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Quadratic‐wavelet‐transform‐based fault detection approach for temperature sensor
Author(s) -
Han Xiaojia,
Xu Aidong,
Wang Kai,
Guo Haifeng,
Zhang Ning,
Liu Yang,
Hong Seung Ho
Publication year - 2019
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22772
Subject(s) - wavelet , discrete wavelet transform , wavelet transform , wavelet packet decomposition , second generation wavelet transform , stationary wavelet transform , fault detection and isolation , noise (video) , filter (signal processing) , signal (programming language) , algorithm , harmonic wavelet transform , computer science , electronic engineering , artificial intelligence , engineering , computer vision , actuator , image (mathematics) , programming language
Addressing the problem of online fault detection of a temperature sensor, a fault detection algorithm based on quadratic wavelet transform is proposed in this paper. First, the discrete wavelet transform is used to extract noise from the process signal; since the process noise signal is related to the internal structure and status of the sensor, a Butterworth low‐pass filter is used to filter out the high‐frequency electrical interference signal to obtain the process noise signal. Second, the continuous wavelet transform is used to detect abrupt fault from the process noise signal. Experimental results show that the noise‐analysis‐based fault detection algorithm (quadratic wavelet transform) is better than the process‐signal‐analysis‐based approach and that the quadratic wavelet transform is feasible for thermowell drop fault detection of the temperature sensor. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.