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Sensor fault signal feature extraction method based on wavelet analysis

A technology for sensor faults and signal characteristics, applied to instruments, character and pattern recognition, computer components, etc., can solve problems such as weak ability to analyze non-stationary signals and immutable time-frequency windows, so as to reduce information redundancy and improve general-purpose Sexuality, the effect of improving effectiveness

Pending Publication Date: 2022-04-15
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0004] Purpose of the invention: In view of the problems existing in the above-mentioned background technology, the present invention provides a feature extraction method of sensor fault signals based on wavelet analysis, which solves the problems of weak ability of analyzing non-stationary signals and immutable time-frequency window of traditional feature extraction methods

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  • Sensor fault signal feature extraction method based on wavelet analysis
  • Sensor fault signal feature extraction method based on wavelet analysis
  • Sensor fault signal feature extraction method based on wavelet analysis

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Embodiment Construction

[0037] The present invention will be further described below in conjunction with the accompanying drawings. Apparently, the described embodiments are some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0038] A specific embodiment is provided below to illustrate the sensor fault signal feature extraction method based on wavelet analysis provided by the present invention, such as figure 1 shown.

[0039] Step S1, collect the sensor output signal data through the NI data acquisition device, perform data injection on the sensor output signal data based on the typical sensor fault model, and obtain the sensor fault signal data.

[0040] Step S2, performing dimensionless preprocessing on the sensor fault signal data. In this embodiment, the Standardization method i...

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Abstract

The invention discloses a sensor fault signal feature extraction method based on wavelet analysis, and the method comprises the steps: firstly obtaining sensor fault signal data, and carrying out the dimensionless preprocessing; carrying out N-layer wavelet decomposition on the dimensionless sensor fault signal by adopting Daubechies wavelet, and storing a wavelet decomposition coefficient; respectively carrying out modulus maximum feature extraction processing and high-frequency relative wavelet energy feature extraction processing on the wavelet decomposition coefficient to obtain a modulus maximum feature vector and a high-frequency relative wavelet energy feature vector, and finally forming a sensor fault signal feature matrix; according to the sensor fault signal feature extraction method provided by the invention, wavelet transform is only carried out on some discrete points on wavelet time and scale planes by adopting multi-scale one-dimensional wavelet decomposition, so that information redundancy is reduced, the feature extraction speed is improved, time domain and frequency domain features of fault data can be obtained at the same time, and the feature stability is enhanced; and the validity of the characteristic parameters and the reliability of subsequent fault identification are improved.

Description

technical field [0001] The invention relates to the technical field of sensor fault diagnosis, and mainly relates to a wavelet analysis-based feature extraction method for sensor fault signals. Background technique [0002] With the popularization of automation and intelligence in all walks of life, the application range of intelligent systems is becoming wider and wider. A large number of sensors are used in intelligent systems as components to obtain system operating data. Once a sensor fails, it will have a negative impact on the normal operation of the system. Due to the sensor's working environment, its own characteristics and other reasons, the sensor is prone to various failures, some failures will have serious consequences, and major safety accidents caused by sensor failures occur every year. According to the research of the Intelligent Maintenance System (IMS) Center in the United States, among the failures of the intelligent system, the failure of the sensor itse...

Claims

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Application Information

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IPC IPC(8): G06K9/00
Inventor 李子凡叶志锋王彬肖玲斐
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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