Suspension system sensor fault diagnosis and signal redundancy fault tolerance method and system

Abstract

The invention relates to the technical field of suspension system detection, and discloses a suspension system sensor fault diagnosis and signal redundancy fault tolerance method and system, and the method comprises the steps: firstly collecting an unsprung acceleration signal and a vehicle speed signal, and obtaining an unsprung acceleration power spectrum through preprocessing and power spectrum estimation; knee point frequency is determined through piecewise fitting, then speed normalization roll-off frequency is calculated, final roll-off frequency is determined, piecewise power law pavement displacement power spectrum priori is established in combination with a vehicle dynamics model transfer function, and an expected output power spectrum is obtained. And dividing an analysis frequency band based on the roll-off frequency, calculating a single-channel normalized energy statistic, setting a fault threshold according to statistical distribution and a significance level, and judging whether the sensor fails or not. If the fault occurs, performing frequency domain regularization inversion by taking power spectrum prior as constraint to obtain pavement displacement power spectrum estimation and generate target channel virtual output; and finally, according to the difference between the statistical magnitude and the fault threshold, outputting a fault-tolerant result.

Description

Technical Field

[0001] This invention relates to the field of suspension system testing technology, and more specifically, to a method and system for fault diagnosis and signal redundancy tolerance of suspension system sensors. Background Technology

[0002] In vehicle dynamics, the excitation of the road surface on the suspension is often expressed in the form of power spectral density. Although standards provide statistical definitions for road surface roughness levels, the spatial structure of real roads often exhibits multi-scale characteristics. Factors such as paving materials, maximum aggregate particle size, and repair block length in different road sections directly alter the energy distribution of the road surface spatial spectrum. When a vehicle travels at different speeds, the road surface spatial roughness is converted into time-frequency excitation, and its energy peak position changes proportionally with speed. This phenomenon, where geometric and kinematic parameters work together, causes the same suspension model to exhibit completely different output patterns at different speeds or on different road sections.

[0003] Traditional suspension sensor fault diagnosis algorithms are mostly based on the Gaussian assumption, which assumes that the statistical properties of road excitation remain consistent over time. However, in reality, changes in the principal scale of road geometry render this assumption invalid. When a vehicle enters a road section with different materials or repaired structures, the frequency domain center of gravity of the suspension acceleration and displacement signals shifts, causing the residuals or energy criteria based on the standard power distribution to become invalid. As a result, the diagnostic system cannot distinguish between environmental changes and sensor malfunctions, leading to frequent false alarms or missed detections.

[0004] As vehicle speed increases, the frequency of tire-road interaction shifts upwards; conversely, on coarser pavement, the proportion of low-frequency energy increases. The combined effect of these factors causes the suspension system's vibration input to exhibit a dynamic spectrum that varies with speed and geometric scale. If the diagnostic model still evaluates sensor functionality using fixed parameters, a systematic deviation arises between statistical expectations and measured signals. This deviation manifests as energy distribution misalignment in the frequency domain and amplitude drift in the time domain. Furthermore, road repairs and discontinuities in local structures introduce significant energy concentration points. This structural excitation can cause individual sensor outputs to deviate from model predictions, but does not necessarily indicate component failure. Existing algorithms cannot identify these pattern differences caused by environmental geometric variations; therefore, the stability and repeatability of the diagnostic system significantly decrease when facing dynamic driving conditions across road sections and speeds. Summary of the Invention

[0005] This invention provides a method and system for sensor fault diagnosis and signal redundancy tolerance in a suspension system, which solves the technical problem mentioned in the background art: how to keep the sensor fault diagnosis and signal redundancy processing of the suspension system reliable and comparable under the condition of frequent changes in driving conditions and road surface spatial scale, so as to avoid systematic misjudgment caused by changes in environmental scale.

[0006] Firstly, a method for sensor fault diagnosis and signal redundancy tolerance in a suspension system includes:

[0007] Acquire driving signals, including unsprung acceleration signals and vehicle speed signals; preprocess the driving signals and estimate their power spectrum to obtain the unsprung acceleration power spectrum;

[0008] The knee frequency was determined in the unsprung acceleration power spectrum by piecewise fitting, and the speed-normalized roll-off frequency was calculated based on the knee frequency and the vehicle speed signal.

[0009] The roll-off frequency is determined based on the speed-normalized roll-off frequency. A piecewise power-law road displacement power spectrum prior is established with the roll-off frequency as the boundary. The power spectrum prior is combined with the vehicle dynamics model transfer function to obtain the desired output power spectrum.

[0010] The analysis frequency band is determined based on the roll-off frequency. Within the analysis frequency band, the statistics of the normalized energy of a single channel are calculated. The fault threshold is determined based on the statistical distribution and significance level. When the statistics exceed the fault threshold, the sensor is determined to be in a fault state.

[0011] When the fault state is determined, frequency domain regularization inversion is performed with power spectrum prior as constraint to obtain the road displacement power spectrum estimate, and the virtual output of the target channel is generated based on the road displacement power spectrum estimate.

[0012] Based on the difference between the statistics and the fault threshold, a weighted soft substitution is applied to the measured output and the virtual output of the target channel to output the fault-tolerant result.

[0013] Secondly, a suspension system sensor fault diagnosis and signal redundancy fault tolerance system, applied in any of the suspension system sensor fault diagnosis and signal redundancy fault tolerance methods described above, includes:

[0014] The data acquisition module collects driving signals, including unsprung acceleration signals and vehicle speed signals; it preprocesses and estimates the power spectrum of the driving signals to obtain the unsprung acceleration power spectrum.

[0015] The data processing module determines the knee frequency in the unsprung acceleration power spectrum through piecewise fitting, and calculates the speed-normalized roll-off frequency based on the knee frequency and the vehicle speed signal.

[0016] The desired output power spectrum module determines the roll-off frequency based on the speed-normalized roll-off frequency, establishes a piecewise power-law road displacement power spectrum prior with the roll-off frequency as the boundary, and combines the power spectrum prior with the vehicle dynamics model transfer function to obtain the desired output power spectrum.

[0017] The fault determination module determines the analysis frequency band based on the roll-off frequency, calculates the statistics of the normalized energy of a single channel within the analysis frequency band, determines the fault threshold based on the statistical distribution and significance level, and determines the sensor as faulty when the statistics exceed the fault threshold.

[0018] When a fault condition is determined, the virtual output generation module performs frequency domain regularization inversion with power spectrum prior as a constraint to obtain the road displacement power spectrum estimate, and generates the virtual output of the target channel based on the road displacement power spectrum estimate.

[0019] The fault tolerance calculation module performs weighted soft substitution on the measured output and virtual output of the target channel based on the difference between the statistics and the fault threshold, and outputs the fault tolerance result.

[0020] The beneficial effects of this invention include: by introducing a speed-normalized roll-off frequency and a piecewise power-law road displacement power spectrum prior model, adaptive fault diagnosis and signal redundancy tolerance of the suspension system under different vehicle speeds and road conditions are realized, which can accurately distinguish between environmental changes and sensor failures, and significantly reduce false alarms and missed detection rates; when a sensor fails, a virtual output is generated through frequency domain regularization inversion and weighted fusion with the measured signal to achieve smooth fault-tolerant output, thereby improving the stability and reliability of the system and effectively ensuring the safety and performance consistency of the vehicle suspension system under complex working conditions. Attached Figure Description

[0021] Figure 1 This is a flowchart of the present invention;

[0022] Figure 2 This is a block diagram of the present invention. Detailed Implementation

[0023] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0024] Example 1: As Figure 1 As shown, a method for sensor fault diagnosis and signal redundancy tolerance in a suspension system includes:

[0025] Acquire driving signals, including unsprung acceleration signals and vehicle speed signals; preprocess the driving signals and estimate their power spectrum to obtain the unsprung acceleration power spectrum;

[0026] The knee frequency was determined in the unsprung acceleration power spectrum by piecewise fitting, and the speed-normalized roll-off frequency was calculated based on the knee frequency and the vehicle speed signal.

[0027] The roll-off frequency is determined based on the speed-normalized roll-off frequency. A piecewise power-law road displacement power spectrum prior is established with the roll-off frequency as the boundary. The power spectrum prior is combined with the vehicle dynamics model transfer function to obtain the desired output power spectrum.

[0028] The analysis frequency band is determined based on the roll-off frequency. Within the analysis frequency band, the statistics of the normalized energy of a single channel are calculated. The fault threshold is determined based on the statistical distribution and significance level. When the statistics exceed the fault threshold, the sensor is determined to be in a fault state.

[0029] When the fault state is determined, frequency domain regularization inversion is performed with power spectrum prior as constraint to obtain the road displacement power spectrum estimate, and the virtual output of the target channel is generated based on the road displacement power spectrum estimate.

[0030] Based on the difference between the statistics and the fault threshold, a weighted soft substitution is applied to the measured output and the virtual output of the target channel to output the fault-tolerant result.

[0031] In one embodiment of the present invention, the driving signal is preprocessed and its power spectrum is estimated to obtain the unsprung acceleration power spectrum, including:

[0032] With a fixed sampling frequency Acquire discrete signals of unsprung acceleration at discrete times. The value is Unsprung acceleration discrete signals include Discrete moments;

[0033] Calculate the full-length mean of the unsprung acceleration discrete signal. ;in, for Index;

[0034] Subtracting the mean value from the discrete signal of unsprung acceleration along the entire length yields the sub-mean signal. ;

[0035] The mean-removing signal eliminates the DC component (constant offset) in the signal, thus avoiding interference from the DC component in the energy estimation of the low-frequency region of the power spectrum.

[0036] Set the number of discrete points to The sliding step of the Hanning window is... ;

[0037] The Hanning window is set to ensure a smooth transition between the two ends of the signal segment and to suppress spectral leakage (i.e., spurious diffusion of frequency domain energy) caused by signal truncation during Discrete Fourier Transform.

[0038] Setting a sliding step size is to achieve reasonable overlap when segmenting the signal, and to improve the computational efficiency and statistical stability of balanced spectrum estimation.

[0039] Segmenting the mean-removed signal , No. The starting sample position of the segment mean-removed signal is :

[0040] ;

[0041] For the Applying a Hanning window to the mean signal of segment 1, the first segment The first segment of the mean signal The windowing signal for the mean signal is :

[0042] ;in, for Index;

[0043] Applying a Hanning window enhances the smoothness of the endpoints of a single signal segment, completely eliminating spectral distortion caused by truncation of a single signal segment;

[0044] Perform a Discrete Fourier Transform on the windowed signal to obtain the transform result. :

[0045] ;in, For frequency point index, the first The physical frequency corresponding to each frequency point , The value ranges from 1 to , The imaginary unit, is the base of the natural exponential function. This represents the q-th sample value of the discrete-time signal after the p-th segment has been processed with a Hanning window;

[0046] The acceleration signal in the time domain is converted into a complex spectrum in the frequency domain, and a mapping relationship between the signal from time-amplitude to frequency-amplitude is established.

[0047] Calculate the window energy normalization coefficient ;

[0048] The window energy normalization coefficient is calculated to correct the effect of the Hanning window on the signal energy. That is, if the energy of the Hanning window is not 1, it will change the original signal energy. The window energy normalization coefficient can compensate for this energy deviation.

[0049] Based on the transformation result and the window energy normalization coefficient, calculate the first... The signal segment in the first Single-segment power spectrum at each frequency point , express The modulus;

[0050] The complex spectrum in the frequency domain is transformed into power spectrum data that can directly characterize energy. That is, by taking the square of the magnitude of the complex spectrum and combining it with the normalization coefficient, the energy density distribution of each signal segment at different frequency points is obtained.

[0051] The unsprung acceleration power spectrum is obtained by summing and averaging the power spectra of each segment of the mean-de-mean signal. ; This indicates the number of segments in the mean-removed signal.

[0052] The Welch power spectrum estimation method is used to reduce the statistical variance (random error) of a single power spectrum segment, improve the stability and reliability of the spectrum estimation results, and finally obtain a power spectrum that can accurately reflect the frequency domain energy characteristics of the unsprung acceleration signal.

[0053] In one embodiment of the present invention, the knee frequency is determined by piecewise fitting in the unsprung acceleration power spectrum, and the speed-normalized roll-off frequency is calculated based on the knee frequency and the vehicle speed signal, including:

[0054] Perform a natural logarithmic transformation on the physical frequency and the corresponding power spectrum value at each frequency point to obtain the th... Logarithmic frequency at each frequency point With logarithmic power ;in, It is the natural logarithm function;

[0055] The power spectrum of unsprung acceleration often exhibits a power-law distribution in different frequency ranges (frequency and power have a non-linear exponential relationship), and logarithmic transformation can convert this non-linear relationship into a linear one;

[0056] Selecting candidate boundary frequency points , The value ranges from 1 to ;

[0057] The knee frequency is the boundary point where the power spectrum changes from one pattern (fractal power law segment) to another (roll-off segment), and its position cannot be directly predicted. Traversing all frequency points from 1 to 2 as candidates can ensure that the true knee position is not missed.

[0058] For candidate boundary frequency points Establish the left-segment linear model and the right-segment linear model:

[0059] The left-side linear model is ;

[0060] The right-hand linear model is ;

[0061] in, The intercept of the left-side linear model. The slope of the left segment of the linear model. The intercept of the right-hand linear model. The slope of the right segment of the linear model;

[0062] The power spectrum exhibits different characteristics on both sides of the knee point (e.g., the power on the left decreases slowly with increasing frequency, while the power on the right decreases more rapidly). Two linear models are used to describe the frequency-power relationship on both sides, which can match the actual spectral shape of each segment and avoid the overall deviation caused by fitting two spectral lines with different characteristics using a single model.

[0063] Calculate the sum of squares of the left segment residuals :

[0064] ;

[0065] Sum of squared residuals on the right segment :

[0066] ;

[0067] Will and The sum of these values ​​is used as the total residual;

[0068] The sum of squared residuals is an index that quantifies the deviation between the model's predicted value and the actual power spectrum value. The total residual (the sum of the sums of squared residuals of the left and right segments) can comprehensively reflect the fitting accuracy of the model on both sides of the candidate point to the overall spectrum shape.

[0069] The candidate boundary frequency point that will cause the total residual to be minimized As knee frequency ;

[0070] Following the logic of minimizing fitting error, when the total residual is minimized, the models on both sides of the candidate point can fit the actual change law of the power spectrum to the greatest extent, ensuring that the knee frequency is the true and accurate boundary between the two characteristic transitions;

[0071] Get the Vehicle speed signal at discrete time points ;

[0072] The average vehicle speed is the average value of all discrete-time vehicle speed signals. ;

[0073] Instantaneous vehicle speed fluctuates due to driving operations (such as acceleration and deceleration), and direct use of this information can easily lead to unstable results. Taking the average of vehicle speeds at all discrete moments can eliminate the influence of instantaneous fluctuations and obtain the representative vehicle speed for the road segment.

[0074] The ratio of knee frequency to average vehicle speed is used as the speed-normalized roll-off frequency. .

[0075] Knee frequency is affected by vehicle speed (on the same road surface, the knee frequency changes proportionally with different vehicle speeds). Comparing it with the average vehicle speed can eliminate the interference of this exogenous variable, vehicle speed, and obtain a pure environmental characteristic quantity determined only by road surface geometry (such as aggregate particle size and repair block size). This characteristic quantity is comparable across different vehicle speeds and road sections.

[0076] In one embodiment of the present invention, the roll-off frequency is determined based on the speed-normalized roll-off frequency, a piecewise power-law road displacement power spectrum prior is established with the roll-off frequency as the boundary, and the power spectrum prior is combined with the vehicle dynamics model transfer function to obtain the desired output power spectrum, including:

[0077] Based on velocity-normalized roll-off frequency With average vehicle speed Calculate the roll-off frequency ;

[0078] Speed-normalized roll-off frequency is a pure road surface geometric feature quantity stripped of the influence of vehicle speed. When combined with the average vehicle speed, this pure environmental feature can be transformed into a specific boundary frequency (roll-off frequency) of the power spectrum in the time domain at the current vehicle speed.

[0079] Constructing a priori pavement displacement power spectrum in piecewise power law form Specifically:

[0080] When physical frequency satisfy hour, ;

[0081] When physical frequency satisfy hour, ;

[0082] in, This is the relative amplitude calibration constant. As the reference frequency, It is a self-affine power exponent. The additional decay exponent after roll-off and ;

[0083] The spatial spectrum of actual road surfaces follows different variation patterns at different frequency bands (corresponding to different scales of road surface characteristics). That is, the power attenuation characteristics differ between the low-frequency band (large-scale road surfaces, such as repair blocks) and the high-frequency band (small-scale road surfaces, such as aggregate texture). The piecewise power-law model can fit this actual characteristic, and the additional attenuation exponent can reflect the measured law of faster power attenuation in the high-frequency band. The relative amplitude calibration constant and the reference frequency can calibrate the amplitude level of the spectrum to match the specific road surface grade (such as ISO 8608 road surface classification), ensuring that the prior spectrum can truly reflect the frequency domain energy distribution of the actual road surface.

[0084] The vehicle dynamics model transfer function from road surface displacement to unsprung acceleration is defined as follows:

[0085] ;

[0086] in, For the Laplace complex frequency, For the sprung mass, For unsprung mass, This is the suspension damping coefficient. This is the suspension stiffness coefficient. This refers to the tire stiffness coefficient.

[0087] The purpose of the transfer function is to establish the frequency domain correlation between the road displacement input and the unsprung acceleration output. In other words, it quantifies the dynamic response characteristics of the suspension system (including parameters such as sprung mass, unsprung mass, damping, and stiffness) to road excitations at different frequencies, clarifying how road displacement excitation is converted into an unsprung acceleration signal through the vibration transmission of suspension components. The introduction of the Laplace complex frequency characterizes the amplitude-frequency and phase-frequency characteristics of the system at different frequencies, avoiding the difficulty of quantifying dynamic transfer relationships in time-domain analysis.

[0088] By combining the transfer function with the prior knowledge of the road displacement power spectrum, the expected output power spectrum of unsprung acceleration is calculated. ;in, For the transfer function in The modulus at that point.

[0089] The prior of the road displacement power spectrum is the input energy distribution, and the square of the magnitude of the transfer function represents the system's efficiency in transferring energy at different frequencies. Combining the two, we can obtain the energy distribution (desired output power spectrum) that should exist under normal operating conditions for unsprung acceleration.

[0090] In one embodiment of the present invention, a fault threshold is determined based on statistical distribution and significance level. When the statistic exceeds the fault threshold, the sensor is determined to be in a fault state, including:

[0091] Calculate the degrees of freedom of the chi-square distribution. ;in, For the set of frequency points Number of mid-frequency points;

[0092] Calculate the fault threshold ;in, Describing the degrees of freedom as The quantile function of the chi-square distribution, Indicates the pre-set significance level. ;

[0093] If the statistics of single-channel normalized energy > Fault Threshold If so, the sensor is determined to be in a faulty state.

[0094] The roll-off frequency is the boundary where the road surface spectrum transitions from the fractal power-law segment to the roll-off segment. By setting upper and lower limits around this frequency using preset coefficients, the analysis frequency band can be accurately covered to ensure that the most significant road surface characteristics are captured. This approach includes the roll-off transition region to capture core environmental information while avoiding the inclusion of low-frequency interference (such as vehicle body steady-state vibration) or high-frequency noise (such as electronic interference).

[0095] By selecting frequency points that fall within the analysis band and forming a dedicated calculation dataset, interference from irrelevant frequency points outside the band can be eliminated. This allows statistical calculations to focus only on frequency components that are strongly correlated with the road environment and sensor response, reducing the dilution of statistical results by invalid data and ensuring that the statistics can truly reflect the sensor's output deviation in the core frequency band.

[0096] The inclusion of a minimum value prevents the denominator from approaching zero and causing abnormal distortion of the statistics when the expected output power spectrum value is too small. Normalization by dividing the measured energy by the expected energy eliminates the influence of differences in the absolute energy value under different road surfaces (such as roughness) and different vehicle speeds, allowing the statistics to quantify only the degree of deviation between the measured output and the normal expectation. The deviation is small under normal operating conditions, but significant when the sensor malfunctions.

[0097] In one embodiment of the present invention, when a fault state is determined, frequency domain regularization inversion is performed with power spectrum prior as a constraint to obtain a road surface displacement power spectrum estimate. Based on the road surface displacement power spectrum estimate, a virtual output of the target channel is generated, including:

[0098] Calculate the first Discrete spectrum estimation of road displacement at each frequency point :

[0099] ;in, Indicates the transfer function in The complex conjugate at the location, For the prior of the road displacement power spectrum at physical frequency The value of , Represents the regularization coefficient;

[0100] Calculate the first Road displacement power spectrum estimation at each frequency point :

[0101] ;in, Represents the discrete spectrum estimation of road displacement The modulus;

[0102] Calculate the first Virtual output of target channel frequency domain at each frequency point :

[0103] ;in, The transfer function from road surface displacement to the target channel :

[0104] .

[0105] The complex conjugate of the transfer function is used to establish the inverse mapping from the unsprung acceleration output to the road displacement input, ensuring that the inversion direction conforms to the dynamic logic of the system. The prior of the road displacement power spectrum serves as a constraint, enabling the inversion results to conform to the frequency domain energy distribution law of the actual road surface (avoiding the inversion results from deviating from the real road surface characteristics). The regularization coefficient can suppress the divergence of the inversion results caused by noise or measurement errors (such as abnormal increase in high-frequency values), thereby obtaining a road displacement spectrum that takes into account both the real road surface characteristics and numerical stability.

[0106] The discrete spectrum of road displacement is in complex form. The square of its modulus can transform the spectrum information in the complex domain into a power spectrum that can directly characterize the energy density of road displacement at different frequencies. The purpose is to clarify the excitation intensity distribution of the road surface in each frequency band, provide quantified road excitation input data for the subsequent generation of the target channel virtual output, and ensure that the virtual output can match the actual road excitation level.

[0107] The transfer function from road surface displacement to the target channel quantifies the dynamic characteristics of road excitation transmitted through the suspension system to the target channel (such as the vehicle acceleration channel) (transmission efficiency of excitation at different frequencies). By combining this transfer function with the road surface displacement spectrum, the frequency domain signal that the target channel should output under normal road surface excitation can be simulated. The purpose is to provide a virtual substitute signal that matches the actual working conditions (road surface excitation, system characteristics) for the faulty channel.

[0108] In one embodiment of the present invention, a weighted soft substitution is performed on the measured output and virtual output of the target channel based on the difference between the statistical quantity and the fault threshold, and the fault-tolerant result is output, including:

[0109] Calculate the proportion of the difference between the statistic and the fault threshold. ;in, It is a positive part function. ;

[0110] Calculate the weighting coefficients of the target channel ;in, This represents a coefficient that adjusts the intensity of substitution.

[0111] Determine the measured discrete spectrum of the target channel With virtual discrete spectrum ;

[0112] Calculate the target channel at physical frequency Fusion discrete spectrum :

[0113] ;in, For the target channel at physical frequency The fused spectrum value;

[0114] Total number of preset frequency points Calculate the target channel in the first... Time-domain fault-tolerant output at discrete moments .

[0115] The positive part function only retains the portion of the statistic that exceeds the fault threshold (the difference ratio is 0 when it does not exceed the threshold). The purpose is to quantify the severity of the sensor fault, that is, the more the statistic exceeds the threshold, the greater the difference ratio.

[0116] Adjusting the substitution strength coefficient can control the intervention level of the virtual output. When the difference ratio is large (severe fault), the weighting coefficient is small and the proportion of virtual output increases; when the difference ratio is small (minor fault), the weighting coefficient is large and the proportion of measured output increases. The purpose is to dynamically allocate the weights of measured and virtual signals according to the severity of the fault, avoid hard substitution that causes signal abrupt changes, and improve fault tolerance smoothness.

[0117] Although the measured spectrum contains faults, it may retain some valid information in the frequency bands. The virtual spectrum is generated based on road surface excitation and system characteristics (high reliability).

[0118] The purpose of combining the measured and virtual spectra by weight is to use virtual data to correct faults and anomalies in the measured spectrum, while retaining the effective components in the measured spectrum that are not affected by faults, so as to obtain a fused signal in the frequency domain that both conforms to the actual working conditions and eliminates fault interference.

[0119] The purpose of converting the frequency domain fusion signal into a time domain signal is to output time domain data that can be directly adapted to actual application scenarios such as suspension system control and monitoring (the system usually relies on time domain signals to work), thus completing the conversion from frequency domain fusion to a practically usable fault-tolerant signal and realizing the final implementation of the fault-tolerant function.

[0120] Example 2: Figure 2As shown, a suspension system sensor fault diagnosis and signal redundancy fault tolerance system, applied in any of the suspension system sensor fault diagnosis and signal redundancy fault tolerance methods described herein, includes:

[0121] The data acquisition module collects driving signals, including unsprung acceleration signals and vehicle speed signals; it preprocesses and estimates the power spectrum of the driving signals to obtain the unsprung acceleration power spectrum.

[0122] The data processing module determines the knee frequency in the unsprung acceleration power spectrum through piecewise fitting, and calculates the speed-normalized roll-off frequency based on the knee frequency and the vehicle speed signal.

[0123] The desired output power spectrum module determines the roll-off frequency based on the speed-normalized roll-off frequency, establishes a piecewise power-law road displacement power spectrum prior with the roll-off frequency as the boundary, and combines the power spectrum prior with the vehicle dynamics model transfer function to obtain the desired output power spectrum.

[0124] The fault determination module determines the analysis frequency band based on the roll-off frequency, calculates the statistics of the normalized energy of a single channel within the analysis frequency band, determines the fault threshold based on the statistical distribution and significance level, and determines the sensor as faulty when the statistics exceed the fault threshold.

[0125] When a fault condition is determined, the virtual output generation module performs frequency domain regularization inversion with power spectrum prior as a constraint to obtain the road displacement power spectrum estimate, and generates the virtual output of the target channel based on the road displacement power spectrum estimate.

[0126] The fault tolerance calculation module performs weighted soft substitution on the measured output and virtual output of the target channel based on the difference between the statistics and the fault threshold, and outputs the fault tolerance result.

[0127] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A method for sensor fault diagnosis and signal redundancy fault tolerance in a suspension system, characterized in that, include: Acquire driving signals, including unsprung acceleration signals and vehicle speed signals; preprocess the driving signals and estimate their power spectrum to obtain the unsprung acceleration power spectrum; The knee frequency was determined in the unsprung acceleration power spectrum by piecewise fitting, and the speed-normalized roll-off frequency was calculated based on the knee frequency and the vehicle speed signal. The roll-off frequency is determined based on the speed-normalized roll-off frequency. A piecewise power-law road displacement power spectrum prior is established with the roll-off frequency as the boundary. The power spectrum prior is combined with the vehicle dynamics model transfer function to obtain the desired output power spectrum. The analysis frequency band is determined based on the roll-off frequency. Within the analysis frequency band, the statistics of the normalized energy of a single channel are calculated. The fault threshold is determined based on the statistical distribution and significance level. When the statistics exceed the fault threshold, the sensor is determined to be in a fault state. When the fault state is determined, frequency domain regularization inversion is performed with power spectrum prior as constraint to obtain the road displacement power spectrum estimate, and the virtual output of the target channel is generated based on the road displacement power spectrum estimate. Based on the difference between the statistics and the fault threshold, a weighted soft substitution is applied to the measured output and the virtual output of the target channel to output the fault-tolerant result.

2. The method for sensor fault diagnosis and signal redundancy tolerance in a suspension system according to claim 1, characterized in that, The driving signal is preprocessed and its power spectrum is estimated to obtain the unsprung acceleration power spectrum, including: With a fixed sampling frequency Acquire discrete signals of unsprung acceleration at discrete times. The value is Unsprung acceleration discrete signals include Discrete moments; Calculate the full-length mean of the unsprung acceleration discrete signal. ;in, for Index; Subtracting the mean value from the discrete signal of unsprung acceleration along the entire length yields the sub-mean signal. ; Set the number of discrete points to The sliding step of the Hanning window is... ; Segmenting the mean-removed signal , No. The starting sample position of the segment mean-removed signal is : ； For the Applying a Hanning window to the mean signal of segment 1, the first segment The first segment of the mean signal The windowing signal for the mean signal is : ;in, for Index; Perform a Discrete Fourier Transform on the windowed signal to obtain the transform result. : ;in, For frequency point index, the first The physical frequency corresponding to each frequency point , The value ranges from 1 to , The imaginary unit, is the base of the natural exponential function. This represents the q-th sample value of the discrete-time signal after the p-th segment has been processed with a Hanning window; Calculate the window energy normalization coefficient ; Based on the transformation result and the window energy normalization coefficient, calculate the first... The signal segment in the first Single-segment power spectrum at each frequency point , express The modulus; The unsprung acceleration power spectrum is obtained by summing and averaging the power spectra of each segment of the mean-de-mean signal. ; This indicates the number of segments in the mean-removed signal.

3. The method for sensor fault diagnosis and signal redundancy tolerance in a suspension system according to claim 2, characterized in that, The knee frequency is determined by piecewise fitting in the unsprung acceleration power spectrum. Based on the knee frequency and the vehicle speed signal, the speed-normalized roll-off frequency is calculated, including: Perform a natural logarithmic transformation on the physical frequency and the corresponding power spectrum value at each frequency point to obtain the th... Logarithmic frequency at each frequency point With logarithmic power ;in, It is the natural logarithm function; Selecting candidate boundary frequency points , The value ranges from 1 to ; For candidate boundary frequency points Establish the left-segment linear model and the right-segment linear model: The left-side linear model is ; The right-hand linear model is ; in, The intercept of the left-side linear model. The slope of the left segment of the linear model. The intercept of the right-hand linear model. The slope of the right segment of the linear model; Calculate the sum of squares of the left segment residuals : ； Sum of squared residuals on the right segment : ； Will and The sum of these values ​​is used as the total residual; The candidate boundary frequency point that will cause the total residual to be minimized As knee frequency ; Get the Vehicle speed signal at discrete time points ; The average vehicle speed is the average value of all discrete-time vehicle speed signals. ; The ratio of knee frequency to average vehicle speed is used as the speed-normalized roll-off frequency. .

4. The method for sensor fault diagnosis and signal redundancy tolerance in a suspension system according to claim 3, characterized in that, The roll-off frequency is determined based on the speed-normalized roll-off frequency. A piecewise power-law road displacement power spectrum prior is established with the roll-off frequency as the boundary. The power spectrum prior is combined with the vehicle dynamics model transfer function to obtain the desired output power spectrum, including: Based on velocity-normalized roll-off frequency With average vehicle speed Calculate the roll-off frequency ; Constructing a priori pavement displacement power spectrum in piecewise power law form Specifically: When physical frequency satisfy hour, ; When physical frequency satisfy hour, ; in, This is the relative amplitude calibration constant. As the reference frequency, It is a self-affine power exponent. The additional decay exponent after roll-off and ; The vehicle dynamics model transfer function from road surface displacement to unsprung acceleration is defined as follows: ； in, For the Laplace complex frequency, For the sprung mass, For unsprung mass, This is the suspension damping coefficient. This is the suspension stiffness coefficient. This refers to the tire stiffness coefficient. By combining the transfer function with the prior knowledge of the road displacement power spectrum, the expected output power spectrum of unsprung acceleration is calculated. ;in, For the transfer function in The modulus at that point.

5. The method for sensor fault diagnosis and signal redundancy tolerance in a suspension system according to claim 4, characterized in that, The analysis band is determined based on the roll-off frequency. Within the analysis band, the statistics of the normalized energy of a single channel are calculated, including: Preset analysis frequency band lower limit coefficient Analysis of the upper limit coefficient of the frequency band Calculate and analyze the lower limit of the frequency band Analysis of the upper limit of the frequency band ; Construct a set of frequency points , For all satisfied The set of frequency points; Statistical calculation of single-channel normalized energy ;in, , The desired output power spectrum at the physical frequency for unsprung acceleration The value at that location.

6. The method for sensor fault diagnosis and signal redundancy tolerance in a suspension system according to claim 5, characterized in that, The fault threshold is determined based on statistical distribution and significance level. When the statistic exceeds the fault threshold, the sensor is determined to be in a faulty state, including: Calculate the degrees of freedom of the chi-square distribution. ;in, For the set of frequency points Number of mid-frequency points; Calculate the fault threshold ;in, Describing the degrees of freedom as The quantile function of the chi-square distribution, Indicates the pre-set significance level. ; If the statistics of single-channel normalized energy > Fault Threshold If so, the sensor is determined to be in a faulty state.

7. The method for sensor fault diagnosis and signal redundancy tolerance in a suspension system according to claim 6, characterized in that, When a fault condition is identified, frequency domain regularization inversion is performed using power spectrum prior as a constraint to obtain a road surface displacement power spectrum estimate. Based on the road surface displacement power spectrum estimate, a virtual output for the target channel is generated, including: Calculate the first Discrete spectrum estimation of road displacement at each frequency point : ;in, Indicates the transfer function in The complex conjugate at the location, For the prior of the road displacement power spectrum at physical frequency The value of , Represents the regularization coefficient; Calculate the first Road displacement power spectrum estimation at each frequency point : ;in, Represents the discrete spectrum estimation of road displacement The modulus; Calculate the first Virtual output of target channel frequency domain at each frequency point : ;in, The transfer function from road surface displacement to the target channel : 。 8. The method for sensor fault diagnosis and signal redundancy tolerance in a suspension system according to claim 7, characterized in that, Based on the difference between the statistics and the fault threshold, a weighted soft substitution is applied to the measured output and virtual output of the target channel, and the fault-tolerant output results are output, including: Calculate the proportion of the difference between the statistic and the fault threshold. ;in, It is a positive part function. ; Calculate the weighting coefficients of the target channel ;in, This represents a coefficient that adjusts the intensity of substitution. Determine the measured discrete spectrum of the target channel With virtual discrete spectrum ; Calculate the target channel at physical frequency Fusion discrete spectrum : ;in, For the target channel at physical frequency The fused spectrum value; Total number of preset frequency points Calculate the target channel in the first... Time-domain fault-tolerant output at discrete moments .

9. A suspension system sensor fault diagnosis and signal redundancy fault tolerance system, applied in the suspension system sensor fault diagnosis and signal redundancy fault tolerance method according to any one of claims 1-8, characterized in that, include: The data acquisition module collects driving signals, including unsprung acceleration signals and vehicle speed signals; it preprocesses and estimates the power spectrum of the driving signals to obtain the unsprung acceleration power spectrum. The data processing module determines the knee frequency in the unsprung acceleration power spectrum through piecewise fitting, and calculates the speed-normalized roll-off frequency based on the knee frequency and the vehicle speed signal. The desired output power spectrum module determines the roll-off frequency based on the speed-normalized roll-off frequency, establishes a piecewise power-law road displacement power spectrum prior with the roll-off frequency as the boundary, and combines the power spectrum prior with the vehicle dynamics model transfer function to obtain the desired output power spectrum. The fault determination module determines the analysis frequency band based on the roll-off frequency, calculates the statistics of the normalized energy of a single channel within the analysis frequency band, determines the fault threshold based on the statistical distribution and significance level, and determines the sensor as faulty when the statistics exceed the fault threshold. When a fault condition is determined, the virtual output generation module performs frequency domain regularization inversion with power spectrum prior as a constraint to obtain the road displacement power spectrum estimate, and generates the virtual output of the target channel based on the road displacement power spectrum estimate. The fault tolerance calculation module performs weighted soft substitution on the measured output and virtual output of the target channel based on the difference between the statistics and the fault threshold, and outputs the fault tolerance result.

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