A method of anomaly detection and fault diagnosis with online adaptive learning under small training samples

作者：

Highlights：

• This algorithm can solve anomaly detection and fault diagnosis problems at the same time.

• This algorithm has the ability of online adaptive learning under small samples during testing stage.

• This algorithm has classification function and clustering function at the same time.

• This algorithm categorizes the known type samples and clusters the unknown type samples.

摘要

•This algorithm can solve anomaly detection and fault diagnosis problems at the same time.•This algorithm has the ability of online adaptive learning under small samples during testing stage.•This algorithm has classification function and clustering function at the same time.•This algorithm categorizes the known type samples and clusters the unknown type samples.

论文关键词：Artificial immune system,Anomaly detection,Fault diagnosis,Classification,Clustering

论文评审过程：Received 12 June 2015, Revised 24 May 2016, Accepted 29 November 2016, Available online 30 November 2016, Version of Record 8 December 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.11.026