CN104176092A - Turnout steel rail damage monitoring method and device - Google Patents
Turnout steel rail damage monitoring method and device Download PDFInfo
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- CN104176092A CN104176092A CN201410440339.3A CN201410440339A CN104176092A CN 104176092 A CN104176092 A CN 104176092A CN 201410440339 A CN201410440339 A CN 201410440339A CN 104176092 A CN104176092 A CN 104176092A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 81
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 46
- 239000010959 steel Substances 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 42
- 238000012806 monitoring device Methods 0.000 claims abstract description 22
- 238000001514 detection method Methods 0.000 claims abstract description 19
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- 238000004458 analytical method Methods 0.000 claims description 33
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- 239000011159 matrix material Substances 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 230000003595 spectral effect Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
- B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
- B61L27/53—Trackside diagnosis or maintenance, e.g. software upgrades for trackside elements or systems, e.g. trackside supervision of trackside control system conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L5/00—Local operating mechanisms for points or track-mounted scotch-blocks; Visible or audible signals; Local operating mechanisms for visible or audible signals
- B61L5/10—Locking mechanisms for points; Means for indicating the setting of points
- B61L5/107—Locking mechanisms for points; Means for indicating the setting of points electrical control of points position
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Train Traffic Observation, Control, And Security (AREA)
- Arrangements For Transmission Of Measured Signals (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention relates to the technical field of railway equipment and provides a turnout steel rail damage monitoring method and device. The device comprises a sensor, a monitoring extension, a monitoring host and a data center, wherein the sensor is mounted on a turnout steel rail and transmits collected characteristic data to the monitoring extension; after processing the characteristic data, the monitoring extension transmits processing results to the monitoring host; the monitoring host processes the processing results and performs appointed distribution; the data center manages the entire device and controls a client terminal according to reprocessed data The turnout steel rail damage monitoring device solves the technical problem that, when rail circuits and large rail inspection vehicles are utilized to detecting damage of steel rails, rapidly-developed tiny cracks cannot be identified effectively and accordingly lead to non-detection zones. Further, the turnout steel rail damage monitoring device can effectively identify the tiny cracks on the turnout steel rail to overcome the non-detection zones.
Description
Technical Field
The invention relates to the technical field of railway equipment, and provides a turnout steel rail damage monitoring method and device.
Background
The high-speed turnout steel rail is a key facility which is very important in infrastructure of the high-speed railway and influences the driving safety, and the high-speed turnout steel rail is a weak link in a line all the time and is a key point and a difficult point in line maintenance. Therefore, the high-speed turnout steel rail monitoring system is necessary and is also urgently needed for maintenance and repair of the high-speed railway.
The traditional rail damage detection is mainly completed by a railway track circuit and a rail damage inspection vehicle, wherein the rail damage detection principle of the railway track circuit is that when a rail is damaged, characteristic parameters of the railway track circuit are changed, and rail damage such as breakage and the like which are possibly caused is judged according to the change of the characteristic parameters; the rail flaw inspection vehicle is used for detecting whether the rail has flaws such as cracks, fractures and the like in an ultrasonic guided wave rebounding mode.
However, the prior art has the following technical problems:
because the track circuit and the large-scale track inspection vehicle can not effectively identify the micro cracks of the steel rail which are rapidly developed, the detection of the prior art has a blind area.
Disclosure of Invention
The invention aims to provide a turnout steel rail damage monitoring method and device, and solves the technical problems that when a rail circuit and a large rail inspection vehicle are adopted to detect the damage of a steel rail in the prior art, rapidly developed tiny cracks cannot be effectively identified, and a detection blind area exists.
On one hand, the invention provides a turnout steel rail damage monitoring device which comprises a sensor, a monitoring branch machine, a monitoring host machine and a data center, wherein the monitoring branch machine is connected with the sensor;
the sensor is installed on a turnout steel rail, the sensor transmits collected characteristic data to the monitoring extension, the monitoring extension processes the characteristic data and transmits a processing result to the monitoring host, the monitoring host processes the processing result and then performs appointed distribution, and the data center manages the whole device and controls the client terminal according to the reprocessed data.
Further, the monitoring extension set comprises a signal preprocessing module, a data local energy analysis module and an adaptive approximation module.
Furthermore, the signal preprocessing module is used for receiving the characteristic data acquired by the sensor and obtaining a processing result through charge amplification, hardware filtering, analog-to-digital conversion and software filtering.
Further, the characteristic data is specifically voltage data, or temperature and humidity data, or oscillation data.
Further, the data local energy analysis module is used for performing time domain analysis and frequency domain analysis on the processing result and obtaining initial characteristic parameters according to the analysis result.
And further, the self-adaptive approximation module is used for carrying out self-adaptive approximation processing on the initial characteristic parameters according to the established steel rail damage database and obtaining damage data according to a processing result.
Furthermore, the monitoring host completes the receiving, classification management and storage of the uploaded data of the monitoring extension set.
Furthermore, the data center manages the service processing of the whole turnout steel rail damage monitoring device, and performs data support on the client terminal and each alarm device.
Furthermore, the client terminal gives an alarm to the rail damage event and the device component failure event in time.
On the other hand, the invention also provides a turnout steel rail damage monitoring method, which comprises the following steps:
preprocessing the collected characteristic data to obtain a processing result;
performing data local energy analysis on the processing result, and obtaining an initial characteristic parameter according to the analysis result;
performing self-adaptive approximation processing on the initial characteristic parameters according to the established steel rail damage database, and acquiring damage data according to a processing result;
and classifying, managing and storing the damage data, and controlling the client terminal according to the damage data.
By adopting the technical scheme, the invention has the following beneficial effects:
1. the monitoring device for the rail damage of the turnout consists of a sensor, a monitoring extension, a monitoring host and a data center, wherein the sensor is arranged on the rail of the turnout, the collected characteristic data is transmitted to the monitoring extension, the monitoring extension processes the characteristic data and then transmits the processing result to the monitoring host, the monitoring host processes the processing result and then performs appointed distribution, and the data center manages the whole device and controls a client terminal according to the reprocessed data. The rail flaw detection device solves the technical problem that when the rail flaw detection device in the prior art detects the rail flaw by adopting a rail circuit and a large rail inspection vehicle, the rapidly developed tiny cracks can not be effectively identified, and the detection blind area exists, so that the tiny cracks on the turnout rail can be effectively identified, and the detection blind area is overcome.
2. Because the client terminal connected with the data center is used for the rail damage event, the failure event of the device component can be timely alarmed, the occurrence of unsafe accidents is further avoided, and the safety is guaranteed.
Drawings
FIG. 1 is a schematic structural diagram of a turnout rail damage monitoring device in an embodiment of the invention;
fig. 2 is a schematic block diagram of a monitoring extension in an embodiment of the present invention;
FIG. 3 is a block diagram of a data center according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of modules for interaction between monitoring hosts, monitoring extensions and a data center according to an embodiment of the present invention;
fig. 5 is a flow chart of the detection method for rail break of the turnout.
Detailed Description
The invention provides a turnout steel rail damage monitoring method and device, which solve the technical problems that when a rail circuit and a large rail inspection vehicle are adopted to detect the damage of a steel rail in the prior art, rapidly-developed tiny cracks cannot be effectively identified, and a detection blind area exists, so that the tiny cracks on the turnout steel rail can be effectively identified, and the detection blind area is overcome.
In order to solve the technical problem of the existence of the detection blind area, the general idea is as follows:
this scheme adopts a switch rail damage monitoring devices, including the sensor of connecting in proper order, the monitoring extension, monitoring host computer and data center, wherein, the sensor is installed on the switch rail, the sensor sends the characteristic data of gathering to the monitoring extension, the monitoring extension is handled characteristic data, and convey the processing result to the monitoring host computer, appointed distribution is carried out after the processing result that the monitoring host computer uploaded to the monitoring extension is handled again, data center manages whole device, and according to the data control customer terminal after reprocessing.
In order to better understand the technical scheme, the technical scheme is described in detail below with reference to the figures and the detailed description of the specification.
As shown in fig. 1, the turnout steel rail damage monitoring device provided by the invention specifically comprises a sensor 101, a monitoring extension 102, a monitoring host 103 and a data center 104.
The sensor 101 is installed on a turnout steel rail, the sensor 101 transmits collected characteristic data to the monitoring extension 102, the monitoring extension 102 processes the characteristic data and transmits a processing result to the monitoring extension 102, the monitoring host 103 processes the processing result uploaded by the monitoring extension 102 and then performs appointed distribution, and the data center 104 manages the whole device and controls a client terminal according to the reprocessed data. The whole monitoring device completes the connection among all levels on a data link through a data transmission channel, and realizes the quick, safe and stable uplink and downlink transmission of information by using specific data messages.
In a specific implementation mode, a piezoelectric sensor is adopted to acquire excitation data of a steel rail, and possible steel rail damage data is extracted through a specific algorithm according to steel rail damage characteristic data. Because rail fracture signal is an acoustic emission signal, it is generally less, can discern such tiny sound signal through high frequency, high sensitivity's data acquisition module, and then carry out quantization and analysis to rail fracture's damage data, realize the discernment and the location of crackle, this monitoring technology compares in prior art, this switch rail damage monitoring devices's sensor can cover the monitoring blind area in the rail field of detecting a flaw of traditional technology, the insulating properties that adopts piezoelectric sensor is good, do not influence track circuit.
Specifically, as shown in fig. 2, the monitoring extension 102 specifically includes a signal preprocessing module 201, a data local energy analysis module 202, and an adaptive approximation module 203.
The monitoring extension further comprises a power supply module 204 for supplying power to each module of the whole monitoring extension, and certainly, the monitoring extension further comprises a data forwarding module 205, and the data forwarding module 205 is powered by the power supply module 204, so that after the characteristic data is processed by the monitoring extension, the processed data is forwarded to the monitoring host 103 by the data forwarding module 205.
In a specific embodiment, the signal preprocessing module 201 receives characteristic data acquired by the sensor 101, specifically, the acquired characteristic data may specifically be voltage data, temperature and humidity data, or oscillation data, and then the characteristic data is subjected to preprocessing such as charge amplification, hardware filtering, analog-to-digital conversion, and software filtering, so as to obtain a processing result.
The method is characterized in that a piezoelectric sensor with the frequency range of 10 kHz-300 kHz is selected when the sensor collects signals, signals are captured when the steel rail is damaged, a 4-by-4 channel signal acquisition card is selected due to the relationship between the detection distance of the sensor and a detected area and the positioning of a damaged position, and the parameters of the sensor are configured as follows:
| sampling frequency point | Number of sampling points | Number of channels | Sensor frequency range | Filter range | Sampling accuracy |
| 1MHz | 1024 | 4*4 | 10kHz-300kHz | 20K-300K | 16 bit |
Next, the processing result of the signal after the preprocessing is subjected to data local energy analysis in the data local energy analysis module 202, which includes statistical analysis of data time domain characteristics, statistical analysis of data frequency domain characteristics, extraction of local signal energy characteristics in a frequency domain according to the analysis result, and quantitative output of statistical values to generate initial characteristic parameters of the rail damage data, and the specific method is as follows:
time domain analysis, including maximum value analysis, mean value analysis and mean square value analysis, of the damage signal, specifically calculated as follows:
(2) the frequency domain analysis of the damaged signal comprises power distribution maximum spectral value analysis, power spectral density analysis and the like, and specifically comprises the following steps:
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The method comprises the steps of obtaining power spectral density, comprehensively analyzing time domain characteristics and frequency domain characteristics of the rail damage data, obtaining local energy analysis condition of the damage information in original data, quantitatively outputting statistical values through analysis of the obtained local energy distribution condition, and generating initial characteristic parameters of the damage data.
After the initial characteristic parameters are obtained, in the adaptive approximation module 203, the initial characteristic parameters are processed according to the established steel rail damage database, so that damage data are obtained. The specific method comprises the following steps:
based on the criterion that the sum of squares of the output of each snapshot array is minimum, namely a Least Square (LS) criterion, all array data information obtained after algorithm initialization is utilized, and the matrix inversion operation is completed by a recursion method, so that the convergence speed is high, the characteristic value is insensitive to the divergence degree, and the compromise between the convergence speed and the calculation complexity can be realized.
Let the cost function be:
wherein d (i) and wH(i) x (i) becomes the expected response of the array and the output of the array, respectivelyIn response, gamma is more than or equal to 0 and less than or equal to 1, which is a forgetting factor. ByObtaining R (k) w (k) ═ r (k);
wherein,a weighted autocorrelation matrix representing an array acceptance vector;
(i) x (i) denotes the correlation vector of the accepted vector and the expected output vector of the array.
According to the two formulas, calculating to obtain a recursion estimation formula:
and obtaining the inverse matrix p (k) ═ R-1(k) The recurrence formula of (c):
the following can be obtained:
p(k)x(k)＝g(k)
w(k)＝R-1(k)r(k)＝p(k)r(k)
simple and available
e(k)＝d*(k)-xH(k)w(k-1)
Through the series of calculation, effective damage data are finally obtained, and therefore the recognition accuracy and the recognition calculation rate of damage detection are improved.
After obtaining the damage data, the monitoring extension 102 uploads the damage data to the monitoring host 103, the monitoring host 103 receives, classifies, manages and stores the damage data, and the data center 104 can process the service of the whole railroad switch rail damage monitoring device and support the client and each alarm device with data. The client terminal alerts the damage event, the entire device component failure event, in time based on the damage data obtained by the data center 104.
The monitoring host 103 completes the receiving of the data uploaded by the monitoring extension 102, performs classification management on the received damage data, alarm data, service data and the like, and also processes and forwards the instruction issued by the upper level.
The data center 104 manages the whole turnout steel rail damage monitoring device, processes related data according to service logic, including data classification, deep processing, platform forwarding and database storage, and performs data support and service support on clients at all levels.
As shown in fig. 3, the data center 104 includes a data query module 301, a user communication module 302, a host communication module 303, and a data storage module 304, and when the monitoring host 103 at the station receives the damage data acquired by the host communication module 303 from the data storage module 304, the monitoring host queries the type of the damage data according to the data query module 301, so as to trigger the user communication module 302 to send alarm information to the user terminal, which may be in a form of short message to alarm or in a form of push information through the internet network to notify the terminal to alarm.
As shown in fig. 4, a data interaction module diagram between the monitoring host 103 and the monitoring extension 102 and the data center 104 is shown, where the data center 104 is specifically a railway office platform, and both the monitoring extension 102 and the monitoring host 103 can interact data with the railway office platform. Specifically, a lower end communication module and a data forwarding module are arranged near the monitoring host 103 and the monitoring extension 102, an upper end communication module and a data forwarding module are arranged near the railway bureau platform, data uploaded by the monitoring host 103 and the monitoring extension 102 are transmitted to the railway bureau platform through a protocol stack module between the upper end communication module and the lower end communication module, and instructions issued by the railway bureau platform are transmitted at the same time; the forwarded data is processed between the two data forwarding modules through the state processing module, so that the platform of the railway bureau can obtain recognizable data.
The client terminal connected to the data center 104 by wire or wireless can display the pre-alarm information and the equipment status information generated by the monitoring device for the rail damage of the turnout, and the user can visually see the alarm position of the rail of the turnout through the dynamic sensor deployment diagram.
Based on the same inventive concept, the application also provides a turnout steel rail sensor damage monitoring method, as shown in fig. 5, which comprises the following steps:
s10, preprocessing the collected characteristic data of the turnout steel rail to obtain a processing result;
s20, carrying out data local energy analysis on the processing result, and obtaining an initial characteristic parameter according to the analysis result;
s30, performing self-adaptive approximation processing on the initial characteristic parameters according to the established steel rail damage database, and acquiring damage data according to the processing result;
and S40, managing and storing the damage data in a classified mode, and controlling the client terminal according to the damage data.
In a specific embodiment, the collected damage signal is subjected to preprocessing processes of charge amplification, hardware filtering, analog-to-digital conversion, and software filtering in S10, so as to obtain a processing result.
In S20, time domain analysis and frequency domain analysis are performed on the obtained processing result, so as to obtain an initial characteristic parameter according to the analysis result.
In S30, according to the established rail damage database, the initial characteristic parameters are adaptively approximated, so as to obtain damage data according to the obtained processing result, wherein a Least Square (LS) criterion is specifically adopted, and all array data information obtained after algorithm initialization is utilized, and a recursive method is used to complete the inversion operation of the matrix, so that the convergence speed is high, the dispersion of the characteristic values is insensitive, and the compromise between the convergence speed and the calculation complexity can be realized.
After the damage data obtained at S30, S40 may transmit the damage data to an alarm client terminal for alarming.
The method for monitoring the damage of the turnout steel rail is not described in detail in the embodiment of the application.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. The utility model provides a switch rail damage monitoring devices, includes sensor, monitoring extension, monitoring host computer, data center, its characterized in that:
the sensor is installed on a turnout steel rail, the sensor transmits collected characteristic data to the monitoring extension, the monitoring extension processes the characteristic data and transmits a processing result to the monitoring host, the monitoring host processes the processing result and then performs appointed distribution, and the data center manages the whole device and controls the client terminal according to the reprocessed data.
2. The switch rail flaw detection device according to claim 1, characterized in that: the monitoring extension set comprises a signal preprocessing module, a data local energy analysis module and a self-adaptive approximation module.
3. The switch rail flaw monitoring device according to claim 2, characterized in that: the signal preprocessing module is used for receiving the characteristic data acquired by the sensor and obtaining a processing result through charge amplification, hardware filtering, analog-to-digital conversion and software filtering.
4. The turnout steel rail damage monitoring device according to claim 1 or 3, wherein the characteristic data is voltage data, or temperature and humidity data, or oscillation data.
5. The turnout steel rail flaw monitoring device according to claim 3, wherein the data local energy analysis module is used for carrying out time domain analysis and frequency domain analysis on the processing result, and obtaining initial characteristic parameters according to the analysis result.
6. The turnout steel rail damage monitoring device according to claim 5, wherein the adaptive approximation module is used for performing adaptive approximation processing on the initial characteristic parameters according to an established steel rail damage database, and obtaining damage data according to the processing result.
7. The switch rail crack monitoring device of claim 1, wherein: and the monitoring host completes the receiving, the classified management and the storage of the uploaded data of the monitoring extension.
8. The switch rail flaw monitoring device according to claim 1, characterized in that: the data center manages the service processing of the whole turnout steel rail damage monitoring device and supports data of the client terminal and each alarm device.
9. The switch rail flaw monitoring device according to claim 1, characterized in that: and the client terminal gives an alarm in time for a steel rail damage event and a device component fault event.
10. A turnout steel rail damage detection method is applied to a turnout steel rail damage detection device and is characterized by comprising the following steps:
preprocessing the collected characteristic data to obtain a processing result;
performing data local energy analysis on the processing result, and obtaining an initial characteristic parameter according to the analysis result;
performing self-adaptive approximation processing on the initial characteristic parameters according to the established steel rail damage database, and acquiring damage data according to a processing result;
and classifying, managing and storing the damage data, and controlling the client terminal according to the damage data.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410440339.3A CN104176092B (en) | 2014-09-01 | 2014-09-01 | A kind of railroad turnout steel rail trauma monitoring method and device |
| FR1558089A FR3025166B1 (en) | 2014-09-01 | 2015-09-01 | METHOD AND APPARATUS FOR MONITORING DAMAGE TO NEEDLE BLADES |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410440339.3A CN104176092B (en) | 2014-09-01 | 2014-09-01 | A kind of railroad turnout steel rail trauma monitoring method and device |
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| Publication Number | Publication Date |
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| CN104176092A true CN104176092A (en) | 2014-12-03 |
| CN104176092B CN104176092B (en) | 2016-11-23 |
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| CN201410440339.3A Active CN104176092B (en) | 2014-09-01 | 2014-09-01 | A kind of railroad turnout steel rail trauma monitoring method and device |
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| FR (1) | FR3025166B1 (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106442647A (en) * | 2016-10-27 | 2017-02-22 | 北京国兴力德新材料技术有限公司 | Railway turnout switch rail damage monitoring device and method |
| CN107444431A (en) * | 2017-08-15 | 2017-12-08 | 北京华铁瑞通技术有限公司 | A kind of seamless track steel rail condition monitoring system |
| CN108848571A (en) * | 2018-07-04 | 2018-11-20 | 大连声文科技发展有限公司 | A kind of rail traffic safety monitoring system and monitoring method based on MEMS sensor |
| CN109050574A (en) * | 2018-05-23 | 2018-12-21 | 苏州工业园区职业技术学院 | A kind of rail fault monitoring system |
| CN110645899A (en) * | 2019-09-10 | 2020-01-03 | 中国船舶重工集团公司第七一五研究所 | Distributed optical fiber monitoring method and device for structural crack |
| CN111551642A (en) * | 2020-04-02 | 2020-08-18 | 四川睿铁科技有限责任公司 | Steel rail crack monitoring system |
| CN112215411A (en) * | 2020-09-25 | 2021-01-12 | 杭州羿凡科技有限公司 | Switch operation and maintenance quality prediction analysis system |
| CN112411281A (en) * | 2020-11-20 | 2021-02-26 | 中铁宝桥集团有限公司 | Switch service state intelligent detection system and device |
| CN113734231A (en) * | 2021-11-05 | 2021-12-03 | 腾色智能科技(南京)有限公司 | Data center-based electrical detection device, method, equipment and storage medium |
| CN118323218A (en) * | 2024-06-11 | 2024-07-12 | 四川旷谷信息工程有限公司 | System and method for realizing fault detection operation of multi-service expert joint diagnosis |
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| US6216985B1 (en) * | 1997-08-29 | 2001-04-17 | Robert Douglas Stephens | Railway hazard acoustic sensing, locating, and alarm system |
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| GB2372569A (en) * | 2001-02-26 | 2002-08-28 | Roke Manor Research | Active rail health monitoring system |
| CN101607565A (en) * | 2008-06-16 | 2009-12-23 | 唐德尧 | A kind of rail cracks ground on-Line Monitor Device and ground on-line monitoring method thereof |
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| CN106442647A (en) * | 2016-10-27 | 2017-02-22 | 北京国兴力德新材料技术有限公司 | Railway turnout switch rail damage monitoring device and method |
| CN107444431A (en) * | 2017-08-15 | 2017-12-08 | 北京华铁瑞通技术有限公司 | A kind of seamless track steel rail condition monitoring system |
| CN107444431B (en) * | 2017-08-15 | 2023-05-23 | 南京城铁信息技术有限公司 | Seamless line rail state monitoring system |
| CN109050574A (en) * | 2018-05-23 | 2018-12-21 | 苏州工业园区职业技术学院 | A kind of rail fault monitoring system |
| CN108848571B (en) * | 2018-07-04 | 2021-10-26 | 大连声文科技发展有限公司 | Rail transit safety monitoring system and monitoring method based on MEMS sensor |
| CN108848571A (en) * | 2018-07-04 | 2018-11-20 | 大连声文科技发展有限公司 | A kind of rail traffic safety monitoring system and monitoring method based on MEMS sensor |
| CN110645899B (en) * | 2019-09-10 | 2021-07-27 | 中国船舶重工集团公司第七一五研究所 | Distributed optical fiber monitoring method and device for structural crack |
| CN110645899A (en) * | 2019-09-10 | 2020-01-03 | 中国船舶重工集团公司第七一五研究所 | Distributed optical fiber monitoring method and device for structural crack |
| CN111551642A (en) * | 2020-04-02 | 2020-08-18 | 四川睿铁科技有限责任公司 | Steel rail crack monitoring system |
| CN112215411A (en) * | 2020-09-25 | 2021-01-12 | 杭州羿凡科技有限公司 | Switch operation and maintenance quality prediction analysis system |
| CN112411281A (en) * | 2020-11-20 | 2021-02-26 | 中铁宝桥集团有限公司 | Switch service state intelligent detection system and device |
| CN113734231A (en) * | 2021-11-05 | 2021-12-03 | 腾色智能科技(南京)有限公司 | Data center-based electrical detection device, method, equipment and storage medium |
| CN113734231B (en) * | 2021-11-05 | 2022-01-07 | 腾色智能科技(南京)有限公司 | Data center-based electrical detection device, method, equipment and storage medium |
| CN118323218A (en) * | 2024-06-11 | 2024-07-12 | 四川旷谷信息工程有限公司 | System and method for realizing fault detection operation of multi-service expert joint diagnosis |
Also Published As
| Publication number | Publication date |
|---|---|
| FR3025166B1 (en) | 2020-02-07 |
| CN104176092B (en) | 2016-11-23 |
| FR3025166A1 (en) | 2016-03-04 |
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