基于OCKELM与增量学习的在线故障检测方法

doi:10.7527/S1000-6893.2021.25121

Abstract

Abstract: To solve the problems of less samples for avionics equipment fault detection and lack of online real-time detection theory, the One Class Kernel Extreme Learning Machine (OCKELM) is combined with the data driven method of Incremental Learning (IL) to realize online sequential fault detection. Based on the sample data in the normal state, the kernel form of OCKELM is given, and the expressions for the kernel function and kernel weight vector are deduced. According to the incremental learning method, the kernel weight vector is updated and the sample output value is estimated when new samples are absorbed. Finally, based on two kinds of threshold criteria, the inspection threshold of the model is given, and online fault detection of the test samples is carried out. The proposed method is applied to the test data of UCI data set and an avionics device. The experimental results show that the method can realize online detection. Compared with the existing SVDD, PCA and OC-SVM methods, the proposed method has better performance in terms of F1, AUC, G-mean, fault detection rate, and other performance indicators.

Key words: one class classification, kernel extreme learning, incremental learning, online detection, fault detection

CLC Number:

V243
TP273

DAI Jinling, XU Aiqiang, SHEN Jiangjiang, WANG Shuyou. Online fault detection method based on incremental learning and OCKELM[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(3): 325121-325121.

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Online fault detection method based on incremental learning and OCKELM

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