Sensor Fault Diagnosis Method Based on  α -Grey Wolf Optimization-Support Vector Machine

Xuezhen Cheng,Jiming Li,Chuannuo Xu,Dafei Wang,Radu-Emil Precup

doi:10.1155/2021/1956394

Abstract

Aimed to address the low diagnostic accuracy caused by the similar data distribution of sensor partial faults, a sensor fault diagnosis method is proposed on the basis of α Grey Wolf Optimization Support Vector Machine (α-GWO-SVM) in this paper. Firstly, a fusion with Kernel Principal Component Analysis (KPCA) and time-domain parameters is performed to carry out the feature extraction and dimensionality reduction for fault data. Then, an improved Grey Wolf Optimization (GWO) algorithm is applied to enhance its global search capability while speeding up the convergence, for the purpose of further optimizing the parameters of SVM. Finally, the experimental results are obtained to suggest that the proposed method performs better in optimization than the other intelligent diagnosis algorithms based on SVM, which improves the accuracy of fault diagnosis effectively.

Highlights

Due to the similar distribution of some fault data, it is necessary to train a variety of classifiers for the accurate classification of different faults
Multiple time-domain parameters are extracted from sensor fault data, and the Kernel Principal Component Analysis (KPCA) is conducted to perform Principal Component Analysis of the time-domain parameters. en, some of the time-domain parameters are refused to obtain the fusion features that can accurately reflect the characteristics of fault
There is still room for improvement in terms of the search strategy for the GWO [24, 25]. erefore, an improvement is made to the proposed α Grey Wolf Optimization (α-GWO) algorithm as follows. e wolf pack is still divided into four levels, while default α, β, and δ wolves have strong search capability

Summary

Introduction

Due to the similar distribution of some fault data, it is necessary to train a variety of classifiers for the accurate classification of different faults. It is an effective strategy to improve the accuracy of diagnosis by adopting an appropriate method for extracting the feature of fault data.

Results

Conclusion