CN202119630U - On-line monitoring and fault diagnosis device for bench test of reliability of microplane automobile driving axle - Google Patents

On-line monitoring and fault diagnosis device for bench test of reliability of microplane automobile driving axle Download PDF

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Publication number
CN202119630U
CN202119630U CN2011202189359U CN201120218935U CN202119630U CN 202119630 U CN202119630 U CN 202119630U CN 2011202189359 U CN2011202189359 U CN 2011202189359U CN 201120218935 U CN201120218935 U CN 201120218935U CN 202119630 U CN202119630 U CN 202119630U
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CN
China
Prior art keywords
data
module
fault diagnosis
drive axle
axle
Prior art date
Application number
CN2011202189359U
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Chinese (zh)
Inventor
李惠彬
余波
宋金响
黄华
宁梦茜
张曼
钱乾
Original Assignee
北京理工大学
重庆长安汽车股份有限公司
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Application filed by 北京理工大学, 重庆长安汽车股份有限公司 filed Critical 北京理工大学
Priority to CN2011202189359U priority Critical patent/CN202119630U/en
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Publication of CN202119630U publication Critical patent/CN202119630U/en

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Abstract

The utility model discloses an on-line monitoring and fault diagnosis device for a bench test of reliability of a microplane automobile driving axle. Four sets of vibration accelerometers and four sets of temperature sensors are distributed at a left half shaft bearing block of a driving axle, a right half shaft bearing block of the driving axle, a driving axle main reducer shell and positions where a transmission shaft is connected with the bearing blocks respectively. Three sets of torque sensors are distributed at a left half shaft of the driving axle, a right half shaft of the driving axle and a position between connection of the transmission shaft and a motor respectively. Four sets of sound level meters are distributed at a position which is 50cm away from the left half shaft bearing block of the driving axle, the right half shaft bearing block of the driving axle, the driving axle main reducer shell and the positions where the transmission shaft is connected with the bearing blocks respectively. Three sets of stress sensors are distributed at the driving axle main reducer shell and supporting positions of a right half shaft bearing of the driving axle and a left half shaft bearing of the driving axle respectively. A control unit acquires big and small cycle data for fault diagnosis and reliability prediction. The on-line monitoring and fault diagnosis device for the bench test of the reliability of the micorplane automobile driving axle can be used for monitoring vibration, noise, temperature, torque, stress parameters of key positions on the driving axle, thereby achieving the fault diagnosis and the reliability prediction, and obtaining effects of on-line monitoring and fault diagnosis.

Description

Little automobile drive axle reliability bench test on-line monitoring and failure diagnosis apparatus
Technical field
The utility model relates to little automobile drive axle technical field, is specifically related to a kind of little automobile drive axle reliability bench test on-line monitoring and failure diagnosis apparatus.
Background technology
Automobile drive axle is one of critical component of little automobile, and condition of work is also abominable.Therefore its functional reliability is the key that guarantees vehicle power property and travelling and fuel economy.Rear driving axle carries out in the fail-test process on stand; Along with the input of transmission shaft power and motion, mainly subtract the gear meshing transmission; Comprise the main parts such as gear, spring bearing, semiaxis, differential gear, vehicle bridge that subtract; Not only normal rubbing wear will take place, and will produce vibration and noise, certain variation also will take place in the lubricating oil temperature.So on the stand when drive axle is carried out fail-test; If research and develop the parameters such as vibration, noise, temperature, moment of torsion, stress of key position on cover online monitoring and the failure system monitoring driving bridge, fatigue lifetime and reliability mechanism, micromechanism of damage, the vibration noise mechanism of studying the drive axle key components and parts had very great Practical significance.
The utility model content
In view of this; The utility model provides a kind of little automobile drive axle reliability bench test on-line monitoring and failure diagnosis apparatus; Parameters such as the vibration of key position, noise, temperature, moment of torsion, stress on the monitoring driving bridge comparatively all sidedly; And then can utilize these parameters to carry out fault diagnosis and reliability assessment, thereby reach the effect of on-line monitoring and fault diagnosis.
This scheme is achieved in that
A kind of little automobile drive axle reliability bench test on-line monitoring and failure diagnosis apparatus; Comprise: 4 cover vibration acceleration meters, 3 cover torque sensors, 4 sleeving temperature sensors, 3 cover strain gauges, 4 cover sound meters and speed probe, signal processor, each cover of data collecting card, the collection of data low speed and memory module, fault diagnosis module, data high-speed acquisition module, database, reliability prediction module, and figure output module;
4 cover vibration acceleration score are not arranged in drive axle left half axle bearing seat, the drive axle master subtracts housing, drive axle right axle shaft bearing seat, propeller shaft couplings bearing seat place;
3 cover torque sensors are arranged in drive axle left half axle, drive axle right axle shaft, transmission shaft and between motor connects;
4 sleeving temperature sensors are identical with 4 vibration acceleration meter positions;
4 cover sound meters be arranged in position apart from drive axle left half axle bearing seat 50cm, apart from the drive axle master subtract the position of housing 50cm, apart from the position of drive axle right axle shaft bearing seat 50cm, apart from the position of propeller shaft couplings bearing seat 50cm;
3 cover strain gauges are arranged in the drive axle master and subtract housing, drive axle left half axle bearings position, drive axle right axle shaft bearings position;
Speed probe is arranged between transmission shaft and the motor coupling arrangement;
Signal processor connects all the sensors and data collecting card, and the collection of data low speed connects large period data acquisition module and minor cycle data acquisition module respectively; The large period data acquisition module connects database, minor cycle data acquisition module, fault diagnosis module and figure output module respectively; The minor cycle data acquisition module connects database, fault diagnosis module and figure output module respectively; Fault diagnosis module connects database, reliability prediction module and figure output module respectively; The reliability prediction diagnostic module further connects database and figure output module.
Wherein, signal processor has impedance conversion, integration, filtering and enlarging function, and the data of all the sensors collection are carried out exporting to data collecting card after the digital signal processing;
The sensor signal of data collecting card after according to setpoint frequency acquired signal processor processes;
The large period data acquisition module; Receive the sensor signal that data collecting card is sent; Maximal value in every road sensor signal in the per minute is saved in the database as the large period data, and the large period data are sent to the figure output module as one of Monitoring Data; When vibration, noise or temperature signal effective value surpass national normal value or industry standard value, start the minor cycle data acquisition module and trigger fault diagnosis module.
The minor cycle data acquisition module; The real time data of after being activated, from data collecting card, obtaining each sensor is the minor cycle data; Each channel data length is saved in the minor cycle data database and sends to the figure output module as one of Monitoring Data greater than 4096 point;
Fault diagnosis module; Extract from database in the back that is triggered at least 2 hours recently large period data and minor cycle data acquisition module this be activated the minor cycle data of back acquisition; Utilize the data of extracting to carry out fault diagnosis, fault diagnosis result is sent to reliability prediction module and figure output module;
The reliability prediction module; Extract from database at least 2 hours recently large period data and minor cycle data acquisition module this be activated the minor cycle data of back acquisition; Utilize the motion feature of large period data, minor cycle data, moving component and the health index that fault diagnosis result obtains each parts; Draw the time dependent trend curve of health index, and utilize time series models prediction drive axle reliability, the reliability prediction result is sent to the figure output module;
The figure output module, output Monitoring Data, fault diagnosis result, reliability prediction result;
Database, the various signals after being used to store various sensor signals and passing through digital signal processing.
Beneficial effect
The utility model is based on main theory and the experimental study that subtracts the relation of gear, the research of bearing fault vibration noise mechanism, vibration noise and drive axle reliable life; Confirmed a series of crucial measuring point; Adopt vibration, noise, temperature, moment of torsion, rotating speed, the stress signal of these crucial measuring points of various kinds of sensors monitoring; Utilize the sensor signal of crucial measuring point to carry out fault diagnosis, thereby reach the effect of comprehensive on-line monitoring and fault diagnosis.
Drive axle on-line monitoring and fault diagnosis system that the utility model designed can provide an evaluation criterion for the fail-test of drive axle, help control and improvement that the drive axle vibration noise is polluted.This system not only can the using vehicle drive axle monitoring and fault diagnosis, also can be applied to the collection and the analysis of other mechanical system vibration and noise signals.
Description of drawings
Fig. 1 is the structural representation of the utility model on-line monitoring and failure diagnosis apparatus.
Embodiment
Below in conjunction with the accompanying drawing embodiment that develops simultaneously, the utility model is described in detail.
The utility model provides a kind of little automobile drive axle reliability bench test on-line monitoring and failure diagnosis apparatus; Based on main theory and the experimental study that subtracts gear, the research of bearing fault vibration noise mechanism, vibration noise and drive axle reliable life relation; Confirmed a series of crucial measuring point; Adopt vibration, noise, temperature, moment of torsion, rotating speed, the stress signal of these crucial measuring points of various kinds of sensors monitoring; Utilize the sensor signal of crucial measuring point to carry out fault diagnosis, thereby reach the effect of on-line monitoring and fault diagnosis.
Fig. 1 is the structural representation of the utility model on-line monitoring and failure diagnosis apparatus; As shown in Figure 1, this equipment comprises: 4 cover vibration acceleration meters, 3 cover torque sensors, 4 sleeving temperature sensors, 3 cover strain gauges, 4 cover sound meters, 1 cover speed probe, 1 cover signal processor and control module; Control module comprises data collecting card, the collection of data low speed and memory module, fault diagnosis module, data high-speed acquisition module, database, reliability prediction module, and the figure output module.
4 cover vibration acceleration score are not arranged in drive axle left half axle bearing seat, the drive axle master subtracts housing, drive axle right axle shaft bearing seat, propeller shaft couplings bearing seat place; 3 cover torque sensors are arranged in drive axle left half axle, drive axle right axle shaft, transmission shaft and between motor connects; 4 sleeving temperature sensors are identical with 4 vibration acceleration meter positions; 4 cover sound meters be arranged in position apart from drive axle left half axle bearing seat 50cm, apart from the drive axle master subtract the position of housing 50cm, apart from the position of drive axle right axle shaft bearing seat 50cm, apart from the position of propeller shaft couplings bearing seat 50cm; 3 cover strain gauges are arranged in the drive axle master and subtract housing, drive axle left half axle bearings position, drive axle right axle shaft bearings position; Speed probe is arranged between transmission shaft and the motor coupling arrangement.
Signal processor connects all the sensors and data collecting card, and the collection of data low speed connects large period data acquisition module and minor cycle data acquisition module respectively; The large period data acquisition module connects database, minor cycle data acquisition module, fault diagnosis module and figure output module respectively; The minor cycle data acquisition module connects database, fault diagnosis module and figure output module respectively; Fault diagnosis module connects database, reliability prediction module and figure output module respectively; The reliability prediction diagnostic module further connects database and figure output module.
Be described in detail in the face of each functions of modules of removing sensor down.
Signal processor has impedance conversion, integration, filtering and enlarging function, and the data of all the sensors collection are carried out exporting to data collecting card after the digital signal processing.
The sensor signal of data collecting card after according to setpoint frequency and data length acquired signal processor processes.
The large period data acquisition module; Receive the sensor signal that data collecting card is sent; Maximal value in every road sensor signal in the per minute is saved in the database as the large period data; Thereby obtained vibration, noise, temperature, moment of torsion, the main position of drive axle axle housing stress signal long periodicity rule, and the large period data have been sent to the figure output module as one of Monitoring Data, so that the output of figure output module shows.When vibration, noise or temperature signal effective value surpass national normal value or industry standard value 10% the time, can judge the rear driving axle operation irregularity, start the minor cycle data acquisition module and also trigger fault diagnosis module.
The minor cycle data acquisition module; The real time data of after being activated, from data collecting card, obtaining each sensor is the minor cycle data; Data length can be set arbitrarily; But each channel data length is greater than 4096 points, and setting each channel data length in the present embodiment is 4096, and the minor cycle data of obtaining are saved in database and send to the figure output module as one of Monitoring Data.
Fault diagnosis module; Extract from database in the back that is triggered at least 2 hours recently large period data and minor cycle data acquisition module this be activated the minor cycle data of 4096 points of back acquisition; Time domain, frequency domain and the amplitude domain parameter of the vibration that utilization is extracted, noise, temperature, moment of torsion, the main stress signal of drive axle axle housing; Especially kurtosis coefficient is accomplished the General Troubleshooting of drive axle main parts size, and General Troubleshooting comprises wearing and tearing, spot corrosion, the gummed of gear; The crackle of the wearing and tearing of bearing, tired spot corrosion, crackle, axle sends to reliability prediction module and figure output module with fault diagnosis result.In this module, can adopt three-layer neural network to set up the inner link of parameters such as vibration, noise, temperature, moment of torsion and various faults.
The reliability prediction module; Extract from database at least 2 hours recently large period data and minor cycle data acquisition module this be activated the minor cycle data of back acquisition; Utilize the motion feature of large period data, minor cycle data, moving component and the health index that fault diagnosis result obtains each parts; Draw the time dependent trend curve of health index, and utilize time series models prediction drive axle reliability, the reliability prediction result is sent to the figure output module.
The figure output module, output Monitoring Data, fault diagnosis result, reliability prediction result (screen display and printing), the generation and the print job of completion test report.
Database, the various signals after being used to store each sensor signal and passing through digital signal processing.
This equipment can also comprise:
Parameter is provided with module, and completion drive axle model, master subtract settings such as the gear number of teeth, modulus, bearing designation, signals collecting frequency, data length.
The parameter calibration module is accomplished the demarcation of sensors such as vibration, noise, temperature, and demarcation is saved in concrete numerical value in the database after accomplishing.
The data management function module is used for management database, design result and original input parameter is called, and the file of storage input and output is managed and revised.
In sum, more than being merely the preferred embodiment of the utility model, is not the protection domain that is used to limit the utility model.All within the spirit and principle of the utility model, any modification of being done, be equal to replacement, improvement etc., all should be included within the protection domain of the utility model.

Claims (2)

1. one kind little automobile drive axle reliability bench test on-line monitoring and failure diagnosis apparatus; It is characterized in that; Comprise: 4 cover vibration acceleration meters, 3 cover torque sensors, 4 sleeving temperature sensors, 3 cover strain gauges, 4 cover sound meters and speed probe, signal processor, each cover of data collecting card, the collection of data low speed and memory module, fault diagnosis module, data high-speed acquisition module, database, reliability prediction module, and figure output module;
4 cover vibration acceleration score are not arranged in drive axle left half axle bearing seat, the drive axle master subtracts housing, drive axle right axle shaft bearing seat, propeller shaft couplings bearing seat place;
3 cover torque sensors are arranged in drive axle left half axle, drive axle right axle shaft, transmission shaft and between motor connects;
4 sleeving temperature sensors are identical with 4 vibration acceleration meter positions;
4 cover sound meters be arranged in position apart from drive axle left half axle bearing seat 50cm, apart from the drive axle master subtract the position of housing 50cm, apart from the position of drive axle right axle shaft bearing seat 50cm, apart from the position of propeller shaft couplings bearing seat 50cm;
3 cover strain gauges are arranged in the drive axle master and subtract housing, drive axle left half axle bearings position, drive axle right axle shaft bearings position;
Speed probe is arranged between transmission shaft and the motor coupling arrangement;
Signal processor connects all the sensors and data collecting card, and the collection of data low speed connects large period data acquisition module and minor cycle data acquisition module respectively; The large period data acquisition module connects database, minor cycle data acquisition module, fault diagnosis module and figure output module respectively; The minor cycle data acquisition module connects database, fault diagnosis module and figure output module respectively; Fault diagnosis module connects database, reliability prediction module and figure output module respectively; The reliability prediction diagnostic module further connects database and figure output module.
2. little automobile drive axle reliability bench test on-line monitoring as claimed in claim 1 and failure diagnosis apparatus is characterized in that,
Signal processor has impedance conversion, integration, filtering and enlarging function, and the data of all the sensors collection are carried out exporting to data collecting card after the digital signal processing;
The sensor signal of data collecting card after according to setpoint frequency acquired signal processor processes;
The large period data acquisition module; Receive the sensor signal that data collecting card is sent; Maximal value in every road sensor signal in the per minute is saved in the database as the large period data, and the large period data are sent to the figure output module as one of Monitoring Data; When vibration, noise or temperature signal effective value surpass national normal value or industry standard value, start the minor cycle data acquisition module and trigger fault diagnosis module;
The minor cycle data acquisition module; The real time data of after being activated, from data collecting card, obtaining each sensor is the minor cycle data; Each channel data length is saved in the minor cycle data database and sends to the figure output module as one of Monitoring Data greater than 4096 point;
Fault diagnosis module; Extract from database in the back that is triggered at least 2 hours recently large period data and minor cycle data acquisition module this be activated the minor cycle data of back acquisition; Utilize the data of extracting to carry out fault diagnosis, fault diagnosis result is sent to reliability prediction module and figure output module;
The reliability prediction module; Extract from database at least 2 hours recently large period data and minor cycle data acquisition module this be activated the minor cycle data of back acquisition; Utilize the motion feature of large period data, minor cycle data, moving component and the health index that fault diagnosis result obtains each parts; Draw the time dependent trend curve of health index, and utilize time series models prediction drive axle reliability, the reliability prediction result is sent to the figure output module;
The figure output module, output Monitoring Data, fault diagnosis result, reliability prediction result;
Database, the various signals after being used to store various sensor signals and passing through digital signal processing.
CN2011202189359U 2011-06-26 2011-06-26 On-line monitoring and fault diagnosis device for bench test of reliability of microplane automobile driving axle CN202119630U (en)

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CN2011202189359U CN202119630U (en) 2011-06-26 2011-06-26 On-line monitoring and fault diagnosis device for bench test of reliability of microplane automobile driving axle

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Application Number Priority Date Filing Date Title
CN2011202189359U CN202119630U (en) 2011-06-26 2011-06-26 On-line monitoring and fault diagnosis device for bench test of reliability of microplane automobile driving axle

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Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102645341A (en) * 2012-04-19 2012-08-22 李军 Method and system for detecting health condition of motor vehicle
CN102748214A (en) * 2012-07-10 2012-10-24 国电联合动力技术有限公司 Wind generation set state monitoring and fault diagnosis system coupled to control system
CN102758727A (en) * 2012-07-11 2012-10-31 国电联合动力技术有限公司 Wind turbine state monitoring and error diagnosis system and method integrated into control system
CN104634588A (en) * 2015-03-11 2015-05-20 重庆理工大学 Measurement method for support stiffness of drive axle
CN104792526A (en) * 2015-04-29 2015-07-22 湖南科技大学 Wind power gearbox dynamic response multi-parameter detection device
CN104807635A (en) * 2015-04-29 2015-07-29 上汽通用五菱汽车股份有限公司 Testing system and method of portable main retarder
CN105021363A (en) * 2014-04-30 2015-11-04 上海冠图防雷科技有限公司 Ship structure vibration and noise forecasting system based on S-P-R
US20160223431A1 (en) * 2015-02-02 2016-08-04 Goodrich Corporation Systems and methods for detecting wheel bearing wear with mounted accelerometers
CN104535317B (en) * 2014-12-19 2017-02-22 长安大学 Closed driving axle testing device capable of automatically simulating vehicle steering
CN109084997A (en) * 2018-09-14 2018-12-25 淮安信息职业技术学院 A kind of the new-energy automobile fault detection means and detection method of automation

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102645341A (en) * 2012-04-19 2012-08-22 李军 Method and system for detecting health condition of motor vehicle
CN102645341B (en) * 2012-04-19 2015-05-20 李军 Method and system for detecting health condition of motor vehicle
CN102748214A (en) * 2012-07-10 2012-10-24 国电联合动力技术有限公司 Wind generation set state monitoring and fault diagnosis system coupled to control system
CN102748214B (en) * 2012-07-10 2014-09-03 国电联合动力技术有限公司 Wind generation set state monitoring and fault diagnosis system coupled to control system
CN102758727B (en) * 2012-07-11 2014-10-08 国电联合动力技术有限公司 Wind turbine state monitoring and error diagnosis system and method integrated into control system
CN102758727A (en) * 2012-07-11 2012-10-31 国电联合动力技术有限公司 Wind turbine state monitoring and error diagnosis system and method integrated into control system
CN105021363B (en) * 2014-04-30 2017-06-23 上海冠图电气科技有限公司 Ship Structure vibration and noise forecast system based on S P R
CN105021363A (en) * 2014-04-30 2015-11-04 上海冠图防雷科技有限公司 Ship structure vibration and noise forecasting system based on S-P-R
CN104535317B (en) * 2014-12-19 2017-02-22 长安大学 Closed driving axle testing device capable of automatically simulating vehicle steering
US9857272B2 (en) * 2015-02-02 2018-01-02 Goodrich Corporation Systems and methods for detecting wheel bearing wear with mounted accelerometers
US20160223431A1 (en) * 2015-02-02 2016-08-04 Goodrich Corporation Systems and methods for detecting wheel bearing wear with mounted accelerometers
CN104634588A (en) * 2015-03-11 2015-05-20 重庆理工大学 Measurement method for support stiffness of drive axle
CN104634588B (en) * 2015-03-11 2017-03-08 重庆理工大学 A kind of drive axle support stiffness measuring method
CN104807635A (en) * 2015-04-29 2015-07-29 上汽通用五菱汽车股份有限公司 Testing system and method of portable main retarder
CN104792526A (en) * 2015-04-29 2015-07-22 湖南科技大学 Wind power gearbox dynamic response multi-parameter detection device
CN104792526B (en) * 2015-04-29 2018-03-20 湖南科技大学 Wind turbine gearbox dynamic response Multi-parameter detection device
CN109084997A (en) * 2018-09-14 2018-12-25 淮安信息职业技术学院 A kind of the new-energy automobile fault detection means and detection method of automation

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Granted publication date: 20120118

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