CN110579274A - Vehicle chassis fault sound diagnosis method and system - Google Patents

Vehicle chassis fault sound diagnosis method and system Download PDF

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Publication number
CN110579274A
CN110579274A CN201910841662.4A CN201910841662A CN110579274A CN 110579274 A CN110579274 A CN 110579274A CN 201910841662 A CN201910841662 A CN 201910841662A CN 110579274 A CN110579274 A CN 110579274A
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chassis
sound
fault
abnormal
vehicle
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CN201910841662.4A
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陈玺煌
耿立宏
董乾
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SHU-TE UNIVERSITY
Guangzhou Computer Network Information Center Chinese Academy Of Sciences
Computer Network Information Center of CAS
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SHU-TE UNIVERSITY
Guangzhou Computer Network Information Center Chinese Academy Of Sciences
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
    • H04L12/40—Bus networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
    • H04L12/40—Bus networks
    • H04L2012/40267—Bus for use in transportation systems
    • H04L2012/40273—Bus for use in transportation systems the transportation system being a vehicle

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)

Abstract

The invention provides a vehicle chassis fault sound diagnosis method and system, the method carries out feature extraction on a received chassis sound signal through an abnormal sound classification and identification device; inputting the characteristic parameters of the chassis acoustic signals and the characteristics of the controller local area network bus signals into a chassis fault acoustic model to classify chassis abnormal sounds; and judging the chassis state according to the chassis abnormal sound classification and the chassis sound generation source, and performing chassis fault diagnosis. The method uses the technologies of CAN Bus, multiple vehicle chassis audio receiving devices, sound identification, chassis abnormal sound classification and the like to actively diagnose the fault sound of the vehicle chassis, CAN replace an external vehicle chassis abnormal sound detection device, solves the problem that the abnormal sounds of different chassis components cannot be detected simultaneously in the prior art, CAN actively inform a driver of the fault condition to overhaul as early as possible, and improves the driving safety and comfort.

Description

Vehicle chassis fault sound diagnosis method and system
Technical Field
the invention relates to the technical field of vehicle transportation, in particular to a vehicle chassis fault sound diagnosis method and system.
background
Computer self-diagnosis mechanisms exist in the vehicle from an engine, a gearbox and an in-vehicle electronic system, and a good self-detection method is not provided for a chassis system only. The chassis system of the automobile includes tires, shock absorbers, suspension structures, etc., and is associated with each other. Failure of the chassis components can lead to out of control of the vehicle, increased fuel consumption, poor ride comfort, and the like. The fault symptoms of the automobile chassis system mostly generate abnormal sounds, the abnormal sounds are mostly generated when the automobile runs, and the sound sources are mostly at the bottom of the automobile and are difficult to directly detect. The conventional vehicle chassis inspection is performed in a stationary state of a vehicle, and whether the chassis assembly has an abnormal condition is checked through visual inspection and a shake test, but the conventional static inspection method is insufficient as the vehicle manufacturing technology is improved, such as a large number of devices for damping adjustable shock absorbers, multi-link suspension, automatic leveling of a vehicle body, and the like. The automobile manufacturing process is gradually improved, the sound insulation effect in the automobile is better, and in addition, music can be played in the automobile, so that a driver can hardly perceive the abnormal sound generation of the chassis in the automobile, and other components of the chassis are damaged when the driver perceives the abnormal driving.
Although chassis and tire noise detection devices are proposed in the prior art, these devices cannot automatically distinguish the type of chassis noise or need to be interpreted by professional technicians, and thus are difficult to popularize and promote.
Disclosure of Invention
in order to overcome the above disadvantages, the present invention discloses a method and a system for diagnosing a vehicle chassis fault sound, which enable a driver to know in advance whether an abnormality occurs in a vehicle chassis.
The technical scheme adopted by the invention is as follows:
A vehicle chassis failure sound diagnostic method comprising the steps of:
1) Extracting characteristic parameters of chassis acoustic signals;
2) Inputting the characteristic parameters of the chassis acoustic signal and the characteristics of the controller local area network bus signal into a chassis fault acoustic model, and performing chassis abnormal sound classification by matching with a Viterbi algorithm;
3) and judging the chassis state according to the chassis abnormal sound classification, the controller local area network bus signal characteristics and the chassis sound generation source, and performing chassis fault diagnosis.
Further, the chassis fault acoustic model is constructed by the following training steps, including:
1) Extracting chassis sound signals of vehicles with known chassis faults in road driving;
2) And extracting characteristic parameters of the chassis sound signal and the controller area network bus signal characteristics, and inputting the characteristic parameters and the controller area network bus signal characteristics into a hidden Markov model for training to obtain a chassis fault sound model.
Further, the chassis sound signal is obtained by a chassis sound receiving device; the chassis receiving device comprises microphones arranged at the front and the rear of the chassis at the positions of the four suspension frames.
further, the chassis acoustic signal is divided into overlapping short-time frames.
Further, the extracted feature parameters include frequency domain features and time-frequency domain features.
Further, the frequency domain features include mel-frequency cepstral coefficients, pitch frequency, audio spectrum centroid and audio spectrum spread; the time-frequency domain features include audio spectrum flatness, sub-band energy values, and beat centroids.
Further, the controller area network bus signal characteristics comprise steering wheel rotation angle and vehicle speed.
Further, the chassis condition diagnostics include tire diagnostics, bearing diagnostics, shock absorber diagnostics, control arm diagnostics, and exhaust pipe hanger diagnostics.
further, when the chassis state is found to be abnormal through the chassis state diagnosis, the driver is informed through a warning lamp or a display screen.
A vehicle chassis fault sound diagnosis system comprises a chassis sound receiving module, a characteristic parameter extraction module, an abnormal sound classification module and a chassis state diagnosis module, wherein:
1) The chassis sound receiving module is used for acquiring a vehicle chassis sound signal;
2) The characteristic parameter extraction module is used for extracting characteristic parameters of the vehicle chassis acoustic signals;
3) the abnormal sound classification module is used for receiving the characteristic parameters of the vehicle chassis sound signals and the controller local area network bus signals and classifying the chassis abnormal sounds according to the chassis fault sound model;
4) And the chassis state diagnosis module is used for receiving the chassis abnormal sound classification and controller local area network bus signals, judging the chassis state according to the chassis abnormal sound classification and the chassis sound generation source and providing the chassis state diagnosis.
the invention creatively uses the technologies of controller area network Bus (CAN Bus), multiple vehicle chassis audio receiving devices, sound identification, chassis abnormal sound classification and the like to carry out vehicle chassis fault sound active diagnosis, and CAN replace an external vehicle chassis abnormal sound detection device.
The invention can diagnose the range including tires, bearings, shock absorbers, suspension control arms, exhaust pipe hangers and the like, and solves the problem that the abnormal sound of different chassis components cannot be detected simultaneously in the prior art.
the invention has good identification effect, CAN enhance the chassis sound identification rate generated under different control conditions by combining CAN Bus signal analysis, and improves the system reliability. The operations of turning, braking, accelerating, etc. of the vehicle can generate different stress effects on different components of the chassis, for example, the suspension system on the right side is pressurized when the vehicle turns left, and the sound detection for detecting the suspension system on the right side is enhanced, so that the sound identification degree of the chassis can be further improved.
The invention opens up a new application of the On-Board Diagnostics (OBD) of the On-Board diagnostic system. Most of the existing OBD systems detect the exhaust emission component and the power transmission system, but whether the vehicle chassis is normal or not is also a large factor affecting fuel consumption.
The invention diagnoses the fault sound of the vehicle chassis under different control conditions by the technologies of a multi-audio receiving device arranged on the chassis, chassis abnormal sound classification, sound identification, CAN Bus and the like, CAN automatically judge whether the chassis is abnormal or not without manual interpretation, and CAN actively inform a driver to overhaul as early as possible if the fault condition occurs, thereby improving the driving safety and comfort.
drawings
FIG. 1 is a block diagram of a vehicle chassis fault audible diagnostic system.
FIG. 2 is a schematic diagram of pitch period calculation for the autocorrelation method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the following provides detailed descriptions of the present invention through specific embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
the system block diagram of the present invention is shown in fig. 1.
First, chassis sound receiving
the vehicle is provided with a multi-microphone chassis sound receiving device consisting of microphones arranged at the front and rear four suspension bracket parts of the chassis. The microphone transmits the recorded chassis sound signals to the abnormal sound classification and identification device through the multi-microphone chassis sound receiving device in a wired or wireless mode.
second, chassis sound signal feature extraction
The abnormal sound classification and identification device carries out feature extraction on the received chassis acoustic signal, namely, the input chassis acoustic signal is divided into overlapped short-time sound frames and then is subjected to feature parameter extraction operation, and the abnormal sound classification and identification device comprises the following acoustic feature parameters:
one) Frequency domain Features (Frequency Features) are obtained by taking Mel-Scale Frequency Cepstral Coefficients (MFCC), audio spectral Centroid (Sound spectral Centroid) and audio spectral Spread (Sound spectral Spread) from Fourier Transform (Fourier Transform).
1. The mel-frequency cepstrum coefficient is calculated by setting the extracted chassis acoustic signal as s (n):
1) Passing the chassis acoustic signal s (d) through a high-pass filter to highlight the formants at high frequencies, the time domain expression is: s (d) -a (d-1) wherein a is between 0.9 and 1.0;
2) Framing, namely cutting s (d) into frames of 20-30 ms, wherein the lengths of the frames are overlapped 1/2;
3) Multiplying a sound frame by a Hamming (Hamming) window, wherein w (D, alpha) is (1-alpha) -alpha cos (2 pi n/(D-1)), w () is the Hamming window, D is the number of sampling points of the sound frame, D is less than or equal to 0 and less than or equal to D-1, alpha is a parameter for adjusting the broadband of the Hamming window, and different alpha values can generate different Hamming windows;
4) Fourier Transform is carried out on the sound frame multiplied by the Hamming window, and a spectral energy value is obtained
Wherein k is the frequency indicator and M is the maximum value of the frame sample point;
5) Performing triangular band pass filtering
Wherein
wherein H () is a Mel triangular band pass filter, F () is a frequency, b is a number label of the Mel triangular band pass filter, N is a sound frame length, FsIs the sound sampling frequency, fmelIs the Mel triangular band pass frequency;
6) And obtaining the MFCC parameters by performing discrete cosine transform and logarithmic energy calculation:
wherein, P is pitch frequency parameter, and m is discrete cosine transform number mark value.
The pitch frequency parameter p has many calculation methods, and can be divided into two types of calculation methods, namely a time domain calculation method and a frequency domain calculation method, wherein the time domain calculation method comprises a zero crossing rate algorithm, an autocorrelation algorithm, an AMDF (average amplitude difference function), an ASMDF (average mean square difference function) algorithm and the like, and the common frequency domain calculation method comprises a harmonic overtone inner product spectrum method, a cepstrum analysis method, a maximum likelihood estimation method and the like. The following is only described in the context of an Autocorrelation Method (Autocorrelation Method):
(1) The autocorrelation parameter (auto) is defined as:
Wherein s is the input audio;
(2) r [ l, m ] is to shift a sound frame s [ n ] with a sampling starting point of m for a period of time l, then to perform autocorrelation operation with the original s [ n ] sound frame to obtain a sequence with the same length as the original sound frame, then to take the first local maximum and the second local maximum in the sequence, the time difference of the two points is the pitch period, and to perform reciprocal operation to obtain the pitch frequency parameter p of the sound frame, and fig. 2 is a schematic diagram of pitch period calculation of the autocorrelation method.
2. The audio Spectrum center of gravity (SSC) is calculated as follows: setting piat a certain frequency omegaiThe following energy values, SSC is defined as follows:
where i ranges from 0 to the audio maximum frequency value.
3. the audio Spectrum Spread (SSS) is calculated by extending the definition of SSC, and SSS is further obtained by the following formula:
Two) Time-Frequency domain Features (Time-Frequency Features) which are used for obtaining the audio Frequency Spectrum Flatness (Sound Spectrum Flatness), the sub-band Energy value (sub-band Energy) and the beat center (Temporal center) by Wavelet Transform (Wavelet Transform).
1. The audio Spectrum Flatness (SSF) is calculated as follows: SSF can be derived from the extension of subband coefficients of wavelet transform, let cj(k) and NjRepresenting the approximate coefficients (or detail coefficients) and the number of coefficients of the wavelet transform of the jth order, respectively, the definition of SSF is as follows:
2. The Subband Energy value (SE) is calculated as follows:
1) the input chassis audio signal will be decomposed into 2 by wavelet transformationnGroup subbands cj(k) Each sub-frequency Energy will be further increased its discrimination using a ladder Energy Operator (TEO), which is defined as follows:
Ψ[cj(k)]＝cj 2(k)-cj(k+1)cj(k-1) wherein ψ [ ·]represents TEO, wherein cj(k) The wavelet transform approximation coefficients or detail coefficients representing the jth order;
2) The subband energy can be calculated as:
3. Beat center of gravity (TC) calculation:Wherein f issThe frequency of sampling the chassis acoustic signal is,
Thirdly, establishing a chassis fault sound model
The method comprises the steps of utilizing vehicles with known chassis faults to conduct road driving, collecting various chassis abnormal sounds, extracting the characteristic parameters of the known chassis fault sounds and characteristics of steering wheel corners, vehicle speed and the like in vehicle CAN bus signals with the chassis faults, sending the characteristic parameters and the characteristics into a Hidden Markov Model (HMM) for training, and obtaining chassis fault sound models, wherein the abnormal sounds include abnormal sounds caused by faults of tires, bearings, shock absorbers, suspension control arms, exhaust pipe hangers and the like.
Four, chassis abnormal sound classification
And (3) carrying out chassis abnormal sound classification on the recorded chassis sound and the characteristics of steering wheel corner, vehicle speed and the like in the CAN Bus signal by using the chassis fault sound model obtained in the training step and matching with a Viterbi algorithm (Viterbi algorithm).
The viterbi algorithm is a dynamic programming algorithm widely known in the field, and is used to find the viterbi path most likely to generate the observation event sequence, and can be used for comparing the audio signals. The invention trains and calculates the failure sound model of the tire, the bearing, the shock absorber, the suspension control arm, the exhaust pipe hanger and other parts in advance, and forms a chassis abnormal sound classifier with the Viterbi algorithm, and then matches the chassis sound with the CAN Bus signal, and the most possible failure sound type CAN be obtained through the chassis abnormal sound classifier.
fifth, diagnosis of chassis state
The chassis fault sound diagnosis and warning range includes tyre, bearing, shock absorber, suspension control arm, exhaust pipe hanger, etc. and the abnormal conditions, such as wear of the bearing, failure of the shock absorber, excessive gap between the control arm, etc. are judged to be normal or required to be maintained.
chassis condition diagnostic case description (tires, bearings, shock absorbers, suspension control arms, exhaust pipe hangers).
1) In the case of tire diagnosis, the probability of the abnormal sound belonging to the tire is determined to be the highest by the chassis sound classification, and the chassis sound generation source is close to the right front wheel and the left front wheel. Since the tire is abnormally worn and the rolling noise is directly related to the vehicle speed, the tire abnormality of the right front wheel and the left front wheel CAN be determined when the amplitude and the frequency of the abnormal noise are related to the vehicle speed signal obtained by the CAN Bus signal.
2) Bearing diagnosis example, the probability of the abnormal sound belonging to the bearing is determined to be highest in the classification of the chassis sound, and the generation source of the chassis sound is close to the left front wheel. Since the bearing is mounted on the hub axle, its noise is directly related to the speed of the vehicle. Therefore, when the amplitude and frequency of the abnormal sound are related to the vehicle speed signal obtained from the CAN Bus signal, the wear of the left front bearing CAN be determined.
3) Example of the vibration damper diagnosis, the chassis sound classification has judged that the probability of abnormal sound belonging to the vibration damper is the highest, and the chassis sound generation source is close to the right front wheel. Because the front and rear wheel shock absorbers are stressed by compression and elongation during braking, if the shock absorbers fail, abnormal noise is generated during braking. Therefore, when the amplitude and frequency of the abnormal sound are related to the braking signal obtained from the CAN Bus signal, it CAN be determined that the right front shock absorber is out of order.
4) Control arm diagnosis example, the chassis sound classification has determined that the probability of abnormal sound belonging to the control arm is highest, and the chassis sound generation source is close to the left rear wheel. Because the control arm of the chassis of the vehicle is stressed when the vehicle is bent at a high speed, if the gap between the control arms is too large, abnormal sound is generated when the vehicle is bent at a high speed. Therefore, when the amplitude and frequency of the abnormal sound are related to the steering wheel angle/vehicle speed signal obtained from the CAN Bus signal, it CAN be determined that the left rear control arm gap is too large.
5) exhaust hanger diagnostic example chassis sound classification has determined that the probability of abnormal sound belonging to an exhaust hanger is highest, while the chassis sound generation source is close to the exhaust pipe. Since the exhaust pipe hanger is connected with the vehicle body by the rubber, if the rubber of the exhaust pipe hanger fails or the gap is too large, the exhaust pipe collides with the vehicle body during running to generate abnormal sound, so that the rubber of the exhaust pipe hanger fails or the gap is too large.
the above embodiments are only intended to illustrate the technical solution of the present invention and not to limit the same, and a person skilled in the art can modify the technical solution of the present invention or substitute the same without departing from the spirit and scope of the present invention, and the scope of the present invention should be determined by the claims.

Claims (10)

1. A vehicle chassis failure sound diagnostic method comprising the steps of:
1) Extracting characteristic parameters of chassis acoustic signals;
2) Inputting the characteristic parameters of the chassis acoustic signal and the characteristics of the controller local area network bus signal into a chassis fault acoustic model, and performing chassis abnormal sound classification by matching with a Viterbi algorithm;
3) And judging the chassis state according to the chassis abnormal sound classification, the controller local area network bus signal characteristics and the chassis sound generation source, and performing chassis fault diagnosis.
2. The method of claim 1, wherein the chassis fault acoustic model is constructed by training steps comprising:
1) extracting chassis sound signals of vehicles with known chassis faults in road driving;
2) And extracting characteristic parameters of the chassis sound signal and the controller area network bus signal characteristics, and inputting the characteristic parameters and the controller area network bus signal characteristics into a hidden Markov model for training to obtain a chassis fault sound model.
3. The method of claim 1 or 2, wherein the chassis acoustic signal is obtained by a chassis acoustic receiver; the chassis receiving device comprises microphones arranged at the front and the rear of the chassis at the positions of the four suspension frames.
4. A method according to claim 1 or 2, wherein the chassis acoustic signal is divided into overlapping short timeframes.
5. the method of claim 1 or 2, wherein the extracted feature parameters comprise frequency domain features, time-frequency domain features.
6. the method of claim 5, wherein the frequency domain features include mel-frequency cepstral coefficients, an audio spectral centroid and an audio spectral spread; the time-frequency domain features include audio spectrum flatness, sub-band energy values, and beat centroids.
7. The method of claim 1 or 2, wherein the can bus signal characteristics include steering wheel angle, vehicle speed.
8. The method of claim 1, wherein the chassis condition diagnostics include tire diagnostics, bearing diagnostics, shock absorber diagnostics, control arm diagnostics, and exhaust pipe hanger diagnostics.
9. the method of claim 1, wherein the driver is notified via a warning light or a display screen when the chassis condition is found to be abnormal by the chassis condition diagnosis.
10. a vehicle chassis fault sound diagnosis system comprises a chassis sound receiving module, a characteristic parameter extraction module, an abnormal sound classification module and a chassis state diagnosis module, wherein:
1) The chassis sound receiving module is used for acquiring a vehicle chassis sound signal;
2) the characteristic parameter extraction module is used for extracting characteristic parameters of the vehicle chassis acoustic signals;
3) the chassis abnormal sound classification module is used for receiving the characteristic parameters of the vehicle chassis sound signals and the controller local area network bus signals and classifying the chassis abnormal sound according to a chassis fault sound model;
4) and the chassis state diagnosis module is used for receiving the chassis abnormal sound classification and controller local area network bus signals, judging the chassis state according to the chassis abnormal sound classification and the chassis sound generation source and providing the chassis state diagnosis.
CN201910841662.4A 2019-07-24 2019-09-06 Vehicle chassis fault sound diagnosis method and system Pending CN110579274A (en)

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Application publication date: 20191217