Method for detecting impact noise of wiper motor
By collecting the vibration signal of the wiper motor and performing short-time Fourier transform and convolution processing, the accuracy and efficiency problems of wiper motor impact noise detection are solved, and efficient and accurate motor fault diagnosis is achieved.
Patent Information
- Application Number
- CN202511593183.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-03
- Publication Date
- 2026-01-13
AI Technical Summary
There are few existing methods for detecting the impact vibration signal of wiper motors. They rely on human hearing or structural disassembly and inspection, which suffer from insufficient accuracy, high cost, and low efficiency.
Vibration signals from the wiper motor during forward and reverse rotation are collected using a vibration sensor. Short-time Fourier transform is performed to extract characteristic frequencies, which are then normalized and convolved to calculate energy fluctuation values and maximum values. These values are then compared with thresholds to determine motor malfunctions.
It achieves efficient and accurate detection of wiper motor impact noise, is suitable for automated production lines, can quickly screen out faulty motors, reduce the false judgment rate, and is applicable to different motors.
Smart Images
Figure CN121324935A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of motor fault detection technology, and in particular relates to a method for detecting impact noise in a wiper motor. Background Technology
[0002] In modern automobiles, the wiper motor, though a small component, plays a crucial role in safety. The core of the wiper system lies in its motor drive; its operation directly determines whether the wipers can effectively clear debris from the windshield in rain, snow, fog, or dust, ensuring clear visibility for the driver. If the wiper motor malfunctions—for example, with delayed start-up, insufficient speed, or sudden stop—the driver will face severe obstruction of vision at high speeds or in complex traffic conditions, greatly increasing the risk of accidents. Therefore, the wiper motor is not only a comfort feature but also a key actuator directly related to driving safety. From a technical perspective, wiper motors must possess excellent waterproof, dustproof, and durable properties to adapt to various climatic conditions and the demands of prolonged, high-frequency operation. Simultaneously, their design must ensure rapid power cut-off or switching to protection modes in case of electrical overload, mechanical jamming, or other abnormalities, preventing further vehicle safety risks due to overheating or short circuits. With the increasing prevalence of intelligent driving, the reliability of wiper motors is increasingly intertwined with sensing systems and autonomous driving assistance functions; any failure could pose a systemic risk.
[0003] Impact noise in a motor often indicates a significant malfunction. This could be due to defects or peeling in gears and bearings; a loose or faulty component in the transmission system; foreign objects entering the motor and affecting its operation; or sparks caused by transient current changes. Monitoring wiper motor vibration signals, especially impact noise, is an indispensable part of automotive safety engineering.
[0004] Currently, there are relatively few signal detection algorithms for impact vibration of wiper motors. On production lines, detection of such issues often relies on human hearing or structural disassembly, which suffers from insufficient accuracy, high cost, and low efficiency. Therefore, developing an intelligent program for detecting impact noise is essential.
[0005] The relevant literature is: Measurement and Fault Characteristic Analysis of Abnormal Noise from Wiper Motors, published by Professor Wang Baicun in 2023 and included in the proceedings of the 21st Symposium on Precision Machinery and Manufacturing Technology - PMMT2023. This literature uses the triaxial vibration signal of the wiper motor to explore the impact of commutator faults on vibration noise, especially the impact on the time-domain curve and short-time Fourier transform image of the vibration.
[0006] This paper analyzes in detail the relationship between the vibration signal and fault of the steering gear, but it has the following shortcomings for industrial production applications: 1) The time span of the time-frequency diagram analysis in the paper is about 50 seconds, which is a long detection time, meaning that the efficiency of the factory production line may be significantly reduced as a result; 2) The paper mainly focuses on the detection of resonance, frequency stability and high-frequency noise, and does not propose a method specifically for detecting impact noise; 3) The paper does not verify the accuracy of the method under a large number of samples. Summary of the Invention
[0007] The purpose of this invention is to provide a method for detecting impact noise in the audio of a wiper motor, in order to solve the technical problems that there are few signal detection methods for impact vibration of wiper motors in the prior art, and the detection of such problems on the production line mostly relies on human hearing or structural disassembly and inspection, which has limitations such as insufficient accuracy, high cost and low efficiency.
[0008] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:
[0009] A method for detecting impact noise of a windshield wiper motor, the method comprising the following steps:
[0010] Step S1: Collect vibration signals of the wiper motor rotating forward and backward using a vibration sensor to obtain an audio file of the wiper motor rotating forward and backward;
[0011] Step S2: Extract the smooth operating range of the wiper motor in both forward and reverse rotation from the audio file to obtain the smooth audio signal of the wiper motor in forward rotation and the smooth audio signal of the wiper motor in reverse rotation;
[0012] Step S3: Perform short-time Fourier transform on the smooth audio signal of the wiper motor rotating forward and the smooth audio signal of the wiper motor rotating in reverse, respectively, to obtain the time-frequency matrix of forward rotation and the time-frequency matrix of reverse rotation;
[0013] Step S4: Based on the speed of the wiper motor and the structural parameters of the carbon brush, worm gear, worm, and gear, obtain the characteristic frequency of the wiper motor;
[0014] Step S5: Based on the characteristic frequency and multiples of the characteristic frequency of the wiper motor, extract the frequency segments to be analyzed from the forward rotation time-frequency matrix and the reverse rotation time-frequency matrix to obtain the forward rotation preferred time-frequency matrix and the reverse rotation preferred time-frequency matrix;
[0015] Step S6: Normalize the forward rotation optimization time-frequency matrix and the reverse rotation optimization time-frequency matrix respectively, and then calculate the average column by column to obtain the forward rotation vibration energy vector and the reverse rotation vibration energy vector;
[0016] Step S7: Convolve the forward rotation vibration energy vector and the reverse rotation vibration energy vector using the filter vector to obtain the forward rotation filter energy vector and the reverse rotation filter energy vector respectively;
[0017] Step S8: Calculate the standard deviation of the forward-rotating filter energy vector and the reverse-rotating filter energy vector respectively to obtain the forward-rotating energy fluctuation value and the reverse-rotating energy fluctuation value;
[0018] Step S9: Compare the forward rotation energy fluctuation value and the reverse rotation energy fluctuation value with the fluctuation value threshold, and compare the maximum value of the forward rotation filter energy vector and the maximum value of the reverse rotation filter energy vector with the maximum value threshold; if either the forward rotation energy fluctuation value or the reverse rotation energy fluctuation value exceeds the fluctuation value threshold, or either the maximum value of the forward rotation filter energy vector or the maximum value of the reverse rotation filter energy vector exceeds the maximum value threshold, then the wiper motor is considered to be a faulty motor.
[0019] Furthermore, in step S1, the vibration sensor is a single-axis piezoelectric accelerometer. The vibration sensor is mechanically fixed to the wiper motor, and the other end of the vibration sensor is connected to the matching data acquisition card. The data acquisition card is connected to the computer, and the data acquisition card converts the analog signal generated by the vibration sensor into an electrical signal that can be directly read by the computer. The computer reads the vibration signal of the wiper motor through the serial port.
[0020] Furthermore, the short-time Fourier transform formula in step S3 is as follows:
[0021]
[0022] in, It is the output result of the short-time Fourier transform (a two-dimensional complex matrix). It is the position that the window function moves. It is a frequency index. It is a discrete-time domain signal. It is the index of time sampling points. It is a window function. It is the imaginary unit. It represents the number of data points in the Fourier transform.
[0023] Furthermore, the characteristic frequencies of each component of the wiper motor in step S4 are calculated using the following formula:
[0024]
[0025] Where f is the characteristic frequency generated by the target component in the wiper motor. It is the frequency of the wiper motor's rotation. It's the transmission ratio. It refers to the number of impacts generated by the target component in the wiper motor rotating once.
[0026] Furthermore, the convolution processing method in step S7 is as follows:
[0027]
[0028] in, It is the filtered energy vector (i.e., the output of the convolution process). It is a vibrational energy vector. It is the filter vector.
[0029] Compared with the prior art, the present invention has the following beneficial technical effects:
[0030] 1) Automated production line friendly. This program can directly process input WAV vibration files, make a final judgment, and even draw charts to show the degree of failure. Since the inspection time for a single motor is much less than the production time, wiper motors can be inspected one by one after production, preventing product accumulation on the production line.
[0031] 2) High accuracy. For a large number of samples, it can screen out 100% of obviously faulty motors while maintaining an extremely low false positive rate.
[0032] 3) High applicability. Since impact noise is one of the characteristics of most motor failures, this method is theoretically applicable to most mechanical motors. Secondly, the fluctuation threshold and maximum threshold are determined by actual experiments, rather than being static, thus different thresholds can be obtained for different motors. Therefore, the method proposed in this invention can be applied to different motors. Attached Figure Description
[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0034] Figure 1 This is a schematic diagram of the method for detecting impact noise in the audio of a wiper motor according to the present invention.
[0035] Figure 2 This is a schematic diagram of the audio acquisition structure of the wiper motor of the present invention.
[0036] Figure 3 This is a schematic diagram of the time-domain curve and STFT image of normal audio.
[0037] Figure 4 This is a schematic diagram of the time-domain curve and STFT image of the abnormal audio.
[0038] Figure 5 This is a schematic diagram showing the distribution of forward and reverse energy fluctuation values for normal and abnormal audio.
[0039] Figure 6 This is a schematic diagram showing the maximum value distribution of the forward and reverse filtering energy vectors for normal and abnormal audio. Detailed Implementation
[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.
[0041] This invention proposes a method for detecting impact noise in the audio of a windshield wiper motor, such as... Figure 1 As shown, the method includes the following steps:
[0042] Step S1: Collect vibration signals of the wiper motor rotating forward and backward using a vibration sensor to obtain an audio file of the wiper motor rotating forward and backward.
[0043] Specifically, the vibration sensor uses a uniaxial piezoelectric accelerometer, such as... Figure 2 As shown, a vibration sensor is mechanically fixed to the wiper motor. The other end of the sensor is connected to a matching data acquisition card, which in turn connects to a computer. The data acquisition card converts the analog signal generated by the vibration sensor into an electrical signal that can be directly read by the computer. The computer reads the vibration signal from the wiper motor via a serial port. This controls the wiper motor to rotate forward and backward, and the computer obtains an audio file recording the forward and reverse rotation of the wiper motor through the vibration sensor and the data acquisition card.
[0044] Furthermore, the vibration sensor has a sampling frequency of 22050 Hz; the audio file is in WAV format; the total duration of the audio file is 2.8 seconds, the wiper motor first rotates forward for 1.4 seconds, then reverses for 1.4 seconds, and the audio includes stages of starting, accelerating, running smoothly, decelerating, reversing, accelerating, running smoothly, and decelerating.
[0045] Step S2: Extract the smooth operating range of the wiper motor in both forward and reverse rotation from the audio file to obtain the smooth audio signal of the wiper motor in both forward and reverse rotation.
[0046] Specifically, the audio file is extracted to capture the stable 0.9-second intervals between the forward and reverse rotation of the wiper motor, i.e., audio segments with time intervals of [0.35, 1.25] seconds and [1.7, 2.6] seconds. These are used as the stable operating intervals of the wiper motor in forward and reverse rotation, respectively, to obtain the stable audio signal of the wiper motor in forward rotation and the stable audio signal of the wiper motor in reverse rotation.
[0047] Step S3: Perform Short-Time Fourier Transform (STFT) on the smooth audio signal of the wiper motor rotating forward and the smooth audio signal of the wiper motor rotating in reverse, respectively, to obtain the time-frequency matrix for forward rotation and the time-frequency matrix for reverse rotation. The STFT formula is as follows:
[0048]
[0049] in, It is the output result of the short-time Fourier transform (a two-dimensional complex matrix). It is the position that the window function moves. It is a frequency index. It is a discrete-time domain signal. It is the index of time sampling points. It is a window function. It is the imaginary unit. It represents the number of data points in the Fourier transform.
[0050] The window function used is the commonly used Hanning window, and its calculation formula is as follows:
[0051]
[0052] Substituting the stable forward rotation audio signal of the wiper motor into the discrete time domain signal Perform a short-time Fourier transform to obtain the forward-rotating time-frequency matrix. .
[0053] Substituting the stable audio signal of the wiper motor reversing into the discrete time domain signal Perform a short-time Fourier transform to obtain the inverted time-frequency matrix. .
[0054] like Figure 3 As shown, the short-time Fourier transform (SFT) time-frequency plot of a normal wiper motor's steady audio signal shows no obvious abnormalities, while the SFT time-frequency plot of a vibration signal with impact noise shows vertical high-amplitude regions, such as... Figure 4 As shown, for impulse noise, its short-time Fourier transform has the characteristics of wide frequency domain and short time, which will appear as a high-amplitude vertical bar region in the time-frequency graph.
[0055] Step S4: Based on the speed of the wiper motor and the structural parameters of the carbon brush, worm gear, worm, and gear, obtain the characteristic frequency of the wiper motor.
[0056] The internal structure of a windshield wiper motor consists of components such as carbon brushes, worm gears, worm shafts, and gears, all working together. As the wiper motor rotates, these components repeatedly engage and disengage, generating periodic vibrations. The frequency of this periodic vibration is called the characteristic frequency. Therefore, the characteristic frequency of the wiper motor can be obtained based on its rotational speed and the structural parameters of the carbon brushes, worm gears, worm shafts, and gears.
[0057] The characteristic frequencies of each component of the wiper motor are calculated using the following formula:
[0058]
[0059] in, It is the characteristic frequency of the motor generated by the target component. It is the frequency of the wiper motor rotation (in Hz). It is the transmission ratio (that is, the number of rotations of the corresponding parts for one rotation of the wiper motor, which is a ratio). It refers to the number of impacts generated when the target part rotates once. For example, for a carbon brush, it is the number of copper plates; for a gear, it is the number of teeth; for a worm, it is the number of threads; and for a worm wheel, it is the number of teeth.
[0060] The characteristic frequencies of the wiper motor are calculated for each component, resulting in the characteristic frequencies f1, f2, ... f of the wiper motor. Num ,in, This indicates the number of parts in the wiper motor that generate vibration.
[0061] Step S5: Based on the characteristic frequency of the wiper motor and its multiples, extract the frequency segments to be analyzed from the forward rotation time-frequency matrix and the reverse rotation time-frequency matrix to obtain the forward rotation preferred time-frequency matrix and the reverse rotation preferred time-frequency matrix.
[0062] The characteristic frequencies of the wiper motor are f1, f2, ... f Num and its multiples of frequency kf1, kf2, ... kf Num (k=1,2,…) are the main frequencies of vibration generated when the wiper motor is running.
[0063] In the forward rotation time-frequency matrix and the inverted time-frequency matrix In the middle, examine the amplitude of the multiple frequency component. If the amplitude is significantly greater than that of adjacent frequencies, it indicates that this multiple frequency is useful and requires further analysis. (Characteristic frequency of wiper motor) Together with the useful multiples of frequency, this forms the frequency range that needs to be analyzed. From the forward rotating time-frequency matrix... and the inverted time-frequency matrix Extract the frequency band to be analyzed from the data and use it as the forward optimization time-frequency matrix. and the inverted preferred time-frequency matrix .
[0064] In this invention example, data from 500-2000 Hz is selected as the main frequency band to be analyzed.
[0065] Step S6: Normalize the forward rotation optimization time-frequency matrix and the reverse rotation optimization time-frequency matrix respectively, and then calculate the average column by column to obtain the forward rotation vibration energy vector and the reverse rotation vibration energy vector.
[0066] Specifically, for the forward rotation optimization time-frequency matrix and the inverted preferred time-frequency matrix Normalize each value separately, scaling the amplitude proportionally to the [0, 1] interval. Then, average each column to obtain the forward rotation vibration energy vector. and reverse vibrational energy vector (Both are one-dimensional vectors).
[0067] Step S7: Convolve the forward rotation vibration energy vector and the reverse rotation vibration energy vector using the filter vector to obtain the forward rotation filter energy vector and the reverse rotation filter energy vector.
[0068] Use filter vector Energy vector of forward rotational vibration and reverse vibrational energy vector Convolution processes are performed separately to obtain the forward-rotated filter energy vector and the reverse-rotated filter energy vector. The convolution processing method is as follows:
[0069]
[0070] in, It is the filtered energy vector (i.e., the output of the convolution process). It is a vibrational energy vector (a one-dimensional vector). It is the filter vector.
[0071] The forward vibration energy vector Substituting the vibration energy vector into the convolution processing formula Perform convolution processing to obtain the forward-rotating filter energy vector. ; Reverse the vibrational energy vector Substituting the vibration energy vector into the convolution processing formula Perform convolution processing to obtain the inverted filter energy vector. .
[0072] Step S8: Calculate the standard deviation of the forward-rotating filter energy vector and the reverse-rotating filter energy vector respectively to obtain the forward-rotating energy fluctuation value and the reverse-rotating energy fluctuation value.
[0073] The formula for calculating the standard deviation is:
[0074]
[0075] in, It is the energy fluctuation value. It is the number of elements in the filtered energy vector. It is the filtered energy vector. It is the average value of the filtered energy vector.
[0076] The forward-rotating filter energy vector Substitute the filter energy vector into the standard deviation calculation formula The average value of the forward-rotating filter energy vector is substituted into the standard deviation calculation formula to calculate the average value of the filter energy vector. The positive rotation energy fluctuation value can be calculated. Invert the filter energy vector Substitute the filter energy vector into the standard deviation calculation formula Substitute the average value of the inverted filter energy vector into the standard deviation calculation formula for the average value of the filter energy vector. The value of the reverse energy fluctuation can be calculated. .
[0077] Step S9: Compare the forward rotation energy fluctuation value and the reverse rotation energy fluctuation value with the fluctuation value threshold, and compare the maximum value of the forward rotation filter energy vector and the maximum value of the reverse rotation filter energy vector with the maximum value threshold; if either the forward rotation energy fluctuation value or the reverse rotation energy fluctuation value exceeds the fluctuation value threshold, or either the maximum value of the forward rotation filter energy vector or the maximum value of the reverse rotation filter energy vector exceeds the maximum value threshold, then the wiper motor is considered to be a faulty motor.
[0078] For a wiper motor vibration signal with impact noise, at least one of the four calculated values—forward energy fluctuation value, reverse energy fluctuation value, maximum forward filtered energy vector value, and maximum reverse filtered energy vector value—must be greater than the corresponding value for a normal motor.
[0079] The positive energy fluctuation value C1-impulse-std will invert the energy fluctuation value. Denoteed as C2-impulse-std, this will forward-rotate the filtered energy vector. The maximum value is denoted as C1-impulse-max, and the inverted filter energy vector is used. The maximum value is denoted as C2-impulse-max. C1-impulse-std is compared with C2-impulse-std and the fluctuation threshold, and C1-impulse-max is compared with C2-impulse-max and the maximum value threshold. If either C1-impulse-std or C2-impulse-std exceeds the fluctuation threshold, or either C1-impulse-max or C2-impulse-max exceeds the maximum value threshold, the wiper motor is determined to be faulty.
[0080] The fluctuation threshold and maximum value threshold can be reasonably set according to the performance of different wiper motors. Generally, multiple known normal and faulty motors are tested to obtain the forward rotation energy fluctuation value, reverse rotation energy fluctuation value, forward rotation filtered energy vector maximum value, and reverse rotation filtered energy vector maximum value. Then, appropriate fluctuation threshold and maximum value threshold are selected through data analysis, ensuring a clear distinction between normal and faulty motors. For the wiper motor tested in the invention example, its fluctuation threshold is set to 0.26, and its maximum value threshold is set to 0.8.
[0081] like Figure 5 The diagram shows a comparison of C1-impulse-std and C2-impulse-std with a fluctuation threshold of 0.26; as shown... Figure 6 The motor fault result can be determined by comparing C1-impulse-max and C2-impulse-max with the maximum threshold of 0.8. Figure 5 Show the C2-impulse-std vs. C1-impulse-std distributions for 65 normal audio samples and 9 abnormal audio samples. Figure 6 The C2-impulse-max vs. C1-impulse-max distributions of the same 65 normal audio signals and 9 abnormal audio signals are shown. It can be seen that there is a significant difference between the abnormal and normal motors. Therefore, the method proposed in this invention can achieve automated detection of wiper motor impact noise.
[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for detecting impact noise of a windshield wiper motor, characterized in that, The method includes the following steps: Step S1: Collect vibration signals of the wiper motor rotating forward and backward using a vibration sensor to obtain an audio file of the wiper motor rotating forward and backward; Step S2: Extract the smooth operating range of the wiper motor in both forward and reverse rotation from the audio file to obtain the smooth audio signal of the wiper motor in forward rotation and the smooth audio signal of the wiper motor in reverse rotation; Step S3: Perform short-time Fourier transform on the smooth audio signal of the wiper motor rotating forward and the smooth audio signal of the wiper motor rotating in reverse, respectively, to obtain the time-frequency matrix of forward rotation and the time-frequency matrix of reverse rotation; Step S4: Based on the speed of the wiper motor and the structural parameters of the carbon brush, worm gear, worm, and gear, obtain the characteristic frequency of the wiper motor; Step S5: Based on the characteristic frequency and multiples of the characteristic frequency of the wiper motor, extract the frequency segments to be analyzed from the forward rotation time-frequency matrix and the reverse rotation time-frequency matrix to obtain the forward rotation preferred time-frequency matrix and the reverse rotation preferred time-frequency matrix; Step S6: Normalize the forward rotation optimization time-frequency matrix and the reverse rotation optimization time-frequency matrix respectively, and then calculate the average column by column to obtain the forward rotation vibration energy vector and the reverse rotation vibration energy vector; Step S7: Convolve the forward rotation vibration energy vector and the reverse rotation vibration energy vector using the filter vector to obtain the forward rotation filter energy vector and the reverse rotation filter energy vector respectively; Step S8: Calculate the standard deviation of the forward-rotating filter energy vector and the reverse-rotating filter energy vector respectively to obtain the forward-rotating energy fluctuation value and the reverse-rotating energy fluctuation value; Step S9: Compare the forward rotation energy fluctuation value and the reverse rotation energy fluctuation value with the fluctuation value threshold, and compare the maximum value of the forward rotation filter energy vector and the maximum value of the reverse rotation filter energy vector with the maximum value threshold; If either the forward energy fluctuation value or the reverse energy fluctuation value exceeds the fluctuation value threshold, or if either the maximum value of the forward filter energy vector or the maximum value of the reverse filter energy vector exceeds the maximum value threshold, then the wiper motor is considered to be a faulty motor.
2. The method for detecting wiper motor impact noise according to claim 1, characterized in that, In step S1, the vibration sensor is a single-axis piezoelectric accelerometer. The vibration sensor is mechanically fixed to the wiper motor, and the other end of the vibration sensor is connected to the matching data acquisition card. The data acquisition card is connected to the computer, and the data acquisition card converts the analog signal generated by the vibration sensor into an electrical signal that can be directly read by the computer. The computer reads the vibration signal of the wiper motor through the serial port.
3. The method for detecting wiper motor impact noise according to claim 1, characterized in that, The short-time Fourier transform formula in step S3 is as follows: ; in, It is the output result after the short-time Fourier transform. It is the position that the window function moves. It is a frequency index. It is a discrete-time domain signal. It is the index of time sampling points. It is a window function. It is the imaginary unit. It represents the number of data points in the Fourier transform.
4. The method for detecting wiper motor impact noise according to claim 1, characterized in that, In step S4, the characteristic frequencies of each component of the wiper motor are calculated using the following formula: ; Where f is the characteristic frequency generated by the target component in the wiper motor. It is the frequency of the wiper motor's rotation. It's the transmission ratio. It refers to the number of impacts generated by the target component in the wiper motor rotating once.
5. The method for detecting wiper motor impact noise according to claim 1, characterized in that, The convolution processing method in step S7 is as follows: ; in, It is the filtered energy vector. It is a vibrational energy vector. It is the filter vector.