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Journal of Sensors
Volume 2016, Article ID 9693651, 9 pages
Research Article

A Wavelet Based Multiscale Weighted Permutation Entropy Method for Sensor Fault Feature Extraction and Identification

College of Information Science & Technology, Beijing University of Chemical Technology, Beijing City Chaoyang District North Third Ring Road 15, Beijing 100029, China

Received 20 February 2016; Revised 5 May 2016; Accepted 8 May 2016

Academic Editor: Fanli Meng

Copyright © 2016 Qiaoning Yang and Jianlin Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Sensor is the core module in signal perception and measurement applications. Due to the harsh external environment, aging, and so forth, sensor easily causes failure and unreliability. In this paper, three kinds of common faults of single sensor, bias, drift, and stuck-at, are investigated. And a fault diagnosis method based on wavelet permutation entropy is proposed. It takes advantage of the multiresolution ability of wavelet and the internal structure complexity measure of permutation entropy to extract fault feature. Multicluster feature selection (MCFS) is used to reduce the dimension of feature vector, and a three-layer back-propagation neural network classifier is designed for fault recognition. The experimental results show that the proposed method can effectively identify the different sensor faults and has good classification and recognition performance.