the damage monitor on a needle blade composed of sensors, surveillance extension, surveillance host and data center is adopted, and that in this device the sensors are installed on needle blades and transmit the acquired characteristics to the monitoring extension, the monitoring extension processes the characteristics and then transmits a treatment result to the the monitoring host, the processing host retreats the result of the processing and then performs a designated distribution, and the data center manages the entire apparatus and controls a client terminal according to the re-processed data, the present invention solves the technical problems that microcracks that develop rapidly can not be recognized effectively, and These exist when railroad monitoring circuits and large rail inspection vehicles are used to detect track damage in the prior art, and thus can effectively recognize microcracks on the rails. needle blades, and can eliminate blind areas. 3025166 5 2. Since the client terminal connected to the data center can trigger alarms in response to rail damage events and device component failure events, accidents are avoided, and safety is avoided. is 5 guaranteed. DESCRIPTION OF THE DRAWINGS Fig. 1 is a structural diagram of a needle-blade data monitor according to the embodiment of the present invention; Fig. 2 is a modular diagram of a monitoring extension according to the embodiment of the present invention; Fig. 3 is a modular diagram of a data center according to the embodiment of the present invention; FIG. 4 is a modular diagram of an interaction between monitoring hosts, monitoring extensions, and a data center; and Fig. 5 is a flowchart of a method for monitoring damage to a needle blade.
    [0005] DESCRIPTION OF EMBODIMENTS The present invention provides a method and apparatus for monitoring damage to a needle blade, and resolves the technical problems that rapidly developing microcracks can not be effectively recognized, and blind zones. exist when large rail inspection vehicles are used to detect rail damage in the prior art, and thus can effectively recognize the microcracks on the needle blades and can eliminate the blind areas.
    [0006] In order to solve the technical problem that blind zones exist, the overall idea is as follows: the solution adopts a pointer blade damage monitor, which includes sensors, a monitoring extension, a host of monitoring and a data center, in which the sensors are installed on needle blades, and then transmit the acquired characteristics to the monitoring extension, the monitoring extension processes the characteristics and transmits the data. From the host to the monitoring host, the monitoring host re-processes the processing data downloaded by the monitoring extension and then performs a designated distribution, and the data center manages the entire apparatus and controls a client terminal. according to the re-processed data. In order to better understand the aforementioned technical solution, it will be described below in detail, in combination with the drawings and embodiments. As illustrated in FIG. 1, the needle blade damage monitoring apparatus of the present invention specifically includes sensors 101, a monitoring extension 102, a monitoring host 103 and a data center 104. The sensors 101 are installed on the needle blades and transmit the acquired characteristics to the monitoring extension 102, the monitoring extension 102 processes the characteristics and transmits the processed data to the monitoring host 103, the host of monitoring 103 re-processes the processed data downloaded by the monitoring extension 102 and then performs a designated distribution, and the data center 104 manages the entire apparatus and controls a client terminal according to the processed data. The entire monitoring apparatus provides a link between all hierarchies on a data link, via data transmission channels, and rapidly, reliably and stably transmits information in uplink and downlink, using specific data messages.
    [0007] According to a specific embodiment, piezoelectric sensors are used as sensors 101 to acquire the data of the excitation undergone by the rails, and to extract the rail damage data that can occur, using of a specific algorithm, depending on the rail damage characteristics. Since the cracking signals of the rails correspond to a type of sound emission signal whose intensity is generally low, these weak signals can be recognized by high frequency and high sensitivity data acquisition modules. so that quantification and analysis can be performed on the damage data to recognize and determine the location of the cracks. When this monitoring technique is compared with the prior art, the sensors of the needle blade damage monitor can cover blind surveillance areas of the conventional art in the field of the detection of rail defects, the piezoelectric sensor insulation performance is good, and the track monitoring circuits are not influenced. More specifically, as shown in FIG. 2, the monitoring extension 102 includes a signal preprocessing module 201, a local energy data analysis module 202, and a self-adaptive approximation module 203.
    [0008] The monitoring extension 102 further includes a power supply module 204 for supplying power to each module of the entire monitoring extension 102 and, of course, further comprises a data sending module 205. Power is supplied to the data sending module 205 by the power supply module 204, so that the monitoring extension 102 processes the characteristics and then sends the processed data to the monitoring host 103 at the same time. In a specific embodiment, the signal preprocessing module 201 receives the characteristics acquired by the sensors 101. More specifically, the characteristics acquired can be voltage, temperature, or other data. and humidity, or oscillation data. Then, the signal pretreatment module 201 performs pretreatment such as charge amplification, hardware filtering, analog / digital conversion, and software filtering according to the features, to obtain a processing result. During the acquisition by the sensors 101, piezoelectric sensors having a frequency range of 10 kHz to 300 kHz are selected, and can capture signals as soon as rails exhibit damage. In order to create a link between the detection distance of the piezoelectric sensors and the detection zones, and to determine the location of the damage, signal acquisition cards with 4 * 4 channels are selected. The configurations of the piezoelectric sensors are as follows: 302 5 1 6 6 8 Frequency Number of Range of Sampling Precision Range points channels sampling sampling filtering frequencies 1 MHz sensors 1024 4 * 4 10 kHz- 20 K-16 bit 300 kHz 300 K Next, an analysis of the local energy data is performed on the result of processing the signals subjected to the pre-processing mentioned above in the local energy data analysis module 202. The analysis of the data at Local energy includes a statistical analysis of the temporal characteristics of the data and a statistical analysis of the frequency characteristics of the data. The local energy characteristics of the signals in a frequency domain are extracted according to a result of the analysis, and the statistical values are quantized and transmitted to generate the initial characteristic parameters of the rail damage data. The method specified is as follows: (1) Time Domain Analysis: Time analysis of the damage signals includes maximum value analysis, mean value analysis, and mean squared value analysis. The specific calculation is: X max = max {x ', s (m)} x' - -12n x2. = 1 1 2n X '- - 1-1 X 1 n X2-Xrms = 302 5 1 6 6 9 (2) Frequency domain analysis: the frequency domain analysis of the damage signals includes an analysis of the maximum spectral value in power distribution, a power spectrum density analysis, etc. The specific calculation is the following: uT (v) = feur (t) in '= f T uWei2111't 2 By satisfying f u7,2 (t) d t = f0 uT dv; We obtain 1 -1 -T u2 (t) dt = 2T '2T lu, I (v) 2 dv; Thus, the density of the power spectrum is obtained. By analyzing in detail the time characteristics and frequency characteristics of the rail damage data, the local energy analysis situation of the damage information in the original data can be obtained, and by analyzing the local energy analysis situation obtained and by quantifying and providing the statistical values, the initial characteristic parameters of the damage data can be generated. Once the initial characteristic parameters have been acquired, the initial characteristic parameters are processed in the auto-adaptive approximation module 203 according to the rail damage database to obtain damage data. The specific method is as follows: on the basis of a criterion according to which each quadratic sum provided by an analysis group is the smallest, that is the least squares criterion, all the data of analysis obtained after the initialization of the algorithm are used, and an inverting operation of a matrix is performed by adopting a recursion method. Therefore, the speed of convergence is fast, it is not sensitive to a degree of dispersion of the characteristic values, and the compromise between the speed of convergence and the complexity of the calculation can be realized.
    [0009] Consider the following cost function: J (w) = yld (i) - w "(i) x (12, where d (i) and w11 (i) x (i) become respectively the expected response of the analysis and the output response of the analysis, sys 1 and y is a forgetting factor According to ai (w) - o, R (k) w (k) = r (k) is w obtained; R (k) = expresses the weighted autocorrelation matrix of the acceptable analysis vectors, and r (k) = rk-id * (i) x (i) expresses the related vectors of the acceptable vectors, and the vectors According to the above two formulas, a recursive estimation formula is obtained after the calculation: R (k) = yR (k - 1) + x (k) x "(k) r (k) ) = yr (k - 1) + d * (k) x (k) Then, a recursion formula of the inverse matrix p (k) = R - '(k) can be obtained: p (k) = [ (k - 1) - g (k) xr (k) p (k - We obtain then: p (k) x (k) = g (k) w (k) = R-1 (k) r (k) = p (k) r (k) The formula is then simplified to obtain: e (k) = d * (k) - xll (k) w (k - 1) Through a series of As described above, effective damage data is finally obtained, so that the recognition accuracy and the recognition rate of the damage detection are improved. After obtaining the damage data, the monitoring extension 102 downloads the damage data to the monitoring host 103, the monitoring host 103 provides for the reception, classification and storage of the damage data, and the data center 104 can manage maintenance of the needle blade damage monitor and provide data to the client terminal and each alarm device; the client terminal triggers alarms in response to the damage events and the failure events of the components of the apparatus according to the damage data obtained by the data center 104. The monitoring host 103 receives the data downloaded from the data center. the monitoring extension 102, classifies the damage data, alarm data, received maintenance data, and the like, and simultaneously processes and sends the commands provided by the higher authority. The data center 104 manages the needle blade damage detection apparatus, processes the relevant data according to a precise logic, which includes a classification, a thorough processing, a dispatch to a platform and storing data in the database, and providing data and service support to customers at all levels. As shown in FIG. 3, the data center 104 comprises a data interrogation module 301, a user communication module 302, a host communication module 303 and a data loading module 304. When The monitoring host 103 located at a station receives the damage data acquired by the host communication module 303 from the data loading module 304, the interrogation module 301 searches the data type of the data module 303. damage according to the data and, thus, the user communication module 302 is triggered to transmit alarm information to the client terminal, so as to trigger alarms using short messages, or to inform the client terminal of 35 way to trigger alarms using information provided on the Internet.
    [0010] As shown in FIG. 4, which is a modular diagram of interaction between the monitoring hosts 103, the monitoring extensions 102 and the data center 104, the data center 104 being more specifically a railway office platform, and monitoring extensions 102 and monitoring host 103, may be interacting data with the railway office platform. More specifically, the near side of the monitoring hosts 103 and the monitoring extensions 102 is provided with a downstream communication module and a data sending module, the near side of the paths office platform. of iron is provided with an upstream communication module and a data sending module, the data downloaded by the monitoring hosts 103 and the monitoring extensions 102 are transmitted to the railway office platform. by a protocol stack module located between the upstream communication module and the downstream communication module, and, at the same time, the commands provided by the railway office platform are sent, and the data sent is processed by a status processing module located between the two data sending modules, so that the railway office platform can obtain recognizable data. The client terminal wired or unconnected with the data center 104 may present the pre-alarm information and device status information generated by the needle blade damage monitor, and at the same time users can intuitively observe locations that correspond to needle blade alarms using a dynamic sensor deployment map. On the basis of the same concept as that of the invention, the present application further provides a method of monitoring damage to needle blades. As illustrated in FIG. 5, the method comprises the following steps: S10: pretreatment of the acquired characteristics, in order to obtain a treatment result; S20: performing a local energy data analysis according to the result of the processing, and obtaining initial characteristic parameters according to the result of the analysis; 302 5 1 6 6 13 S30: Performing a self-adaptive approximation on the initial characteristic parameters, according to an established rail damage database, and obtaining damage data according to the result of the processing; 5 S40: Execution of classification and storage of damage data, and control of a client terminal according to the damage data. According to a specific embodiment, in step S10, pretreatment such as charge amplification, hardware filtering, analog / digital conversion, and software filtering is specifically performed on the acquired damage signals, so that get the treatment result. In step S20, time analysis and frequency analysis are specifically performed on the above-obtained result of processing so as to obtain initial characteristic parameters according to the result of the analysis. In step S30, a self-adaptive approximation is performed on the initial characteristic parameters, according to the established rail damage database, so as to obtain the damage data according to the obtained processing result, according to which a criterion of least squares (LS) is adopted, the information of all the analyzes obtained after the initialization of the algorithm are used, and an inversion operation of a matrix is performed by adopting a recursion method. Thus, the speed of convergence is rapid, it is not sensitive to the degree of dispersion of the characteristic values, and the compromise between the speed of convergence and the computational complexity can be realized. Once the damage data has been obtained in step S30, in step S40, alarm information can be transmitted to the alarm client terminal to trigger alarms according to the damage data. The method of monitoring needle blade damage is not described in detail repeatedly in the present embodiment of the present application.
    [0011] Although the preferred embodiments of the present invention have already been described, as soon as those skilled in the art have become aware of the basic concept of the invention, further modifications may be made to these embodiments. . Thus, the appended claims are intended to be explained including the preferred embodiments, and all of the modifications will be part of the present invention. Of course, one skilled in the art can make various modifications to the present invention without departing from his mind. In the event that said modifications and variations of the present invention form part of the claims of the present invention and the technical equivalents thereof, the present invention will also include said modifications and variations.
    权利要求:
    Claims (10)
    [0001]
    REVENDICATIONS1. Device for monitoring damage to needle blades, which includes sensors, a monitoring extension, a surveillance host and a data center, and characterized in that: the sensors are installed on needle blades and transmit the characteristics acquired at the surveillance extension; the monitoring extension processes the features to obtain processed data, and transmits the processed data to the monitoring host; the monitoring host classifies the processed data and stores the processed data; and the data center manages the entire apparatus, receives and re-processes the data transmitted by the monitoring host, and controls a client terminal according to the re-processed data; wherein the monitoring extension comprises a signal preprocessing module, a local energy data analysis module, and a self-adaptive approximation module.
    [0002]
    A needle blade damage monitoring apparatus according to claim 1, characterized in that the signal pre-processing module receives the characteristics acquired by the sensors and performs charge amplification, hardware filtering, analog / digital conversion. digital and software filtering on the features to obtain a treatment result.
    [0003]
    A needle blade damage monitoring apparatus according to claim 2, characterized in that the features are specifically voltage, temperature and humidity data, or oscillation data.
    [0004]
    A needle blade damage monitoring apparatus as claimed in claim 2, characterized in that the local energy data analysis module performs time analysis and frequency analysis of the processing result of the pretreatment module. signal, and obtains initial characteristic parameters according to the result of the analysis.
    [0005]
    Needle blade damage monitoring apparatus according to claim 4, characterized in that the self-adaptive approximation module comprises a rail damage database, and the self-adaptive approximation module performs a self-adaptive approximation to the initial characteristic parameters according to the rail damage database established in order to obtain damage data.
    [0006]
    6. A needle blade damage monitoring apparatus according to claim 5, characterized in that the monitoring extension further comprises a power supply module and a data sending module; the power module provides power to each module of the monitoring extension; and the sending data module sends the processed damage data to the monitoring host. 20
    [0007]
    Needle blade damage monitoring apparatus according to claim 1, characterized in that the data center manages the maintenance of the needle blade damage monitor and provides data to the client terminal. . 25
    [0008]
    A needle blade damage monitor as claimed in claim 7, characterized in that the data center comprises a host communication module, a data loading module, a data interrogation module and a data module. user communication, the host communication module 30 receiving the classified data from the monitoring host, the data loading module acquiring the damage data, the data interrogation module searching for the type of the damage data and thus, the user communication module being triggered to transmit alarm information to the client terminal. 3025166 17
    [0009]
    Needle blade damage monitoring apparatus according to claim 8, characterized in that the client terminal triggers alarms in response to rail damage events and device component failure events. 5
    [0010]
    A method of monitoring damage to needle blades applied to a damage monitor on needle blades, characterized in that the method comprises the following steps: pretreating the acquired characteristics to obtain a treatment result. ; performing a local energy data analysis according to the result of the processing, and obtaining initial characteristic parameters according to a result of the analysis; performing a self-adaptive approximation on the initial characteristic parameters according to an established damage database, and obtaining damage data according to a result of the processing; and performing a classification and storage of the damage data, and controlling a client terminal according to the damage data.
    类似技术:
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    同族专利:
    公开号 | 公开日
    CN104176092A|2014-12-03|
    CN104176092B|2016-11-23|
    FR3025166B1|2020-02-07|
    引用文献:
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    CN113734231B|2021-11-05|2022-01-07|腾色智能科技有限公司|Data center-based electrical detection device, method, equipment and storage medium|
    法律状态:
    2017-02-13| PLFP| Fee payment|Year of fee payment: 2 |
    2017-09-13| PLFP| Fee payment|Year of fee payment: 3 |
    2018-09-21| PLFP| Fee payment|Year of fee payment: 4 |
    2019-09-19| PLFP| Fee payment|Year of fee payment: 5 |
    2020-09-14| PLFP| Fee payment|Year of fee payment: 6 |
    2021-09-14| PLFP| Fee payment|Year of fee payment: 7 |
    优先权:
    申请号 | 申请日 | 专利标题
    CN201410440339.3A|CN104176092B|2014-09-01|2014-09-01|A kind of railroad turnout steel rail trauma monitoring method and device|
    CN2014104403393|2014-09-01|
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