CN113218667B - Fault diagnosis device and method for giant magnetostrictive brake-by-wire system - Google Patents
Fault diagnosis device and method for giant magnetostrictive brake-by-wire system Download PDFInfo
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Abstract
The invention discloses a fault diagnosis device and method for a giant magnetostrictive brake-by-wire system, which comprises the following steps: the braking state detection unit, the braking action detection unit, the current detection unit, the braking displacement transmission detection unit, the tire detection unit and the calculation unit; on the basis of a giant magnetostrictive brake-by-wire system, various sensors are used for detecting components of each brake system in the braking process, and then the fault is diagnosed based on sensor data; the device is easy to be integrated with the existing giant magnetostrictive line control brake system, and is beneficial to realization.
Description
Technical Field
The invention belongs to the technical field of vehicle fault diagnosis, and particularly relates to a fault diagnosis device and method for a giant magnetostrictive brake-by-wire system.
Background
The braking of the vehicle directly influences the traffic safety and guarantees the life and property safety of drivers and pedestrians. The braking effect is quick and effective, and is particularly important for the overall performance and the working state of the automobile, so that in order to avoid the braking failure of a braking system, the faults need to be diagnosed and positioned accurately in time, troubleshooting is carried out, and the driving safety is guaranteed.
The invention provides a device and a method for diagnosing two faults, namely a brake circuit fault and a brake switch clamping stagnation, which are provided in Chinese invention with the patent application number of CN110733489A and the patent name of 'a vehicle brake fault diagnosis device and method', and improve the accuracy of brake fault judgment.
The invention has the Chinese patent application number of 201911130905.X, and the patent name of the disc type brake-by-wire system based on magnetostrictive materials and the control method thereof provides the disc type brake-by-wire system based on magnetostrictive materials, which utilizes the characteristics of the magnetostrictive materials, takes the magnetostrictive materials as a driving source, and controls the brake system by controlling the current in the magnet exciting coil, thereby eliminating the defects of the electric control hydraulic brake system; compared with a mechanical brake-by-wire system, the brake-by-wire system has the advantages of simple structure, light weight, small volume and low energy consumption, and is beneficial to control.
However, in the prior art, only the fault diagnosis is carried out on the fault of the brake circuit and the clamping stagnation of the brake switch, and various fault reasons are not diagnosed; the giant magnetostrictive brake-by-wire system is not provided with a fault diagnosis device and method, and a fault diagnosis method of the brake-by-wire system is needed, so that the problem of troubleshooting when the brake fails is solved, and a basic effect on subsequent fault-tolerant control is achieved.
Disclosure of Invention
In view of the defects of the prior art, the invention aims to provide a fault diagnosis device and method for a giant magnetostrictive brake-by-wire system, which utilize the sensor data to be acquired when a brake process of the giant magnetostrictive brake system fails to extract and fuse the diagnosis evidence by classification of the sensor data, and infer and decide the type of the fault, thereby improving the accuracy of brake fault judgment and the fault tolerance of the brake-by-wire system.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention relates to a fault diagnosis device for a giant magnetostrictive brake-by-wire system, which comprises: the braking state detection unit, the braking action detection unit, the current detection unit, the braking displacement transmission detection unit, the tire detection unit and the calculation unit;
the braking state detection unit is used for detecting the variation sequence of the braking force of the four wheels and the wheel speed along with time;
the braking action detection unit is used for detecting the stroke of a vehicle brake pedal;
a current detection unit for detecting a current passing through a circuit in the wheel brake actuator;
the brake displacement detection unit is used for detecting the expansion amount of the giant magnetostrictive rod and the displacement amount of the piston connecting rod;
the brake displacement transmission detection unit is used for detecting the displacement and the stress loss degree of the transmission rod caused by material rigidity factors in the displacement transmission process;
a tire detection unit for detecting a tire rotation and vibration state;
and the calculating unit is respectively electrically connected with the braking state detecting unit, the braking action detecting unit, the current detecting unit, the braking displacement transmission detecting unit and the tire detecting unit and is used for determining the fault type when the vehicle braking failure occurs according to the obtained data sent by each unit.
Further, the braking state detection unit includes: vehicle speed sensor, wheel speed sensor, braking force sensor.
Further, the current detected by the current detection unit includes currents of the exciting coil and the demagnetizing coil.
Further, the braking displacement detection unit comprises micro displacement sensors which are respectively arranged on the giant magnetostrictive rod and the piston connecting rod.
Further, the brake displacement transmission detecting unit includes a stress strain gauge disposed on the displacement amplification mechanism.
Further, the tire detection unit comprises a tire pressure detection device and a tire vibration sensor.
Furthermore, the braking state detection unit judges the vehicle speed and braking force information acquired by a vehicle speed sensor and a braking force sensor; if the braking force exists, the vehicle is in the braking process, and the pedal displacement sensor data of the braking action detection unit is predicted; and judging the brake failure degree of each wheel according to the wheel speed and the vehicle speed information of each wheel, which are acquired by the wheel speed sensor.
Further, the current detection unit detects the magnitude of the current flowing through the wheel brake driver, that is, the excitation current and the demagnetization current, and is used for the calculation unit to determine whether the Current Regulation Module (CRM) has a fault.
Further, the braking displacement detection unit judges the response effect of the giant magnetostrictive rod according to the expansion amount of the giant magnetostrictive rod so as to distinguish current regulation faults from giant magnetostrictive rod execution response faults and judge whether the clearance between the caliper and the brake disc is too large; and whether the transmission rod has a fault is judged according to the displacement of the piston connecting rod.
Further, the tire detection unit detects the rotation and vibration states of the tire to judge whether the tire has the problems of rotation vibration and inconsistency, and diagnoses the brake failure reasons of tire factors including insufficient air pressure, pattern abrasion and inconsistent color.
The invention relates to a fault diagnosis method for a giant magnetostrictive brake-by-wire system, which is based on the device and comprises the following steps:
(1) the method comprises the following steps of stepping down a brake pedal, and transmitting signals collected by sensors in a brake state detection unit, a brake action detection unit, a current detection unit, a brake displacement transmission detection unit and a tire detection unit to a calculation unit;
(2) the calculating unit detects the sensor data x collected by each detecting unit after the brake failure is detected according to the brake state information collected in the step (1)1,x2,...,xnDetecting and judging the type of the primary fault;
(3) according to the preliminary fault types obtained by judging in the step (2), specific fault types are divided aiming at different preliminary fault types;
(4) and (4) obtaining a fault diagnosis result according to the specific fault type obtained in the step (3).
Further, the sensor signal collected in step (1) includes: the system comprises a brake force signal of each wheel, a vehicle speed signal, a pedal displacement signal, a brake circuit exciting current signal, a brake circuit demagnetizing current signal, a piston connecting rod displacement, a brake transmission rod strain, and a rotating speed and vibration signal of each tire.
Further, the number of sensors in the step (2)According to x1,x2,...,xnRespectively as follows: the braking force of each wheel, the vehicle speed, the pedal displacement, the exciting current of a braking circuit, the demagnetizing current of the braking circuit, the displacement of a piston connecting rod, the strain of a brake transmission rod, the rotating speed of each tire and the vibration amplitude.
Further, the preliminary fault type determination method in step (2) is as follows: if data loss and data abnormality occur, judging that the sensor is in fault; and if the sensor data is not obviously abnormal, judging that the actuator has a fault.
Further, the specific fault type division method in step (3) is as follows:
(31) if the preliminary fault type in the step (2) is a sensor fault, specific sensor fault type division is carried out, and the steps are as follows:
(311) if the sensor data has a constant, the sensor fault is indicated to be stuck;
(312) if the signal data in the sensor data long sequence presents constant gain, the gain change fault is represented;
(313) if the data of the sensors have constant deviation, the constant deviation of the sensors is invalid;
(32) if the preliminary fault type in the step (2) is an actuator fault, performing specific actuator fault type division, and the steps are as follows:
(321) preprocessing actuator fault data, removing redundant information, and splitting the fault data into fault principal component characteristics;
(322) describing the support degree of the fault characteristics to each fault mode by using the diagnosis evidence, and calculating the confidence coefficient and the reliability factor of diagnosis;
(323) according to the confidence coefficient and the reliability factor obtained in the step (322), the fault characteristic variable x is subjected tokWeight value omega ofkCarrying out optimization and adjustment;
(324) utilizing the fault characteristic variable x obtained in the step (323)kAnd its corresponding weight value omegakAnd merging the diagnostic evidence of the fault characteristics.
Further, in the step (321), a Principal Component Analysis (PCA) method is adopted to preprocess the fault data; reducing the dimension of the data, and obtaining a low-dimensional data set on the premise of keeping the main characteristics of the original data set; the dimension-reduced data characteristic value reflects the characteristic attribute of the source data and has no correlation with each other; the method comprises the following steps:
(3211) containing the original data variable x1,x2,...,xnThe original data set variable is constructed into a linear combination Y ═ Y through a principal component analysis method1,y2,...,yn]TAnd is recorded as:
wherein,
in the formula, x1,x2,...,xnRespectively obtaining data set variables of braking force, vehicle speed, pedal displacement, brake circuit exciting current, brake circuit demagnetizing current, piston connecting rod displacement, brake transmission rod strain, rotating speed and vibration amplitude of each tire and the like of each wheel; y is1Is the maximum square difference among all linear combinations satisfying the formula (1), y1,y2,...,ynAre not correlated with each other, and the variance of each correspondence gradually decreases.
(3212) Normalizing each row of matrix X; let X be ═ X1,x2,...,xn]TFormula (1) is simplified as Y ═ AX, and A is an orthogonal matrix satisfying AAT＝I；
(3213) Solving a covariance matrix; according to principal component analysis rule requirement, y1,y2,...,ynAre not related to each other; i.e. COV (y)i,yj) 0(i ≠ j); the covariance is expressed as:
VAR(Y)＝YYT＝(AX)(AX)T＝AXXTAT＝κ (3)
(3214) protocol solving partyThe eigenvalues of the difference matrix and the corresponding eigenvectors; let R be XXTThen, there are:
ATARAT＝RAT＝ATκ (4)
substituted and simplified to obtain | R-lambdaiI | ═ 0 is a characteristic equation;
(3215) calculating the ith component contribution rate PiComprises the following steps:
the cumulative contribution of the first k principal components is:
the selected principal component contribution rate is accumulated to reach more than 90%; combining the eigenvectors into a matrix form from large to small according to the sizes of the corresponding eigenvalues, selecting the eigenvectors corresponding to the principal component eigenvalues which are ranked in the front and meet the requirement of contribution rate to form an eigenvector matrix, wherein the eigenvector matrix can express most of the characteristics of the data set; taking the first k rows to form a matrix P, wherein Y' is PX which is a new matrix after dimensionality reduction; each set of fault data includes k principal components, x respectively1,x2,...,xk(ii) a The method selects k sensor data from n sensor data through preprocessing.
Further, the method for obtaining confidence and reliability factors of the diagnosis in step (322) comprises the following steps:
(3221) selecting a training sample set of delta groups of known faults; let set of failure modes θ ═ F1,...,Fi,...,FN}，FiIndicating an ith failure mode which is one of a power module failure, a brake pedal module failure, a brake actuator failure and a tire factor failure; y' comprises the preprocessed fault feature vectors obtained in the step (321), and a certain fault mode F is obtained from the fault mode setiW characteristic variables of
Composition set
The values of the characteristic variables are sorted,
respectively represent the maximum value and the minimum value in the w characteristic variable set, have
Thereby obtaining w different value interval subsets
Thus obtaining a characteristic variable change interval with a fault; the characteristic values outside the interval are characteristic variables of which the fault types cannot be determined, so that a corresponding table of all the characteristic variables falling in the fault characteristic interval is obtained;
(3222) counting the number of each characteristic variable falling in each change interval by using different fault modes, thereby obtaining the reliability of the fault mode by utilizing the likelihood function to carry out normalization processing, namely obtaining the probability of the characteristic variable accurately identifying the fault mode, and obtaining the diagnosis evidence
Forming a set of diagnostic evidence
The confidence level represents the feature variable versus the failure mode FiThe reliability matrix is formed by the diagnosis evidence;
(3223) adding delta% or-delta% disturbance to each characteristic variable value, counting the number falling into the original interval again, and counting the number not falling into the original interval as sigmai，σiThe larger the reliability, the lower the reliability.
Further, the fault characteristic variable x is processed in the step (323)kWeight value omega ofkOptimizing for each diagnostic certificateAccording to
Individually making a weight
The method specifically comprises the following steps:
(3231)
has a value range of [0,1 ]]Setting the initial value as 1, combining the diagnostic evidences by using the training sample set generated in the step (3221), solving the Euclidean distance between each diagnostic evidence combination and the actually occurring fault mode, and measuring the similarity between the fusion result and the actually occurring fault;
(3232) traversing the value range by a certain step length d, and selecting the weighted value with the minimum Euclidean distance;
(3233) and obtaining the optimized optimal weight set W.
Further, the fault feature merging of the step (324) specifically includes the following steps:
(3241) making a fault decision criterion according to a fault feature vector fusion result, which specifically comprises the following steps: if it is not
The fusion result points to failure mode Fi(ii) a If it is not
The fusion result is not pointed, that is, the fusion result cannot be effectively judged;
(3242) and (4) obtaining a fault diagnosis result of the brake system based on the fault decision criterion in the step (3241), if the fault modes are mixed up, returning to the step (3211), increasing the number of the characteristic variables, so as to improve the contribution rate of the principal components of the characteristic variables, continuing to perform the step (3), and improving the diagnosis precision.
The invention has the beneficial effects that:
on the basis of a giant magnetostrictive line control brake system, various sensors are used for detecting components of each brake system in the braking process, and then the fault is diagnosed based on sensor data; the device is easy to be integrated with the existing giant magnetostrictive line control brake system, and is beneficial to realization.
The fault diagnosis method adopts a principal component analysis method to preprocess data and remove redundant information; and the diagnosis evidence integrating the confidence coefficient and the reliability factor is adopted for fault diagnosis, so that the fault diagnosis accuracy and the fault diagnosis efficiency are improved.
Drawings
FIG. 1 is a structural diagram of a giant magnetostrictive brake-by-wire system;
FIG. 2 is a block diagram of the wire actuator;
fig. 3 is a diagram of a failure diagnosis apparatus;
FIG. 4 is a flow chart of a fault diagnosis method;
in the figure, 1-left front wheel, 2-left front brake, 3-left front tire detection unit, 4-brake pedal module, 5-POWER supply (POWER), 6-control module (ECU), 7-left rear wheel, 8-left rear tire detection unit, 9-left rear brake, 10-right rear brake, 11-right rear wheel, 12-right rear tire detection unit, 13-Current Regulation Module (CRM), 14-right front tire detection unit, 15-right front tire, 16-right front brake, 17-caliper body, 18-magnetism isolating pad, 19-magnetism isolating top cover, 20-driver shell, 21-transmission rod, 22-piston push rod, 23-left piston, 24-left brake block assembly, 25-brake disc, 26-giant magnetostrictive rod, 27-right brake block assembly, 28-supporting pin, 29-right piston, 30-demagnetizing coil, 31-exciting coil and 32-brake actuator.
Detailed Description
In order to facilitate understanding of those skilled in the art, the present invention will be further described with reference to the following examples and drawings, which are not intended to limit the present invention.
The invention relates to a fault diagnosis device of a giant magnetostrictive brake-by-wire system, which is a disc type brake-by-wire system based on a giant magnetostrictive material, and the system comprises: a brake pedal module 4, a left front brake 2, a right front brake 16, a left rear brake 9, a right rear brake 10, a left front wheel 1, a right front wheel 15, a left rear wheel 7, a right rear wheel 11, a control module (ECU)6, a POWER supply (POWER)5 and a Current Regulation Module (CRM)13, as shown in fig. 1;
the brake pedal module 4 is used for receiving a driver brake signal, feeding back pedal feeling to a driver, and transmitting brake information to the control module 6 according to driver pedal displacement, and comprises: the brake pedal, the pedal bracket, the pedal rotating shaft, the connecting rod, the push rod and the pedal feel simulator;
the input end of the pedal rotating shaft is fixedly connected with the output end of the brake pedal, and the output end of the pedal rotating shaft is fixedly connected with the input end of the connecting rod;
the input end of the push rod is hinged with the output end of the connecting rod;
the lower extreme of footboard sensation simulator is fixed on the footboard support, and it includes: the simulator comprises a push rod, a simulator shell, an outer ring spring, an inner ring spring, an upper end cover, a lower end cover, an adjusting gasket and a base;
the upper end cover and the lower end cover are respectively screwed at the upper end and the lower end of the pedal feeling simulator;
the outer ring spring and the inner ring spring are positioned in the simulator shell, are respectively sleeved on the base and are used for generating simulated pedal force;
the adjusting gasket is positioned between the pedal feel simulator and the pedal bracket and is used for adjusting the pretightening force of the pedal feel simulator;
each wheel is provided with a tire detection unit (a left front tire detection unit 3, a left rear tire detection unit 8, a right rear tire detection unit 12 and a right front tire detection unit 14) which is electrically connected with the control module and used for detecting the tire pressure, the rotating speed and the vibration condition of the tire;
each of the brakes (left front brake 2, right front brake 16, left rear brake 9, right rear brake) includes: a brake assembly, a brake actuator 32 and a force and displacement transfer module;
the brake assembly includes: the brake caliper body 17, the left brake block assembly 24, the left piston 23, the right piston 29, the right brake block assembly 27 and the brake disc 25;
the brake caliper body 17 is fixed on a steering knuckle of a front axle of the vehicle, and a brake driver is arranged on the brake caliper body;
the brake disc 25 is fixed on the wheel hub of the vehicle and extends between the left brake block and the right brake block;
the left brake block back plate and the right brake block back plate are respectively connected with the left piston and the right piston; the left brake block and the right brake block are respectively fixed on a left brake block back plate and a right brake block back plate;
rubber rings are respectively embedded in the ring grooves with the trapezoidal sections on the inner walls of the holes of the left piston 23 and the right piston 29 and are used for returning the brake when braking is finished;
the brake actuator 32 is mounted on the caliper body 17 and is powered by the power supply 5, and includes: the magnetic isolation device comprises a driver shell 20, a super-magnetostrictive rod 26, an excitation coil 31, a demagnetizing coil 30, a magnetic isolation liner 18 and a magnetic isolation top cover 19;
the input end of the giant magnetostrictive rod 26 is fixed at the bottom end inside the driver housing 20;
the excitation coil 31 and the demagnetizing coil 30 are wound on the giant magnetostrictive rod 26;
the magnetic isolation gasket 18 is closely attached to the inner side of the driver shell 20;
the magnetic isolation top cover 19 is screwed on the top end of the brake driver and isolates the influence of a magnetic field on the outside together with the magnetic isolation liner 18;
the force and displacement transmission module is used for transmitting force and displacement output by the brake driver and comprises: a drive rod 21, a piston push rod 22 and a support pin 28; the giant magnetostrictive rod 26 in the brake driver extends to transmit force and displacement to the piston push rod 22 through the transmission rod 21;
the supporting pins 28 are fixed on two sides of the caliper body 17;
the middle part of the transmission rod 21 is hinged on the supporting pin 28, the lower end of the transmission rod 21 is hinged with the input end of the piston push rod 22, and the upper end of the transmission rod 21 is hinged with the output end of the giant magnetostrictive rod 26;
the output end of the piston push rod 22 is fixedly connected with the left piston 23 and the right piston 29 respectively.
The current adjusting module 13 is used for receiving a control current signal output by the control module, regulating and controlling the current, inputting the regulated and controlled current to the brake driver, and feeding the current value of each magnet exciting coil back to the control unit;
in the braking process, when a brake pedal is stepped on, signals collected by a pedal displacement sensor, a vehicle speed sensor, a wheel speed sensor and a braking force sensor are transmitted to each wheel brake driver through a control module, and the intensity of magnetic field on the giant magnetostrictive rod is controlled through the adjustment of the current, so that the elongation of the giant magnetostrictive rod is controlled, the brake clearance is eliminated, and the braking is completed; the brake structure is shown in fig. 2.
Referring to fig. 3, the fault diagnosis apparatus includes: the braking state detection unit, the braking action detection unit, the current detection unit, the braking displacement transmission detection unit, the tire detection unit and the calculation unit;
the braking state detection unit is used for detecting the variation sequence of the braking force and the wheel speed of four wheels (a left front wheel 1, a right front wheel 15, a left rear wheel 7 and a right rear wheel 11) along with time; it includes: a vehicle speed sensor, a wheel speed sensor and a braking force sensor;
the braking state detection unit judges vehicle speed and braking force information acquired by a vehicle speed sensor and a braking force sensor; if the braking force exists, the vehicle is in the braking process, and the pedal displacement sensor data of the braking action detection unit is predicted; and judging the brake failure degree of each wheel according to the wheel speed and the vehicle speed information of each wheel, which are acquired by the wheel speed sensor.
The braking action detection unit is used for detecting the stroke of a vehicle brake pedal;
a current detection unit for detecting a current passing through a circuit in the wheel brake actuator, the current including the exciting coil and the demagnetizing coil;
the current detection unit detects the magnitude of current flowing through the wheel brake driver, namely detects the exciting current and the demagnetizing current, and is used for the calculation unit to judge whether the Current Regulation Module (CRM) has a fault or not.
The braking displacement detection unit is used for detecting the stretching amount of the giant magnetostrictive rod and the displacement amount of the piston connecting rod; the micro-displacement sensor is respectively arranged on the giant magnetostrictive rod and the piston connecting rod; the braking displacement detection unit judges the response effect of the giant magnetostrictive rod according to the expansion amount of the giant magnetostrictive rod so as to distinguish current regulation faults from giant magnetostrictive rod execution response faults and judge whether the clearance between the calipers and the brake disc is too large; and whether the transmission rod has a fault is judged according to the displacement of the piston connecting rod.
The brake displacement transmission detection unit is used for detecting the displacement and the stress loss degree of the transmission rod caused by material rigidity factors in the displacement transmission process; includes a stress strain gauge disposed on the displacement amplification mechanism.
A tire detection unit for detecting a tire rotation and vibration state; comprises a tire pressure detection device and a tire vibration sensor;
the tire detection unit detects the rotation and vibration states of the tire to judge whether the tire has the problems of rotation vibration and inconsistency, and diagnoses the brake failure reasons of tire factors including insufficient air pressure, pattern abrasion and inconsistent color.
And the calculating unit is respectively electrically connected with the braking state detecting unit, the braking action detecting unit, the current detecting unit, the braking displacement transmission detecting unit and the tire detecting unit and is used for determining the fault type when the vehicle braking failure occurs according to the obtained data sent by each unit.
Referring to fig. 4, the fault diagnosis method for the giant magnetostrictive brake-by-wire system according to the present invention includes the following steps based on the above apparatus:
(1) the method comprises the following steps that a brake pedal is stepped on, and signals (including braking force signals of wheels, vehicle speed signals, pedal displacement signals, brake circuit exciting current signals, brake circuit demagnetizing current signals, piston connecting rod displacement, brake transmission rod strain, rotating speed and vibration signals of tires) collected by sensors in a brake state detection unit, a brake action detection unit, a current detection unit, a brake displacement transmission detection unit and a tire detection unit are transmitted to a calculation unit;
(2) the calculating unit detects the sensor data x collected by each detecting unit after the brake failure is detected according to the brake state information collected in the step (1)1,x2,...,xnDetecting and judging the type of the primary fault; the sensor data x1,x2,...,xnRespectively as follows: the braking force of each wheel, the vehicle speed, the pedal displacement, the exciting current of a braking circuit, the demagnetizing current of the braking circuit, the displacement of a piston connecting rod, the strain of a brake transmission rod, the rotating speed of each tire and the vibration amplitude;
the preliminary fault type judging method comprises the following steps: if data loss and data abnormality occur, judging that the sensor is in fault; and if the sensor data is not obviously abnormal, judging that the actuator has a fault.
(3) According to the preliminary fault types obtained by judging in the step (2), specific fault types are divided aiming at different preliminary fault types;
referring to fig. 4, the fault type division method is as follows:
(31) if the preliminary fault type in the step (2) is a sensor fault, specific sensor fault type division is carried out, and the steps are as follows:
(311) if the sensor data has a constant, the sensor fault is indicated to be stuck;
(312) if the signal data with constant gain appears in the long sequence of the sensor data, the fault of gain change is represented;
(313) if the data of the sensors have constant deviation, the constant deviation of the sensors is invalid;
(32) if the preliminary fault type in the step (2) is an actuator fault, performing specific actuator fault type division, and the steps are as follows:
(321) preprocessing actuator fault data, removing redundant information, and splitting the fault data into fault principal component characteristics;
(322) describing the support degree of the fault characteristics to each fault mode by using the diagnosis evidence, and calculating the confidence coefficient and the reliability factor of diagnosis;
(323) according to the confidence coefficient and the reliability factor obtained in the step (322), the fault characteristic variable x is subjected tokWeight value omega ofkCarrying out optimization and adjustment;
(324) utilizing the fault characteristic variable x obtained in the step (323)kAnd its corresponding weight value omegakAnd merging the diagnostic evidence of the fault characteristics.
Preprocessing fault data by adopting a Principal Component Analysis (PCA) in the step (321); reducing the dimension of the data, and obtaining a low-dimensional data set on the premise of keeping the main characteristics of the original data set; the dimension-reduced data characteristic value reflects the characteristic attribute of the source data and has no correlation with each other; the method comprises the following steps:
(3211) containing the original data variable x1,x2,...,xnThe original data set variable is constructed into a linear combination Y ═ Y through a principal component analysis method1,y2,...,yn]TAnd is recorded as:
wherein,
in the formula, x1,x2,...,xnRespectively obtaining data set variables of braking force, vehicle speed, pedal displacement, brake circuit exciting current, brake circuit demagnetizing current, piston connecting rod displacement, brake transmission rod strain, rotating speed and vibration amplitude of each tire and the like of each wheel; y is1Is the maximum square difference among all linear combinations satisfying the formula (1), y1,y2,...,ynAre not correlated with each other, and the variance of each correspondence gradually decreases.
(3212) Go each run of matrix XPerforming line standardization; let X be ═ X1,x2,...,xn]TFormula (1) is simplified as Y ═ AX, and A is an orthogonal matrix satisfying AAT＝I；
(3213) Solving a covariance matrix; according to principal component analysis rule requirements, y1,y2,...,ynAre not related to each other; i.e. COV (y)i,yj) 0(i ≠ j); the covariance is expressed as:
VAR(Y)＝YYT＝(AX)(AX)T＝AXXTAT＝κ (3)
(3214) solving an eigenvalue of the covariance matrix and a corresponding eigenvector; let R be XXTThen, there are:
ATARAT＝RAT＝ATκ (4)
substituted and simplified to obtain | R-lambdaiI | ═ 0 is a characteristic equation;
(3215) calculating the ith component contribution rate PiComprises the following steps:
the cumulative contribution of the first k principal components is:
the selected principal component contribution rate is accumulated to reach more than 90%; combining the eigenvectors into a matrix form from large to small according to the sizes of the corresponding eigenvalues, selecting the eigenvectors corresponding to the principal component eigenvalues which are ranked in the front and meet the requirement of contribution rate to form an eigenvector matrix, wherein the eigenvector matrix can express most of the characteristics of the data set; taking the first k rows to form a matrix P, wherein Y' is PX which is a new matrix after dimensionality reduction; each set of fault data includes k principal components, x respectively1,x2,...,xk(ii) a The method selects k sensor data from n sensor data through preprocessing.
The method for obtaining the reliability and reliability factor of the diagnosis in step (322) comprises the following steps:
(3221) selecting a training sample set of delta groups of known faults; let failure mode set θ ═ F1,...,Fi,...,FN}，FiIndicating an ith failure mode which is one of a power module failure, a brake pedal module failure, a brake actuator failure and a tire factor failure; y' comprises the preprocessed fault feature vectors obtained in the step (321), and a certain fault mode F is obtained from the fault mode setiW characteristic variables of
Composition set
The values of the characteristic variables are sorted,
respectively represent the maximum value and the minimum value in the w characteristic variable set, have
Thereby obtaining w different value interval subsets
Thus obtaining a characteristic variable change interval with a fault; the characteristic values outside the interval are characteristic variables of which the fault types cannot be determined, so that a corresponding table of all the characteristic variables falling in the fault characteristic interval is obtained;
(3222) counting the number of each characteristic variable falling in each change interval by using different fault modes, thereby obtaining the reliability of the fault mode by utilizing the likelihood function to carry out normalization processing, namely obtaining the probability of the characteristic variable accurately identifying the fault mode, and obtaining the diagnosis evidence
Forming a set of diagnostic evidence
The confidence level represents the feature variable versus the failure mode FiThe reliability matrix is formed by the diagnosis evidence;
(3223) adding delta% or-delta% disturbance to each characteristic variable value, counting the number falling into the original interval again, and counting the number not falling into the original interval as sigmai，σiThe larger the reliability, the lower the reliability.
In the step (323), a fault characteristic variable x is subjected tokWeight value omega ofkOptimizing for each diagnostic evidence
Individually making a weight
The method specifically comprises the following steps:
(3231)
has a value range of [0,1 ]]Setting the initial value as 1, combining the diagnostic evidences by using the training sample set generated in the step (3221), solving the Euclidean distance between each diagnostic evidence combination and the actually occurring fault mode, and measuring the similarity between the fusion result and the actually occurring fault;
(3232) traversing the value range by a certain step length d, and selecting the weighted value with the minimum Euclidean distance;
(3233) and obtaining the optimized optimal weight set W.
The fault feature merging of the step (324) specifically includes the following steps:
(3241) making a fault decision criterion according to a fault feature vector fusion result, which specifically comprises the following steps: if it is not
The fusion result points to failure mode Fi(ii) a If it is not
The fusion result is not pointed, that is, the fusion result cannot be effectively judged;
(3242) and (4) obtaining a fault diagnosis result of the brake system based on the fault decision criterion in the step (3241), if confusion among fault modes exists, returning to the step (3211), increasing the number of the characteristic variables so as to improve the principal component contribution rate of the characteristic variables, continuing to perform the step (3), and improving the diagnosis precision.
(4) And (4) obtaining a fault diagnosis result according to the specific fault type obtained in the step (3).
While the invention has been described in terms of its preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.
Claims (5)
1. A failure diagnosis device for a giant magnetostrictive brake-by-wire system is characterized by comprising: the braking state detection unit, the braking action detection unit, the current detection unit, the braking displacement transmission detection unit, the tire detection unit and the calculation unit;
the braking state detection unit is used for detecting the variation sequence of the braking force of the four wheels and the wheel speed along with time;
the braking action detection unit is used for detecting the stroke of a vehicle brake pedal;
a current detection unit for detecting a current passing through a circuit in the wheel brake actuator;
the brake displacement detection unit is used for detecting the expansion amount of the giant magnetostrictive rod and the displacement amount of the piston connecting rod;
the brake displacement transmission detection unit is used for detecting the displacement and the stress loss degree of the transmission rod in the displacement transmission process;
a tire detection unit for detecting a tire rotation and vibration state;
the calculating unit is respectively electrically connected with the braking state detecting unit, the braking action detecting unit, the current detecting unit, the braking displacement transmission detecting unit and the tire detecting unit and is used for determining the fault type when the vehicle braking fails according to the obtained data sent by each unit;
the current detection unit is used for detecting the sizes of the exciting current and the demagnetizing current which are circulated by the wheel brake driver and used for judging whether the current regulation module has a fault or not by the calculation unit;
the braking displacement detection unit judges the response effect of the giant magnetostrictive rod according to the expansion amount of the giant magnetostrictive rod so as to distinguish a current regulation fault from a giant magnetostrictive rod execution response fault and judge whether the clearance between the caliper and the brake disc is too large; judging whether the transmission rod has a fault according to the displacement of the piston connecting rod;
the braking state detection unit judges the vehicle speed and braking force information acquired by the vehicle speed sensor and the braking force sensor; if the braking force exists, the vehicle is in the braking process, and the pedal displacement sensor data of the braking action detection unit is predicted; judging the brake failure degree of each wheel according to the wheel speed and the vehicle speed information of each wheel, which are acquired by each wheel speed sensor;
the tire detection unit judges whether the tire has the problems of rotation vibration and inconsistency by detecting the rotation and vibration states of the tire, and diagnoses the brake failure reasons of tire factors including insufficient air pressure, pattern abrasion and inconsistent color.
2. A fault diagnosis method for a giant magnetostrictive brake-by-wire system is based on the device of claim 1, and is characterized by comprising the following steps:
(1) the method comprises the following steps of stepping down a brake pedal, and transmitting signals collected by sensors in a brake state detection unit, a brake action detection unit, a current detection unit, a brake displacement transmission detection unit and a tire detection unit to a calculation unit;
(2) the calculating unit detects the sensor data x collected by each detecting unit after the brake failure is detected according to the brake state information collected in the step (1)1,x2,...,xnDetecting and judging the type of the primary fault;
(3) according to the preliminary fault types obtained by judging in the step (2), specific fault types are divided aiming at different preliminary fault types;
(4) and (4) obtaining a fault diagnosis result according to the specific fault type obtained in the step (3).
3. The method for diagnosing faults of a giant magnetostrictive brake-by-wire system according to claim 2, characterized in that the sensor data x in the step (2)1,x2,...,xnRespectively as follows: the braking force of each wheel, the vehicle speed, the pedal displacement, the exciting current of a braking circuit, the demagnetizing current of the braking circuit, the displacement of a piston connecting rod, the strain of a brake transmission rod, the rotating speed of each tire and the vibration amplitude.
4. The fault diagnosis method for the giant magnetostrictive brake-by-wire system according to claim 2, wherein the preliminary fault type judgment method in the step (2) is as follows: if data loss and data abnormality occur, judging that the sensor is in fault; and if the sensor data is not obviously abnormal, judging that the actuator has a fault.
5. The giant magnetostrictive brake-by-wire system fault diagnosis method according to claim 2, wherein the specific fault type division method in the step (3) is as follows:
(31) if the preliminary fault type in the step (2) is a sensor fault, specific sensor fault type division is carried out, and the steps are as follows:
(311) if the sensor data has a constant, indicating that the sensor fault is stuck;
(312) if the signal data with constant gain appears in the sensor data long sequence, indicating that a gain change fault occurs;
(313) if the data of the sensors have constant deviation, indicating that the constant deviation of the sensors fails;
(32) if the preliminary fault type in the step (2) is an actuator fault, performing specific actuator fault type division, and the steps are as follows:
(321) preprocessing actuator fault data, removing redundant information, and splitting the fault data into fault principal component characteristics;
(322) describing the support degree of the fault characteristics to each fault mode by using the diagnosis evidence, and calculating the confidence coefficient and the reliability factor of diagnosis;
(323) according to the confidence coefficient and the reliability factor obtained in the step (322), the fault characteristic variable x is subjected tokWeight value omega ofkCarrying out optimization and adjustment;
(324) utilizing the fault characteristic variable x obtained in the step (323)kAnd its corresponding weight value omegakAnd merging the diagnostic evidence of the fault characteristics.
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