Fault diagnostic method and diagnostic device of sensor

A technology of sensor faults and diagnostic methods, applied in the direction of instruments, etc., can solve problems such as the inability to express the time-frequency local properties of signals

Inactive Publication Date: 2012-09-19
WEICHAI POWER CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

In the prior art, the traditional fault diagnosis method based on signal analysis is based on Fourier transform. The inventor found in the process of realizing the present invention that although Fourier transform can effectively analyze stationary signals, what it uses is a The global transformation is either completely in the time domain or completely in the frequency domain, so it cannot express the time-frequency local properties of the signal, and this property is precisely the most fundamental and critical property of non-stationary signals
Therefore, there are great limitations in diagnosing sensor mutation faults with traditional signal analysis methods

Method used

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  • Fault diagnostic method and diagnostic device of sensor
  • Fault diagnostic method and diagnostic device of sensor

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Embodiment 1

[0043] In this embodiment, the wavelet packet transform and the neural network are combined to diagnose the sudden fault of the sensor. The wavelet packet can be regarded as an extension of the step-by-step orthogonal division of the function space, and inherits the characteristics of the corresponding wavelet function. Wavelet packet analysis can provide a more refined analysis method for the signal. It divides the frequency band into multiple levels, performs the same further decomposition on the high-frequency part of the signal as the low-frequency part, and can adaptively analyze the signal according to the characteristics of the analyzed signal. The corresponding frequency band is selected to match the signal spectrum, thereby improving the time-frequency resolution. Each decomposition is equivalent to performing low-frequency and high-frequency filtering at the same time, further decomposing the low-frequency and high-frequency parts, and so on, so that the low-frequenc...

Embodiment 2

[0095] image 3 It is a schematic diagram of the device in Example 2 of the present invention. This embodiment provides a sensor fault diagnosis device, including:

[0096] a signal receiving unit 301, configured to receive the output signal of the sensor;

[0097] A wavelet packet decomposing unit 302, configured to decompose the output signal using a wavelet packet;

[0098] A wavelet packet transform coefficient screening unit 303, configured to filter out the wavelet packet transform coefficient with the largest magnitude, so as to retain data that can characterize fault characteristics;

[0099] A feature vector extraction unit 304, configured to perform feature extraction according to the rate of change of energy of each frequency component of the sensor, to obtain a feature vector;

[0100] The neural network judging unit 305 is used to input the feature vector into the designated neural network to obtain the sensor fault type, wherein the number of input and output ...

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Abstract

The invention embodiment discloses a fault diagnostic method and diagnostic device of a sensor. The method includes the following steps: receiving output signals of the sensor; utilizing a wavelet packet to analyze the output signals; screening the conversion coefficient of the wavelet packet with the maximum amplitude, so as to reserve the data capable of representing the fault features; performing feature extraction according to the change rate of the component energies of the frequencies of the sensor, so as to obtain feature vectors; and inputting the feature vectors into a specified neural network, so as to obtain the fault type of the sensor, wherein the node number of the input and output layers of the neural network are determined by the dimension numbers of the feature vectors and the fault type numbers of the sensor respectively, and the network weight and the threshold are determined by training through a training sample. The fault diagnostic method and diagnostic device adopt the wavelet packet to perform refinement partition of the dynamic signals of the sensor, and determine the fault type of the sensor according to the output of the neural network, so as to effectively diagnose the catastrophic fault of the sensor.

Description

technical field [0001] The invention relates to the field of sensor fault diagnosis, in particular to a sensor fault diagnosis method and a diagnostic device. Background technique [0002] Sensors composed of precision components often work in harsh environments such as high temperature, high pressure, vibration, shock, pollution, electromagnetic interference, etc., making the sensor prone to failure, causing distortion of the output signal, and affecting the accuracy, stability and reliability of signal acquisition , and then affect the performance of the entire control system, and even pose a threat to the safety of vehicles. Therefore, the fault diagnosis of sensors is an important means to improve the reliability of control systems such as vehicles. According to the manifestations of sensor faults, they can be divided into slow-change faults and sudden-change faults. The present invention only relates to the diagnosis of sudden faults in sensors. [0003] When the sens...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01D18/00
Inventor 刘信奎潘凤文文武红张洪坤陈雪丽
Owner WEICHAI POWER CO LTD
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