CN108645634B - Rail vehicle fault diagnosis device - Google Patents
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Abstract
The invention provides a rail vehicle fault diagnosis device, comprising: the vibration sensor is arranged on the axle box of the railway vehicle and used for acquiring a vibration signal when the vehicle runs; the temperature sensor is arranged on the brake of the railway vehicle and used for acquiring a temperature signal of the brake when the vehicle runs; the vibration signal processing module is wirelessly connected with the vibration sensor and is used for processing the acquired vibration signal, extracting fault characteristic information of the vibration signal and detecting faults; the temperature signal processing module is wirelessly connected with the temperature sensor and is used for processing the acquired temperature signal, predicting the change trend of the temperature signal and carrying out fault detection; and the control device is connected with the vibration signal processing module and the temperature signal processing module and is used for receiving the fault detection results of the vibration signal processing module and the temperature signal processing module and sending out an alarm message when the fault is detected. The invention has strong reliability and high adaptability.
Description
Technical Field
The invention relates to the technical field of intelligent interaction, in particular to a rail vehicle fault diagnosis device.
Background
With the high-speed development of the Chinese social economy, the urbanization process of China is deepened gradually, the total length of urban rail lines is estimated to reach 3500 kilometers in 2016, and more than 3 ten thousand urban rail vehicles with over 60 cities are put into operation in 2020. The rapid development of urban rail transit and the use of a large number of vehicles make the operation safety of vehicles become the focus of attention in recent years.
The urban rail vehicle running system is complex in working environment, various faults are easy to occur when load impact is too large or the urban rail vehicle running system is influenced by factors such as improper installation design and poor lubrication state, the system is one of the most vulnerable systems in the urban rail vehicle, real-time monitoring of the system is an important means for guaranteeing the driving safety of the urban rail vehicle, and meanwhile the system is also a key for providing higher passenger service quality, so that the system has great practical significance in real-time monitoring, fault early warning and diagnosis of the state of the urban rail vehicle running system.
At present, fault diagnosis of a train running system in China still mainly adopts a manual measurement mode, the manual measurement is high in labor intensity and complicated in process, and due to the fact that a measurement tool falls behind, manual measurement errors are difficult to avoid. If an on-line monitoring system is applied in the maintenance link, an automatic diagnosis device is formed, so that not only can detection personnel be relieved from high-intensity labor and complex data analysis, but also reliable technical guarantee can be provided for maintenance. The research on the automatic diagnosis device of the urban rail vehicle running system mainly focuses on time-frequency analysis of vibration signals, such as wavelet analysis, short-time Fourier transform, empirical mode decomposition, amplitude spectrum characteristics and the like, the methods are used for analyzing a single sensor and a single fault mode, the urban rail vehicle running system faults have a plurality of influence factors and are related to each other, and the diversity and uncertainty of the faults and the complexity among various faults form the difficulty of fault diagnosis, so that the accurate diagnosis of the urban rail vehicle running system faults is difficult to complete by only using the single sensor and the method of single fault characteristic quantity.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a rail vehicle fault diagnosis device.
The purpose of the invention is realized by adopting the following technical scheme:
a rail vehicle fault diagnosis device comprising:
the vibration sensor is arranged on the axle box of the railway vehicle and used for acquiring a vibration signal when the vehicle runs;
the temperature sensor is arranged on the brake of the railway vehicle and used for acquiring a temperature signal of the brake when the vehicle runs;
the vibration signal processing module is wirelessly connected with the vibration sensor and is used for processing the acquired vibration signal, extracting fault characteristic information of the vibration signal and detecting faults;
the temperature signal processing module is wirelessly connected with the temperature sensor and is used for processing the acquired temperature signal, predicting the change trend of the temperature signal and carrying out fault detection;
and the control device is connected with the vibration signal processing module and the temperature signal processing module and is used for receiving the fault detection results of the vibration signal processing module and the temperature signal processing module and sending out an alarm message when the fault is detected.
Further, the temperature signal processing module processes the acquired temperature signal to predict a change trend of the temperature signal, and further includes:
receiving a temperature signal collected by a temperature sensor; processing the acquired temperature signals by adding a data window, and forming temperature data in the data window into a temperature sequence [ W ]M-CL+1,…,WM-1,WM]In which C isL denotes the width of the data window used, i.e. the number of temperature data contained in the data window, WMTemperature data representing a current time;
acquiring the variation trend of the temperature sequence, wherein the adopted temperature variation trend function is as follows:
in the formula, QS represents a temperature change tendency;
predicting the temperature data of the next moment according to the temperature sequence, wherein the adopted temperature prediction function is as follows:
wherein W' represents the predicted temperature at the next time; QS represents the variation trend of temperature sequence in the data window, WMTemperature data representing a current time;
when the temperature sequence [ W ]M-CL+1,…,WM-1,WM]Or the predicted temperature W' at the next moment is greater than a set threshold
Or the variation trend QS of the temperature sequence is larger than a set threshold
And when the brake is in failure, marking the brake to be in failure.
Further, the vibration signal processing module processes the acquired vibration signal, extracts the fault feature information of the vibration signal, and further includes:
receiving a vibration signal collected by a vibration sensor; processing the acquired vibration signals by adding a time window, and performing Fast Fourier Transform (FFT) on the vibration signals in the time window to acquire vibration signal representation on a frequency domain; performing wavelet decomposition according to the vibration signal representation on the frequency domain to obtain a wavelet packet decomposition tree of the vibration signal, wherein the adopted wavelet packet decomposition tree obtaining function is as follows:
wherein BS (p, q) represents the q-th sub-band energy in the p-th layer of the wavelet packet decomposition tree, SL represents the coefficient sequence length of the original vibration signal in the time window, XSn(p, q) represents the nth coefficient in the coefficient sequence corresponding to the qth sub-band in the p-th layer of the wavelet packet decomposition tree;
2 in p-th layer in combined wavelet packet decomposition treepThe energy characteristics of the sub-bands are used as an energy characteristic vector TX of the vibration signal in the time window, namely TX ═ { BS (p, q) };
matching an energy characteristic vector TX of a vibration signal in a time window with energy characteristic vectors of pre-stored sample vibration signals of different state types, and identifying the state type of the vibration signal in the time window, wherein the state type comprises a normal state or different types of fault states, and an adopted state identification function is as follows:
wherein ZS represents a state judgment factor, XZnRepresenting the nth subband energy signature in the signature vector TX, ZN (i) table
Displaying the nth sub-band energy characteristic in the energy characteristic vector of the preset ith sample vibration signal;
and
a mathematical expectation representing an energy signature; i represents the total number of different preset types of sample vibration signals, PS represents the total number of subband energy features in the feature vector, omega1And ω2Representing a weight factor;
when the state judgment factor ZS is smaller than the set judgment threshold value ZSωAnd marking the state type of the acquired vibration signal as a normal state or a fault state to which the ith sample vibration signal corresponding to the state judgment factor ZS belongs.
Further, in the vibration signal processing module, 2 in the p-th layer of the combined wavelet packet decomposition treepThe energy characteristics of the sub-frequency bands are used as energy characteristic vectors TX of the vibration signals in the time window, wherein the specifically selected layer number p is obtained by the following functions:
where p denotes the number of layers in the wavelet packet decomposition tree selected when the energy eigenvector TX is obtained, faRepresenting the frequency of sampling of the vibration signal, fbIndicating the characteristic vibration frequency of the fault, fcRepresenting the vibration frequency of the normal work of the transformer body;
indicating a rounded-down symbol.
The invention has the beneficial effects that: the fault diagnosis device collects vibration signals and temperature signals of a rail vehicle during running through the vibration sensor arranged on the axle box of the rail vehicle and the temperature sensor arranged on the brake, carries out analysis processing and fault detection on the collected vibration signals and temperature signals through the processing module, sends alarm information to the console in time when a fault is detected, detects the running state of the rail vehicle from multiple dimensions, and improves the reliability and the adaptability of fault detection; meanwhile, the automatic fault diagnosis device can effectively avoid high cost caused by manual monitoring; meanwhile, the fault diagnosis device is convenient to install and implement.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a frame structure diagram of the present invention.
Reference numerals:
vibration sensor 1, temperature sensor 2, vibration signal processing module 3, temperature signal processing module 4, and control device 5
Detailed Description
The invention is further described in connection with the following application scenarios.
Referring to fig. 1, there is shown a rail vehicle fault diagnosis apparatus including:
the vibration sensor 1 is arranged on an axle box of the railway vehicle and is used for acquiring a vibration signal when the vehicle runs;
the temperature sensor 2 is arranged on the brake of the railway vehicle and is used for acquiring a temperature signal of the brake when the vehicle runs;
the vibration signal processing module 3 is wirelessly connected with the vibration sensor and is used for processing the acquired vibration signal, extracting fault characteristic information of the vibration signal and detecting faults;
the temperature signal processing module 4 is wirelessly connected with the temperature sensor 2 and is used for processing the acquired temperature signal, predicting the change trend of the temperature signal and carrying out fault detection;
and the control device 5 is connected with the vibration signal processing module 3 and the temperature signal processing module 4 and is used for receiving fault detection results of the vibration signal processing module 3 and the temperature signal processing module 4 and sending out an alarm message when detecting that a fault occurs.
According to the embodiment of the invention, the fault diagnosis device collects the vibration signal and the temperature signal of the vehicle during operation through the vibration sensor 1 arranged on the axle box of the rail vehicle and the temperature sensor 2 arranged on the brake, carries out analysis processing and fault detection on the collected vibration signal and temperature signal through the processing module, sends the alarm message to the console in time when the fault is detected, detects the operation state of the rail vehicle from multiple dimensions, and improves the reliability and the adaptability of the fault detection; meanwhile, the automatic fault diagnosis device can effectively avoid high cost caused by manual monitoring; meanwhile, the fault diagnosis device is convenient to install and implement.
Further, the temperature signal processing module 4 processes the acquired temperature signal to predict a variation trend of the temperature signal, and further includes:
receiving a temperature signal collected by the temperature sensor 2; processing the acquired temperature signals by adding a data window, and forming temperature data in the data window into a temperature sequence [ W ]M-CL+1,…,WM-1,WM]Where CL denotes the width of the data window used, i.e. the number of temperature data contained in the data window, WMTemperature data representing a current time;
acquiring the variation trend of the temperature sequence, wherein the adopted temperature variation trend function is as follows:
in the formula, QS represents a temperature change tendency;
predicting the temperature data of the next moment according to the temperature sequence, wherein the adopted temperature prediction function is as follows:
wherein W' represents the predicted temperature at the next time; QS represents the variation trend of temperature sequence in the data window, WMTemperature data representing a current time;
when the temperature sequence [ W ]M-CL+1,…,WM-1,WM]Or the predicted temperature W' at the next moment is greater than a set threshold
Or the variation trend QS of the temperature sequence is larger than a set threshold
Time, markAnd recording the fault of the brake.
According to the embodiment of the invention, the temperature signal acquired by the temperature sensor 2 is analyzed in the manner so as to judge whether the brake of the rail vehicle has a fault, and when the temperature of the brake rises sharply or is overhigh, the brake is indicated to have a fault, so that the temperature signal processing module 4 in the device accurately judges the change trend of the temperature at the current moment and accurately predicts the temperature at the next moment, can predict the temperature of the brake in advance, timely judges the running condition of the brake of the rail vehicle and feeds the running condition back to the vehicle control center, and the reliability and the real-time performance of the fault diagnosis of the rail vehicle are effectively improved.
Further, the vibration signal processing module processes the acquired vibration signal, extracts the fault feature information of the vibration signal, and further includes:
receiving a vibration signal collected by the vibration sensor 1; processing the acquired vibration signals by adding a time window, and performing Fast Fourier Transform (FFT) on the vibration signals in the time window to acquire vibration signal representation on a frequency domain; performing wavelet decomposition according to the vibration signal representation on the frequency domain to obtain a wavelet packet decomposition tree of the vibration signal, wherein the adopted wavelet packet decomposition tree obtaining function is as follows:
wherein BS (p, q) represents the q-th sub-band energy in the p-th layer of the wavelet packet decomposition tree, SL represents the coefficient sequence length of the original vibration signal in the time window, XSn(p, q) represents the nth coefficient in the coefficient sequence corresponding to the qth sub-band in the p-th layer of the wavelet packet decomposition tree;
2 in p-th layer in combined wavelet packet decomposition treepThe energy characteristics of the sub-bands are used as an energy characteristic vector TX of the vibration signal in the time window, namely TX ═ { BS (p, q) };
matching an energy characteristic vector TX of a vibration signal in a time window with energy characteristic vectors of pre-stored sample vibration signals of different state types, and identifying the state type of the vibration signal in the time window, wherein the state type comprises a normal state or different types of fault states, and an adopted state identification function is as follows:
wherein ZS represents a state judgment factor, XZnRepresenting the nth subband energy signature in the signature vector TX, ZN (i) table
Displaying the nth sub-band energy characteristic in the energy characteristic vector of the preset ith sample vibration signal;
and
a mathematical expectation representing an energy signature; i represents the total number of different preset types of sample vibration signals, PS represents the total number of subband energy features in the feature vector, omega1And ω2Representing a weight factor;
when the state judgment factor ZS is smaller than the set judgment threshold value ZSωAnd marking the state type of the acquired vibration signal as a normal state or a fault state to which the ith sample vibration signal corresponding to the state judgment factor ZS belongs.
In the above embodiment of the present invention, the vibration signal obtained by the vibration sensor 1 is processed by the above method, the wavelet packet decomposition tree of the vibration signal is first obtained, the energy eigenvector of the vibration signal is obtained from the wavelet packet decomposition tree, the obtained energy eigenvector of the vibration signal is then matched with the energy eigenvector of the vibration signal of different samples, the specific state type of the vibration signal is obtained, so as to determine the operation state of the axle box, the vibration signal sent by the axle box is collected, the energy eigenvector in the vibration signal is extracted as a basis, and the state type to which the eigenvector belongs is determined, so as to determine the working state of the axle box, so that the accuracy is high, and the performance of the fault diagnosis device is further improved.
Further, in the vibration signal processing module 3, the p-th layer 2 in the combined wavelet packet decomposition treepThe energy characteristics of the sub-frequency bands are used as energy characteristic vectors TX of the vibration signals in the time window, wherein the specifically selected layer number p is obtained by the following functions:
where p denotes the number of layers in the wavelet packet decomposition tree selected when the energy eigenvector TX is obtained, faRepresenting the frequency of sampling of the vibration signal, fbIndicating the characteristic vibration frequency of the fault, fcRepresenting the vibration frequency of the normal work of the transformer body;
indicating a rounded-down symbol.
In the above embodiment of the present invention, in the vibration signal processing module 3, when the energy feature vector of the vibration signal is obtained, 2 of the p-th layer of the wavelet packet decomposition tree is determined and selected in the above mannerpThe energy characteristics of the sub-frequency bands are combined into the energy characteristic vector of the vibration signal, the energy characteristic vector thinning degree which is most suitable for distinguishing the fault state and the normal state can be determined according to the vibration frequency of the fault state sample vibration signal and the normal state sample signal and the sampling frequency of the vibration sensor 1, the complexity of operation is optimized, the identification accuracy of the vibration signal is improved, the complexity of operation is reduced, and the performance of the fault diagnosis device is further improved.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be analyzed by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (1)
1. A rail vehicle fault diagnosis device characterized by comprising:
the vibration sensor is arranged on the axle box of the railway vehicle and used for acquiring a vibration signal when the vehicle runs;
the temperature sensor is arranged on the brake of the railway vehicle and used for acquiring a temperature signal of the brake when the vehicle runs;
the vibration signal processing module is wirelessly connected with the vibration sensor and is used for processing the acquired vibration signal, extracting fault characteristic information of the vibration signal and detecting faults;
the temperature signal processing module is wirelessly connected with the temperature sensor and is used for processing the acquired temperature signal, predicting the change trend of the temperature signal and carrying out fault detection;
the control device is connected with the vibration signal processing module and the temperature signal processing module and is used for receiving fault detection results of the vibration signal processing module and the temperature signal processing module and sending out an alarm message when a fault is detected;
the temperature signal processing module processes the acquired temperature signal, predicts a change trend of the temperature signal, and further includes:
receiving a temperature signal collected by a temperature sensor; processing the acquired temperature signals by adding a data window, and forming temperature data in the data window into a temperature sequence [ W ]M-CL+1,…,WM-1,WM]Where CL denotes the width of the data window used, i.e. the number of temperature data contained in the data window, WMTemperature data representing a current time;
acquiring the variation trend of the temperature sequence, wherein the adopted temperature variation trend function is as follows:
in the formula, QS represents a temperature change tendency;
predicting the temperature data of the next moment according to the temperature sequence, wherein the adopted temperature prediction function is as follows:
wherein W' represents the predicted temperature at the next time; QS represents the variation trend of temperature sequence in the data window, WMTemperature data representing a current time;
when the temperature sequence [ W ]M-CL+1,…,WM-1,WM]Or the predicted temperature W' at the next moment is greater than a set threshold
Or the variation trend QS of the temperature sequence is larger than a set threshold
When the brake is in failure, marking the brake to be in failure;
the vibration signal processing module processes the acquired vibration signal, extracts fault feature information of the vibration signal, and further includes:
receiving a vibration signal collected by a vibration sensor; processing the acquired vibration signals by adding a time window, and performing Fast Fourier Transform (FFT) on the vibration signals in the time window to acquire vibration signal representation on a frequency domain; performing wavelet decomposition according to the vibration signal representation on the frequency domain to obtain a wavelet packet decomposition tree of the vibration signal, wherein the adopted wavelet packet decomposition tree obtaining function is as follows:
wherein BS (p, q) represents the q-th sub-band energy in the p-th layer of the wavelet packet decomposition tree, SL represents the coefficient sequence length of the original vibration signal in the time window, XSn(p, q) represents the nth coefficient in the coefficient sequence corresponding to the qth sub-band in the p-th layer of the wavelet packet decomposition tree;
2 in p-th layer in combined wavelet packet decomposition treepEnergy characteristics of sub-bandsCharacterizing an energy eigenvector TX as a vibration signal within the time window, i.e., TX ═ BS (p, q) };
matching an energy characteristic vector TX of a vibration signal in a time window with energy characteristic vectors of pre-stored sample vibration signals of different state types, and identifying the state type of the vibration signal in the time window, wherein the state type comprises a normal state or different types of fault states, and an adopted state identification function is as follows:
wherein ZS represents a state judgment factor, XZnRepresenting the nth sub-band energy characteristic in the characteristic vector TX, and ZN (i) representing the nth sub-band energy characteristic in the energy characteristic vector of the preset ith sample vibration signal;
and
a mathematical expectation representing an energy signature; i represents the total number of different preset types of sample vibration signals, PS represents the total number of subband energy features in the feature vector, omega1And ω2Representing a weight factor;
when the state judgment factor ZS is smaller than the set judgment threshold value ZSωIf so, marking the state type of the acquired vibration signal as a normal state or a fault state to which the ith sample vibration signal corresponding to the state judgment factor ZS belongs;
wherein, in the vibration signal processing module (3), the combined wavelet packet decomposition tree is 2 in the p layerpThe energy characteristics of the sub-frequency bands are used as energy characteristic vectors TX of the vibration signals in the time window, wherein the specifically selected layer number p is obtained by the following functions:
where p denotes the number of layers in the wavelet packet decomposition tree selected when the energy eigenvector TX is obtained, faRepresenting the frequency of sampling of the vibration signal, fbIndicating the characteristic vibration frequency of the fault, fcRepresenting the vibration frequency of the normal work of the transformer body;
indicating a rounded-down symbol.
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| CN109353376A (en) * | 2018-10-24 | 2019-02-19 | 西安英特迈思信息科技有限公司 | Rail vehicle monitors system and its monitoring method |
| CN110411766A (en) * | 2019-07-30 | 2019-11-05 | 中国神华能源股份有限公司神朔铁路分公司 | The snakelike unstability detection method of train bogie, device, system and storage medium |
| CN110901624B (en) * | 2019-12-06 | 2021-03-26 | 中车株洲电力机车有限公司 | Friction braking fault detection method and device, readable storage medium and controller |
| CN111879776A (en) * | 2020-06-19 | 2020-11-03 | 山东师范大学 | Method and system for fault detection of rail side lubricating device |
| CN111780809B (en) * | 2020-07-21 | 2023-09-08 | 重庆大学 | Rail vehicle part temperature and vibration monitoring and early warning method and system thereof |
| CN114537466B (en) * | 2022-02-25 | 2023-06-16 | 京沪高速铁路股份有限公司 | High-speed railway track structure deformation damage monitoring system and method |
| CN114238038B (en) * | 2022-03-01 | 2022-05-06 | 湖南云箭智能科技有限公司 | Board card temperature monitoring method, device, equipment and readable storage medium |
| CN114777842A (en) * | 2022-04-21 | 2022-07-22 | 深圳技术大学 | Device and vehicle |
| CN115031828B (en) * | 2022-05-30 | 2025-05-06 | 广西电网有限责任公司电力科学研究院 | A method for processing transient acoustic vibration signals of transformer |
Family Cites Families (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7860663B2 (en) * | 2004-09-13 | 2010-12-28 | Nsk Ltd. | Abnormality diagnosing apparatus and abnormality diagnosing method |
| JP2006341659A (en) * | 2005-06-07 | 2006-12-21 | Sumitomo Metal Ind Ltd | Rail vehicle abnormality detection method |
| CN100546144C (en) * | 2007-11-29 | 2009-09-30 | 昆明理工大学 | A Fault Line Selection Method for Small Current Grounding System Using Wavelet Packet Decomposition and Correlation Analysis |
| DE102013201494A1 (en) * | 2012-09-18 | 2014-03-20 | Siemens Aktiengesellschaft | Diagnostic procedure for rail vehicles |
| CN103852271B (en) * | 2012-12-01 | 2017-02-08 | 中车青岛四方机车车辆股份有限公司 | High-speed train running gear fault diagnosis and remote monitoring system based on Internet of Things |
| CN104181460B (en) * | 2014-07-07 | 2017-06-06 | 沈阳工业大学 | A kind of permanent magnetic actuator vacuum circuit breaker method for diagnosing faults based on Multi-information acquisition |
| CN104833534A (en) * | 2015-04-21 | 2015-08-12 | 广州市地下铁道总公司 | Train running fault diagnosis device based on multi-source information fusion, and method |
| CN104990709B (en) * | 2015-08-07 | 2017-10-13 | 杨玉娇 | Method for detecting Locomotive Bearing failure |
| CN105424364A (en) * | 2015-11-09 | 2016-03-23 | 北京交通大学 | Diagnostic method and device of train bearing failure |
| CN105806604B (en) * | 2016-03-18 | 2018-10-19 | 北京唐智科技发展有限公司 | A kind of rolling stock EEF bogie bearing retainer failure prediction alarm method |
| CN206132406U (en) * | 2016-08-31 | 2017-04-26 | 上海王智测控技术有限公司 | High -speed rail train health condition monitoring devices |
| CN107884214B (en) * | 2017-11-08 | 2019-12-03 | 北京唐智科技发展有限公司 | A kind of train EEF bogie unit failure multi-parameter comprehensive decision-making technique and device |
| CN107966308A (en) * | 2018-01-23 | 2018-04-27 | 唐智科技湖南发展有限公司 | A kind of method and device of rail traffic vehicles Collection transmission |
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