CN110631832A - Subway vehicle bearing fault on-line detection method - Google Patents
Subway vehicle bearing fault on-line detection method Download PDFInfo
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Abstract
The invention discloses an online detection method for bearing faults of a subway vehicle, belonging to the field of subway safety monitoring; the technical scheme is characterized by comprising the following steps: establishing a model: establishing a bearing fault model, which is the basis of fault judgment; fault diagnosis: collecting and diagnosing bearing information of a vehicle; and forming a report and uploading: and forming a report according to the comparison and judgment result and uploading the report. The invention solves the problem that the bearing fault can not be found in time, and realizes the effects of finding the fault bearing in advance and replacing or maintaining the fault bearing in time.
Description
Technical Field
The invention relates to the field of subway safety monitoring, in particular to an online detection method for bearing faults of a subway vehicle.
Background
The subway is taken as a modern vehicle, the running safety is very important, along with the improvement of the speed and the encryption of the train, the pressure is added to the safe running of the subway, and the bearing fault is one of main fault sources in the running of the train and is also the largest factor influencing the safety.
The conventional method is to overhaul the bearing at regular time, but the method can not find the fault of the bearing in time, so that the fault bearing can be used continuously, potential safety hazards exist, excessive overhaul is easy to cause excessive maintenance, and the bearing fault unloading rate and the overhaul cost are reduced.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an online detection method for bearing faults of a metro vehicle, which comprises the following steps of establishing a model through (i): the establishment of a bearing fault model is the basis of fault judgment; fault diagnosis: collecting and diagnosing bearing information of a vehicle; and forming a report and uploading: forming a report according to the comparison and judgment result and uploading the report; the mutual cooperation of three steps has realized can carrying out real-time supervision to the trouble of bearing, to finding trouble bearing in advance, is convenient for in time change trouble bearing, reduces the potential safety hazard that exists, and can avoid transition maintenance, reduces bearing trouble and moves back the purpose of unloading rate and maintenance cost.
In order to achieve the purpose, the invention provides the following technical scheme: an online detection method for bearing faults of a metro vehicle comprises the following steps:
establishing a model: establishing a bearing fault model, which is the basis of fault judgment;
fault diagnosis: collecting and diagnosing bearing information of a vehicle;
and forming a report and uploading: and forming a report according to the comparison and judgment result and uploading the report.
By adopting the technical scheme, the basis that the bearing fault model is used for judging the fault of the subway bearing is established, the basis is improved for the diagnosis of the fault, the fault diagnosis is to collect the information of the passing subway bearing, the collected information is compared and judged with the fault model, so that whether the bearing has problems can be judged, and because a plurality of information of the fault of the bearing exists in the fault model, the type of the fault can be judged simultaneously, the steps of forming the report and uploading are to form the report of the compared result and upload the report, so that maintenance personnel can replace the fault bearing conveniently.
The invention is further configured to: in the first step, through a mathematical spectrum analysis technology and a computer technology, the basic characteristics of the sound signal caused by the vehicle fault bearing can be extracted, analyzed and processed, and the fault characteristics of the bearing roller, the outer ring and the inner ring are respectively modeled.
By adopting the technical scheme, the information contained in the fault model can be improved, so that the diagnosis system can accurately and efficiently diagnose and judge the collected information and feed back the information of the fault bearing in time.
The invention is further configured to: in the process of extracting, analyzing and processing the basic characteristics of the sound signals, Morlet wavelet transform is selected to preprocess the signals, and Hilbert demodulation technology is selected to analyze in frequency domain analysis.
By adopting the technical scheme, the aims of suppressing and preventing the interference of useless signals and improving the signal-to-noise ratio of the signals are fulfilled. Many non-stationary signals exist in the diagnosis and analysis of abnormal sounds, and particularly when mechanical equipment has faults such as friction, looseness, peeling and impact, a large number of non-stationary components are contained in the signals. The project adopts wavelet transformation to preprocess signals and extracts the fault characteristic frequency band of a fault part. The wavelet transform has a band-pass filtering characteristic, Morlet wavelets are selected to decompose signals, so that the change rule of the signals in each frequency band is obtained, and finally, characteristic frequency band signals capable of truly reflecting the abnormal sound impact phenomenon are extracted and serve as the basis for detecting the running states of the rolling bearing and the running parts nearby the rolling bearing.
The invention is further configured to: and secondly, establishing an intelligent diagnosis system for the faults of the subway bearings, wherein the intelligent diagnosis system comprises three parts, namely a monitoring means and an analysis principle of the bearings and determination of defective parts of the bearings. .
By adopting the technical scheme, the fault type and the position of the bearing can be determined by accurately telling the part of the fault bearing through the cooperation of the three parts.
The invention is further configured to: the intelligent subway bearing fault diagnosis system consists of two parts, namely software and hardware;
the hardware part comprises a wheel sensor, an acoustic sensor array box, an MBD acquisition control box, a car number image recognition device, a wireless data transmission device and a subway vehicle bearing fault online detection and prediction platform device;
the software part comprises data acquisition and processing software, car number identification software and online detection and forecast platform software.
By adopting the technical scheme, the hardware can collect information, and the software analyzes the collected information, so that a finished flow can be formed to diagnose the fault of the vehicle bearing; the acoustic sensor array box is controlled by the wheel sensor to start to collect information of passing vehicle bearings, the MBD collection control box amplifies signals, and the vehicle number image recognition device can recognize and store vehicle numbers of passing vehicles, so that fault bearings can be conveniently located.
The invention is further configured to: the car number image recognition device comprises a car passing signal receiving device, a camera package, an LED auxiliary compensation light source, a main case and the like.
By adopting the technical scheme, the vehicle-passing signal receiving device can receive the vehicle-passing signal, the camera package receives the signal to photograph the vehicle number part on the subway, the photo is uploaded to the case, then the case forms a report and uploads the report, the intelligent diagnosis system for the subway bearing fault can track and locate the bearing fault part of the subway, and maintenance personnel can accurately and quickly replace the fault bearing.
The invention is further configured to: and the stroboscopic lamp of the LED auxiliary compensation light source inclines towards the direction of the ground.
Through adopting above-mentioned technical scheme, because subway train's car body side off-plate forms radian curved surface from top to bottom, lead to the stroboscopic lamp during operation at night, a very high vertical retort type bright band of intensity appears in the middle part of the photo of shooing, lead to appearing being corroded away at light band position car number, and simultaneously, because the automobile body is smooth surface's stainless steel, reflection of light effect appears when leading to the stroboscopic lamp to shine the car body surface, make the camera shoot under the adverse light state, the photo both sides grey value is very big, the later stage image processing effect has been influenced. Aiming at the above situation, the angle between the camera and the stroboscopic lamp is adjusted, so that the stroboscopic lamp is in a horizontal slightly downward angle, the backlight shooting effect is avoided, and the area of a light band is reduced.
The invention is further configured to: and in the third step, the collected information and the judgment result form a report and are uploaded to a networked database.
By adopting the technical scheme, the information is reported and uploaded, the information can be stored to form a record, and the maintenance record can be conveniently searched in the subsequent process.
The invention is further configured to: in the third step, a central database can be established by comparing the result with the acquired information.
By adopting the technical scheme, a central database is established, a large amount of bearing fault data are collected to form a rolling bearing fault diagnosis expert system, a system discrimination model is dynamically adjusted, and the purpose of improving the self-adaptive capacity of the system is achieved
In summary, compared with the prior art, the invention has the following beneficial effects:
1. establishing a model by the following steps: the establishment of a bearing fault model is the basis of fault judgment; fault diagnosis: collecting and diagnosing bearing information of a vehicle; and forming a report and uploading: forming a report according to the comparison and judgment result and uploading the report; the three parts are established, so that the bearing can be detected in the running process of the subway vehicle, the fault of the bearing can be found in time, the fault bearing can be replaced in time, and the existence of potential safety hazards is reduced;
2. including car number recognition device in the subway bearing fault intelligent diagnosis system, through the cooperation of hardware and software, can discern the formation report and upload past car number, and then can carry out accurate location to the trouble bearing, need not detect intact bearing, reduced the cost of overhauing, also improved the speed of work.
Drawings
Fig. 1 is a flow chart of signal processing.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings. In which like parts are designated by like reference numerals. It should be noted that the terms "front," "back," "left," "right," "upper" and "lower" used in the following description refer to directions in the drawings, and the terms "bottom" and "top," "inner" and "outer" refer to directions toward and away from, respectively, the geometric center of a particular component.
Example (b): an online detection method for bearing faults of a metro vehicle comprises the following steps:
establishing a model: establishing a fault model, which is the basis of fault judgment;
fault diagnosis: collecting and diagnosing bearing information of a vehicle;
and forming a report and uploading: forming a report according to the comparison and judgment result and uploading the report;
the vehicle bearing passing through the three steps can be detected on line, fault diagnosis and detection of the subway bearing are achieved, the fault bearing can be found in time, and the fault bearing is replaced.
Firstly, establishing a model: establishing a fault model, which is the basis of fault judgment; through a mathematical spectrum analysis technology and a computer technology, the basic characteristics of sound signals caused by a vehicle fault bearing can be extracted, analyzed and processed, and the fault characteristics of a bearing roller, an outer ring and an inner ring are respectively modeled; a large number of fault bearing sound files are used as a basis, project groups process and analyze bearing sounds collected by a detection station, suspected fault bearings with good fault feature performance continuity are found, the home station section of the vehicle is timely informed to overhaul and coordinate unloading of the bearings, and a fault discrimination model is perfected according to unloading decomposition results.
And secondly, in fault diagnosis, an intelligent diagnosis system for subway bearing faults needs to be established, and the faults inside the bearings are accurately judged mainly through an intelligent diagnosis technology, so that excessive maintenance is avoided, the withdrawal rate of the bearing faults is reduced, and the maintenance cost is reduced. Meanwhile, the method has the advantages that key parts such as the bearing outer ring, the bearing inner ring and the roller are detected and diagnosed through an acoustic technology, and automatic prediction is carried out.
Firstly, the fault of a rolling bearing of a vehicle is automatically judged, and the intelligent subway bearing fault diagnosis system is established to accurately judge the internal fault of the bearing, avoid excessive maintenance, reduce the withdrawal rate of the fault of the bearing and reduce the maintenance cost; the vehicle number map selection and identification device can more quickly identify, analyze and store the vehicle number, and the signal processing method can extract a characteristic frequency band signal which truly reflects the abnormal sound impact phenomenon and is used as a basis for detecting the running state of the rolling bearing and the running parts nearby the rolling bearing; the intelligent subway bearing fault diagnosis system is established by detecting bearing faults through vibration and acoustic emission signals and integrating multiple technologies to realize a comprehensive detection technology meeting the actual field application requirements.
The data analysis and discrimination model can extract, analyze and process the basic characteristics of the sound signals caused by the vehicle fault bearing through a mathematical spectrum analysis technology and a computer technology, respectively model the fault characteristics of the bearing roller, the outer ring and the inner ring, form a fault discrimination algorithm and realize the automatic discrimination of the vehicle rolling bearing fault.
Firstly, a sound data preprocessing method is used for achieving the purposes of suppressing and preventing the interference of useless signals and improving the signal-to-noise ratio of the signals; a plurality of non-stationary signals exist in the diagnosis and analysis of bearing faults, and particularly when mechanical equipment has faults such as friction, looseness, peeling, impact and the like, a large number of non-stationary components are contained in the bearing signals; preprocessing signals by adopting wavelet transformation, and extracting a bearing fault characteristic frequency band; the wavelet transform has a band-pass filtering characteristic, Morlet wavelets are selected to decompose signals, so that the change rule of bearing signals in each frequency band is obtained, and finally, characteristic frequency band signals capable of truly reflecting the bearing impact phenomenon are extracted and serve as the basis for detecting the running state of the rolling bearing carrier.
Secondly, extracting fault characteristics in the sound signals, and separating the defect information of the bearing from the complex amplitude modulation signals by applying a Hilbert demodulation technology on frequency domain analysis; in time domain analysis, corresponding parameters and threshold values are selected through fusion of time domain index analysis, cluster analysis and K-nearest neighbor analysis, and fault judgment is carried out on the bearing.
The subway bearing fault intelligent diagnosis system comprises a bearing detection means, an analysis principle and determination of bearing defect positions; the method is mainly realized by the following steps:
(1) the study of vibration and acoustic emission composite diagnosis technology and signal processing technology: advanced diagnosis technologies such as vibration and acoustic emission and signal processing methods are comprehensively used for monitoring the actual state of the bearing with different running mileage or running time. Ensuring that normal bearing failure can be found, while early minor failure and poor lubrication can also be found.
(2) Analysis and research of fault vibration signals: in the signal analysis process, a sensor is used for collecting an original signal, band-pass filtering and rectification processing are carried out, and according to a frequency spectrum obtained by a rectification signal, the frequency spectrum is compared with a frequency spectrum of a fault bearing in a database to check and judge the state of the bearing.
(3) Feature recognition of acoustic emission signals: according to the result of the analysis of the acoustic emission signal, signal pattern recognition methods such as wavelet decomposition, time-frequency energy characteristic analysis, statistical theory and the like are utilized, the characteristic vector of the acoustic emission signal is extracted through a harmonic wave packet, and the intelligent diagnosis is carried out on the fault type of the rolling bearing by combining with the pattern recognition technology.
(4) Bearing defect position determination technology research: the faults are mainly caused by pits, peeling and the like of the outer ring and the inner ring of the bearing and the rollers, and the occurring parts are usually an inner raceway, an outer raceway and a roller. Through the classification and comparison of fault signals, the system can determine the defect part according to the signal characteristics caused by the defect.
The intelligent bearing fault diagnosis system consists of two parts, namely software and hardware, wherein the hardware part is mainly used for acquiring fault signals, converting and sending the signals and displaying diagnosis results, and the software part is mainly used for calculating and analyzing the acquired fault signals and diagnosing the fault results.
The hardware part comprises a wheel sensor, an acoustic sensor array box, an MBD acquisition control box, a car number image recognition device and a wireless data transmission device; the wheel sensor is fixed on the side wall of the steel rail by adopting a magnetic steel fixture, and when a train approaches, the automatic MBD acquisition control box can be started to work and is used for positioning, counting the axle and measuring the speed of the bearing.
The acoustic sensor array box is mainly used for protecting the acoustic sensor in the acoustic sensor array box; the MBD control box internally comprises a signal amplification processing box, a car number recognition control box, a signal acquisition processing industrial control computer, a HUB concentrator, a KVM converter and the like, the signal can be subjected to filtering and high-fidelity amplification processing through the matching of all parts, the signal is acquired and processed, the diagnosis and judgment work of a fault bearing is completed through an established mathematical model, and the HUB concentrator and the KVM converter have the functions of connecting networked equipment, improving the comprehensive processing data capacity of rail-side equipment and the high-speed exchange capacity of data, and providing data to form a report and upload; the acoustic signal sensor is of the type selected from pressure field ¼ "microphone 4938, and is capable of operating in harsh environments.
The bearing signal acquisition is carried out by adopting an acoustic sensor array, and the pointing area of the acoustic sensor array is about 6.5 m. If a single acoustic sensor is used, it is impossible to maintain the consistency of the sensitivity of the received signal in such a large directional area, and it is difficult to accurately discriminate the bearing failure. In order to solve the technical problem, the MBD adopts a single-side 6 acoustic sensor array, the effective area of the directivity design of each acoustic sensor is about 1m, and the effective areas are crossed with each other, so that bearing vibration signals received by the sensors in a detection area are continuous. An adaptive calibration technology is adopted between each sensor and the amplifier, so that the consistency of the sensitivity of the received signals of the 6 acoustic sensors is ensured. Since the sound signals are collected by adopting 6 sensor segments, signals received by the 6 sensors need to be synthesized, and the signal synthesis technology is also a key technology of the system. For the condition that adjacent bearings simultaneously enter a detection area of an acoustic sensor array, the system adopts the technologies of speed measurement, distance measurement and the like to distinguish different bearing signals; the method achieves the effects of improving the collection of the bearing signals more comprehensively and accurately and ensuring the reliability and accuracy of the system fault bearing diagnosis.
The car number image recognition device of the hardware part comprises a car passing signal receiving device, a camera package, an LED auxiliary compensation light source and a mainframe box, when the subway car passes through, the car passing signal receiving device receives a signal, controls the camera to take a picture, outputs the taken picture to the mainframe box, and then transmits the information to the software part for processing; the LED auxiliary compensation light source can supplement light, and then the camera can normally work in the dark environment.
The wireless transmission device can adopt a 4G network or a WIF I mode to transmit the acquired information, so that the construction of information transmission equipment is more flexible, a more appropriate mode can be freely selected according to the environment, and the information transmission is facilitated.
The software part comprises data acquisition and processing software, car number Recognition software and online detection and forecast platform software, the car number needs to be recognized in the data acquisition process, the car number Recognition adopts a Tesseract-OCR library for Recognition, the Tesseract is an open-source OCR (Optical Character Recognition) engine, image files in various formats can be recognized and converted into texts, and at present, 60 languages (including Chinese) are supported. The recognition library can be customized according to requirements through a training method, an OCR engine meeting the requirements of the recognition library is developed, the recognition library of the car number is obtained through training, and the trained picture is a processed field actual car passing picture, so that the car number can be recognized.
The collected information is a picture, so that the picture is processed by adopting OpenCvSharp which is a computer vision library of C #, compared with SharpERCV and OpenCVDotNet, the OpenCvSharp directly encapsulates more OpenCV methods, the learning difficulty is reduced, most of the OpenCvSharp inherits IDisposable interfaces, the use statement blocks are convenient to use, and Mono is supported; can run on any platform supporting Mono (such as Linux, BSD, Mac OS X and the like); the acquired original picture is processed through functions of cutting, graying, binaryzation, median filtering, corrosion, expansion, scaling and the like in an OpenCvSharp library through a set program, and the picture with information of vehicle models, vehicle numbers and the like is extracted.
LabVIEW development platform is used by online detection and forecast platform software, developed and developed by (NI) company, and a graphical editing language G compiling program is used, and the generated program is in a block diagram form; and analyzing and judging the collected data to form a vehicle passing message, wherein the message comprises the judgment information of the bearings of the whole train of vehicles.
The establishment of the intelligent diagnosis system for the subway bearing faults comprises the following steps:
as shown in fig. 1, (1) analysis of the rolling bearing failure sound signal; signal processing is the application of mathematical or physical methods to perform various manipulations or transformations on signals. The method aims to filter noise or interference mixed in signals, convert the signals into a form easy to identify and facilitate extraction of characteristic parameters of the signals; therefore, the essence of signal processing is information analysis, extraction and identification, and the flow of signal processing is received signal, preprocessing, signal analysis, fault feature extraction and fault feature identification.
The bearing sound signals collected by the acoustic sensor array arranged on the rail side contain rich information such as running noise of a train, bearing fault characteristics and the like, signal processing is a main means for extracting the bearing fault characteristic information, and the fault characteristic information is a basis for further diagnosing component faults.
(2) Extracting and identifying fault characteristics of the fault bearing:
when a surface of a certain element of the bearing is cracked, instantaneous vibration occurs when the surface rolls over the surface of an adjacent element. Since the movement of the bearing element is periodic, the vibration is also periodic, and the frequency thereof can be calculated according to the law of kinematics; the frequency of the periodic vibration generated by the surface defects of different bearing elements is different because the radius and the center of the circle of the surface defects are different, and the frequency of the periodic vibration is a function of the rotating frequency of the bearing. When the bearing element fails, the envelope signal frequency can be determined by the following calculation.
The calculation formula is as follows:
assuming that the number of rollers in the bearing is Z, the diameter of the rollers is D, the average diameter of the inner and outer rings of the bearing (i.e., the diameter of the revolving path of the rollers) is D, and the frequency of the rotation of the bearing is f0, the inner ring is assumed to be fixed and the outer ring is assumed to rotate (the opposite is possible because the motions are opposite).
The frequency of the vibrations generated by the surface defects of the different bearing elements can be deduced therefrom in the following manner.
(1) The linear velocity of any point of the bearing outer ring is pi (D + D cos alpha)f 0；
(2) The linear velocity of any point of the bearing inner ring is 0;
(3) linear velocity of roller motion is
π(D +d cosα) f 0；
(4) Revolution frequency of the roller is
π(D +d cosα) f 0；
(5) The passing frequency of a single rolling body on the outer ring is the difference between the outer ring rotation frequency and the roller revolution frequency:
(1-
cosα)f 0
(6) the passing frequency of a single rolling body in the inner ring is the roller revolution frequency:
(1+
cosα) f 0
it can therefore be concluded that when different bearing elements fail. The signal frequencies that can be observed are:
f1 is inner circle characteristic frequency (Hz):f 1=
f 0(1+
cosα)Z；
f2 is outer ring characteristic frequency (Hz):f 2=
f 0(1-cosα)Z；
f3 is rolling element characteristic frequency (Hz):f 3=
f 0(1-(
)2cos2α)；
wherein f0 is the rotation frequency (Hz) of the device,f 0=
n is the rotation speed (r/min) of the equipment; d is the diameter (mm) of the rolling body; d is the bearing pitch diameter (mm); z is the number of rolling bodies; α is the pressure angle.
Step three, report formation and uploading: forming a report according to the comparison and judgment result and uploading the report; the networked database is established, and the comparison judgment and the collected information can be stored, so that the aims of establishing a central database, collecting a large amount of bearing fault data, forming a rolling bearing fault diagnosis expert system, dynamically adjusting a system judgment model and improving the self-adaptive capacity of the system are fulfilled.
Through the cooperation of a host computer and software systems in various systems, the collected sound signals and the car number information are synthesized to form an uploading message and a bearing fault message, the system self-checking information is collected, and an equipment state message is formed and then uploaded; the system receives the data message uploaded by the detection station in an FTP mode, adopts the current popular FTP software Serv-U software, configures users and uploads the storage path of the message. And storing the passing message and the self-checking message formed by each train, and sending the passing message and the self-checking message to a server of a subway operation company through a network. Meanwhile, overdue data are automatically cleaned according to setting, so that subsequent vehicle passing processing is facilitated.
The system adopts an Oracle database, and establishes a passing vehicle basic information table, a vehicle information table, a forecast information table and a detection point dictionary information table in the database to store passing vehicle data; the detection station equipment collects data of passing subway vehicles, and after passing the subway vehicles, the collected data are analyzed and distinguished to form vehicle passing messages, wherein the messages contain distinguishing information of bearings of the whole train of vehicles. And uploading the vehicle passing message to a server. A person on duty accesses the MBD special website through a network, monitors the fault bearing information forecasted by the MBD, informs a train inspection patrolman to inspect the arriving fault vehicle, carries out vehicle throwing and wheel changing processing on the bearing with the problem, and feeds back the inspection information on the website. The train inspection carries the replaced wheels to the vehicle section for decomposition, the vehicle section decomposes the fault bearing, and feeds back the decomposition result to the special network. The fault bearing forms closed-loop management from finding, changing wheels and decomposing feedback, so that subsequent data query is facilitated.
The comprehensive monitoring software is developed in a browser/server mode, and a mode of combining graphical display and character display is adopted. In order to make the display content of the information richer and more visual, the system adopts a multi-frame webpage technology, a main monitoring webpage is divided into five subframes according to the display requirements, and the five subframes are respectively used for displaying normal train acquisition information, fault forecast train information, alarm bearing information, equipment state information and dynamic real-time train receiving information, so that a user only needs to open one window during daily monitoring to realize comprehensive monitoring of various information. Meanwhile, a configuration page is designed, and the display content of each subframe can be set in more detail according to actual needs.
To sum up, can be through the cooperation of each part, carry out real-time supervision to the railcar bearing of process, can monitor trouble bearing in advance, and then can in time change trouble bearing, and need not frequent overhaul bearing failure, can provide effectual bearing inside early failure diagnosis result, discover bearing failure before the hot axle, avoid excessive maintenance, reduce bearing failure and move back the unloading rate, reduce the cost of overhaul.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.
Claims (9)
1. A subway vehicle bearing fault on-line detection method is characterized by comprising the following steps: the method comprises the following steps:
establishing a model: establishing a bearing fault model, which is the basis of fault judgment;
fault diagnosis: collecting and diagnosing bearing information of a vehicle;
and forming a report and uploading: and forming a report according to the comparison and judgment result and uploading the report.
2. The subway vehicle bearing fault on-line detection method as claimed in claim 1, wherein: in the first step, through a mathematical spectrum analysis technology and a computer technology, the basic characteristics of the sound signal caused by the vehicle fault bearing can be extracted, analyzed and processed, and the fault characteristics of the bearing roller, the outer ring and the inner ring are respectively modeled.
3. The subway vehicle bearing fault on-line detection method as claimed in claim 2, wherein: in the process of extracting, analyzing and processing the basic characteristics of the sound signals, Morlet wavelet transform is selected to preprocess the signals, and Hilbert demodulation technology is selected to analyze in frequency domain analysis.
4. The subway vehicle bearing fault on-line detection method as claimed in claim 1, wherein: and secondly, establishing an intelligent diagnosis system for the faults of the subway bearings, wherein the intelligent diagnosis system comprises three parts, namely a monitoring means and an analysis principle of the bearings and determination of defective parts of the bearings.
5. The subway vehicle bearing fault on-line detection method as claimed in claim 4, wherein: the intelligent subway bearing fault diagnosis system consists of two parts, namely software and hardware;
the hardware part comprises a wheel sensor, an acoustic sensor array box, an MBD acquisition control box, a car number image recognition device, a wireless data transmission device and a subway vehicle bearing fault online detection and prediction platform device;
the software part comprises data acquisition and processing software, car number identification software and online detection and forecast platform software.
6. The subway vehicle bearing fault on-line detection method as claimed in claim 5, wherein: the car number image recognition device comprises a car passing signal receiving device, a camera package, an LED auxiliary compensation light source, a main case and the like.
7. The subway vehicle bearing fault on-line detection method as claimed in claim 6, wherein: and the stroboscopic lamp of the LED auxiliary compensation light source inclines towards the direction of the ground.
8. The subway vehicle bearing fault on-line detection method as claimed in claim 1, wherein: and in the third step, the collected information and the judgment result form a report and are uploaded to a networked database.
9. The subway vehicle bearing fault on-line detection method as claimed in claim 8, wherein: in the third step, a central database can be established by comparing the result with the acquired information.
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| CN111442928A (en) * | 2020-04-09 | 2020-07-24 | 南京拓控信息科技股份有限公司 | Monitoring management system for rail-side acoustic diagnosis |
| CN114118297A (en) * | 2021-12-08 | 2022-03-01 | 哈尔滨国铁科技集团股份有限公司 | THDS system hot shaft distinguishing method based on artificial intelligence mode recognition technology |
| CN115248119A (en) * | 2022-05-18 | 2022-10-28 | 武汉中誉鼎力智能科技有限公司 | Online detection device and detection method for defects of heavy-load low-speed bearing |
| CN117030724A (en) * | 2023-10-09 | 2023-11-10 | 诺比侃人工智能科技(成都)股份有限公司 | Multi-mode industrial defect analysis method and system based on deep learning |
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