Abstract
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.
Highlights
At present, the sensor is widely used in various processes to obtain a variety of physical quantity of data
Permutation entropy is a time series complexity measure based on comparison of neighborhood values and the numerical mapping into symbol sequence pattern
The MWPE on one single scale is not enough to describe complexity of the signal, and the multiscale weighted permutation entropy is more suitable for the analysis of all kinds of actual signals
Summary
The sensor is widely used in various processes to obtain a variety of physical quantity of data. Permutation entropy is a time series complexity measure based on comparison of neighborhood values and the numerical mapping into symbol sequence pattern. It can describe the local structure features of time series signal and enlarge the subtle changes in the signal with low complexity and antinoise ability. Paper [13] puts forward the weighted permutation entropy (WPE) It extracts the sequential pattern of time series and retains the amplitude information of time series. A wavelet based multiscale weighted permutation entropy (WMWPE) is proposed in this paper.
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