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

Abstract EN

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.