Device for detecting adjustment noise fluctuation of electrically-controlled tubular column

Abstract

The invention discloses a detection device for adjusting noise fluctuation of an electrically-controlled tubular column. The detection device comprises a signal preprocessing module, a current signal estimation module, a magnitude conversion module and a signal post-processing module, the signal preprocessing module is used for processing a motor rotation angular velocity signal and a motor rotation angular acceleration signal, and outputting the motor rotation angular velocity signal and the motor rotation angular acceleration signal; the current signal estimation module corrects the motor rotation angular velocity signal and outputs a multi-order filtering current signal; the magnitude conversion module calculates according to a magnitude calculation formula to obtain a current magnitude signal and outputs the current magnitude signal; the signal post-processing module processes the current magnitude signal to obtain a comparison value, the comparison value is compared with a preset standard value, and if the comparison value is larger than the preset standard value, it is judged that the adjustment noise fluctuation of the electrically-controlled pipe column is abnormal.

Description

Technical Field

[0001] The invention relates to the technical field of steering systems, and in particular to a detection device for adjusting noise fluctuations of an electrically adjustable column. Background Art

[0002] The electric steering system is a part of the steering system, which realizes the electric adjustment of the steering wheel. During the adjustment process, the motor speed will fluctuate due to the fluctuation of the load, which will cause the fluctuation of the adjustment noise. If the noise fluctuation is too large, it will cause discomfort to the driver. Therefore, in the actual production process, 100% online detection is required to isolate products with abnormal noise fluctuations.

[0003] The existing detection technologies mainly include: 1. Testing with a microphone: This method is usually carried out in an anechoic chamber with a background noise of less than 20dB. The air noise generated by the motor is measured by a microphone, and the noise fluctuation can be identified after processing. However, the background noise requirements on the production line cannot be met, and the cost of building an anechoic chamber is also huge; 2. Testing with a vibration acceleration sensor: This method has no requirements for background noise, but the vibration acceleration test is structural noise, which is greatly affected by the environment and cannot accurately determine the size of the noise fluctuation. Therefore, the above detection technology is currently only used in laboratories.

[0004] Therefore, how to achieve 100% detection of products on the production line to adjust the fluctuation of noise, accurately identify defective parts, and realize online detection with high robustness, strong generalization and high accuracy is the technical problem currently faced. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a detection device for electric control string adjustment noise fluctuation, including a signal preprocessing module, a current signal estimation module, a magnitude conversion module and a signal post-processing module;

[0006] The signal preprocessing module processes the motor rotation angular velocity signal and the motor rotation angular acceleration signal, and outputs the motor rotation angular velocity signal and the motor rotation angular acceleration signal;

[0007] The current signal estimation module performs correction processing on the motor rotation angular velocity signal and outputs a multi-order filtered current signal;

[0008] The magnitude conversion module calculates the current magnitude signal according to the magnitude calculation formula and outputs it;

[0009] The signal post-processing module processes the current magnitude signal to obtain a comparison value, and compares the comparison value with a preset standard value. If the comparison value is greater than the preset standard value, it is determined that the electric control column adjustment noise fluctuation is abnormal.

[0010] Preferably, the motor rotation angular velocity signal processing is to convert the original motor current signal into the motor rotation angular velocity; the motor rotation angular acceleration signal processing is to convert the original motor current signal into the motor rotation angular acceleration.

[0011] Preferably, the current signal estimation module performs correction processing on the motor rotation angular velocity signal according to the motor system dynamics model and the Kalman filter estimation method to obtain a corrected motor rotation angular velocity signal.

[0012] Preferably, the current signal estimation module calculates the corrected current signal based on the back electromotive force formula and the motor current calculation formula during the operation of the motor and takes the corrected motor rotation angular velocity signal as input.

[0013] Preferably, the multi-order filtered current signal is calculated by an RMS filtering algorithm.

[0014] Preferably, the RMS filtering algorithm is: taking the corrected current signal as input and calculating the root mean square point by point to obtain a first-order RMS filtered current signal, repeating the RMS filtering algorithm N times to obtain a multi-order filtered current signal, wherein N times is a preset number of times.

[0015] Preferably, the method for the signal post-processing module to obtain a comparison value after processing the current magnitude signal is as follows: step S1, perform peak-valley detection to identify each peak and valley in the current magnitude signal; step S2, perform peak-valley difference calculation to calculate the difference between adjacent peaks and valleys; step S3, perform peak-valley difference sorting to sort the peak-valley differences from large to small; step S4, perform data fitting, intercept a segment in the peak-valley difference to calculate the root mean square value to obtain the comparison value. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0017] Figure 1 It is a schematic diagram of a detection device for adjusting noise fluctuation of an electrically adjustable pipe column in Example 1;

[0018] Figure 2 Schematic diagram of the Kalman filter estimation method in Example 1. DETAILED DESCRIPTION

[0019] The following describes the implementation methods of the present invention through specific specific embodiments, and those skilled in the art can fully understand other advantages and technical effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through different specific implementation methods, and the details in this specification can also be applied based on different viewpoints, and various modifications or changes can be made without deviating from the overall design concept of the invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. The following exemplary embodiments of the present invention can be implemented in a variety of different forms and should not be interpreted as being limited to the specific embodiments described herein. It should be understood that these embodiments are provided to make the disclosure of the present invention thorough and complete, and to fully convey the technical solutions of these exemplary specific embodiments to those skilled in the art.

[0020] Example 1

[0021] like Figure 1 As shown, this embodiment provides a detection device for electric control string adjustment noise fluctuation, including a signal preprocessing module, a current signal estimation module, a magnitude conversion module and a signal post-processing module;

[0022] The signal preprocessing module processes the motor rotation angular velocity signal and the motor rotation angular acceleration signal, and outputs the motor rotation angular velocity signal and the motor rotation angular acceleration signal;

[0023] The current signal estimation module performs correction processing on the motor rotation angular velocity signal and outputs a multi-order filtered current signal;

[0024] The magnitude conversion module calculates the current magnitude signal according to the magnitude calculation formula and outputs it;

[0025] The signal post-processing module processes the current magnitude signal to obtain a comparison value, and compares the comparison value with a preset standard value. If the comparison value is greater than the preset standard value, it is determined that the electric control column adjustment noise fluctuation is abnormal.

[0026] The motor rotation angular velocity signal processing is to convert the original motor current signal into the motor rotation angular velocity; the motor rotation angular acceleration signal processing is to convert the original motor current signal into the motor rotation angular acceleration.

[0027] The current signal estimation module corrects the motor rotation angular velocity signal according to the motor system dynamics model and the Kalman filter estimation method to obtain a corrected motor rotation angular velocity signal The specific process is as follows:

[0028] The dynamic model of the motor system, its dynamic differential equation is: Where M is the motor rotor mass, B is the motor system damping coefficient, k2 is the motor torque constant, i m is the motor current, θ m , They are the rotation angle, angular velocity and angular acceleration of the motor rotor respectively.

[0029] According to the principle of brushed DC motor, the back electromotive force during the operation of the motor Motor current calculation formula Calculated

[0030] The motor system dynamics model is rewritten as a time-continuous state space equation:

[0031] Among them, θ m , is the system state variable.

[0032] Observation equation: Rewrite it into a discrete state space equation using the Euler method:

[0033]

[0034] Where Δt is the sampling period and I is the identity matrix.

[0035] like Figure 2 As shown, according to the Kalman filter principle, the state space equation is simplified to: t =Ax t-1 +Bu t-1 +w t-1 , z t =Hx t +v t , where the current state Past state Control vector u t =i m t , observation quantity State transition matrix Control Input Matrix Observation Matrix Process noise w t , observation noise v t .

[0036] Designed according to the Kalman filter principle, the prediction iteration formula is: state prediction in, is the state prediction at time step (t), is the state estimate correction at the previous time step (t-1), ut-1 is the control vector of the previous time step (t-1).

[0037] Error Covariance Forecast in, is the error covariance of the predicted state estimate, P t-1 is the error covariance correction at the previous time step (t-1), and Q is the process noise covariance matrix.

[0038] Correction iterative formula: Kalman gain Where R is the measurement noise covariance matrix. State estimation correction Error covariance correction

[0039] According to the principle of brushed DC motor, the back electromotive force during the operation of the motor Motor current calculation formula Calculate the corrected current signal

[0040] Through the RMS filtering algorithm, according to the formula The root mean square of the corrected current signal is calculated point by point to obtain the first-order RMS filtered current signal Repeat the RMS filtering algorithm N times, that is, multi-order RMS filtering.

[0041] Then according to the magnitude calculation formula The current magnitude signal is obtained.

[0042] The method by which the signal post-processing module obtains the comparison value after processing the current magnitude signal is:

[0043] Step S1, performing peak-valley detection to identify each peak-valley in the current magnitude signal; specifically, calculating the difference between two adjacent data points at any time point by point; When f(t)×f(t+1)<0, it is judged as the inflection point at time t, and the amplitude Then the i-th inflection point in chronological order is

[0044] Step S2, performing peak-to-valley difference calculation, calculating the difference between adjacent peaks and valleys Diff(j)=F(i+1)-F(i);

[0045] Step S3, sorting the peak-to-valley differences, sorting the peak-to-valley differences from large to small; that is, sorting arr = [Diff(1), Diff(2), Diff(3) ... Diff(j)] from large to small, and obtaining

[0046]

[0047] Step S4, performing data fitting, intercepting segments of the peak-to-valley difference and calculating the root mean square value to obtain the comparison value Diff.

[0048]

[0049] Then the comparison value Diff is compared with the preset standard value Diff0. If Diff>Diff0, it is determined to be a defective product.

[0050] This embodiment is based on accurate system modeling and adopts an algorithm combining Kalman filtering and RMS filtering to obtain a current preprocessing signal of the regulation process with little system noise interference. By obtaining the peak-to-valley difference of the current signal, an accurate match between the peak-to-valley difference and the severity of the subjective evaluation of the regulation noise is achieved, thereby realizing a production line detection method that uses the current signal to accurately judge the severity of the regulation noise.

[0051] The present invention has been described in detail above through specific implementation modes and embodiments, but these do not constitute limitations of the present invention. Without departing from the principles of the present invention, those skilled in the art may also make many variations and improvements, which should also be regarded as the protection scope of the present invention.

Claims

1. A detection device for noise fluctuation of an electrically adjustable pipe string, characterized in that: It includes a signal preprocessing module, a current signal estimation module, a magnitude conversion module and a signal post-processing module; The signal preprocessing module processes the motor rotation angular velocity signal and the motor rotation angular acceleration signal, and outputs the motor rotation angular velocity signal and the motor rotation angular acceleration signal; The current signal estimation module performs correction processing on the motor rotation angular velocity signal and outputs a multi-order filtered current signal; The magnitude conversion module calculates the current magnitude signal according to the magnitude calculation formula and outputs it; The signal post-processing module processes the current magnitude signal to obtain a comparison value, and compares the comparison value with a preset standard value. If the comparison value is greater than the preset standard value, it is determined that the electric control column adjustment noise fluctuation is abnormal.

2. The device for detecting the noise fluctuation of the electric control column adjustment according to claim 1, characterized in that: The motor rotation angular velocity signal processing is to convert the original motor current signal into the motor rotation angular velocity; the motor rotation angular acceleration signal processing is to convert the original motor current signal into the motor rotation angular acceleration.

3. The device for detecting the noise fluctuation of the electric control column adjustment according to claim 1, characterized in that: The current signal estimation module performs correction processing on the motor rotation angular velocity signal according to the motor system dynamics model and the Kalman filter estimation method to obtain a corrected motor rotation angular velocity signal.

4. The device for detecting the noise fluctuation of the electric control column adjustment according to claim 3 is characterized in that: The current signal estimation module calculates the corrected current signal using the corrected motor rotation angular velocity signal as input according to the back electromotive force formula and the motor current calculation formula during the operation of the motor.

5. The device for detecting the noise fluctuation of the electric control column adjustment according to claim 4 is characterized in that: The multi-order filtered current signal is calculated by an RMS filtering algorithm.

6. The device for detecting the noise fluctuation of the electric control column adjustment according to claim 5, characterized in that: The RMS filtering algorithm is: taking the corrected current signal as input and calculating the root mean square point by point to obtain a first-order RMS filtered current signal, repeating the RMS filtering algorithm N times to obtain a multi-order filtered current signal, wherein N times is a preset number of times.

7. The device for detecting the noise fluctuation of the electric control column adjustment according to claim 1, characterized in that: The method for the signal post-processing module to obtain the comparison value after processing the current magnitude signal is: Step S1, performing peak-valley detection to identify each peak-valley in the current magnitude signal; Step S2, performing peak-to-valley difference calculation to calculate the difference between adjacent peaks and valleys; Step S3, sorting the peak-to-valley differences, and sorting the peak-to-valley differences from large to small; Step S4, performing data fitting, intercepting segments of the peak-to-valley difference and calculating the root mean square value to obtain the comparison value.

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