A System Fault Diagnosis Method with a Reclustering Algorithm

• Published in: Scientific programming Vol. 2021; pp. 1 - 8
• Main Authors: Yang, Zhe, Ying, Shi, Wang, Bingming, Li, Yiyao, Dong, Bo, Geng, Jiangyi, Zhang, Ting
• Format: Journal Article
• Language: English
• Published: New York Hindawi 09.03.2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Clustering; Failure; Fault diagnosis; Machine learning; Neural networks; Semantics; Software; Supercomputers
• ISSN: 1058-9244; 1875-919X; 1875-919X
• DOI: 10.1155/2021/6617882

The log analysis-based system fault diagnosis method can help engineers analyze the fault events generated by the system. The K-means algorithm can perform log analysis well and does not require a lot of prior knowledge, but the K-means-based system fault diagnosis method needs to be improved in both efficiency and accuracy. To solve this problem, we propose a system fault diagnosis method based on a reclustering algorithm. First, we propose a log vectorization method based on the PV-DM language model to obtain low-dimensional log vectors which can provide effective data support for the subsequent fault diagnosis; then, we improve the K-means algorithm and make the effect of K-means algorithm based log clustering; finally, we propose a reclustering method based on keywords’ extraction to improve the accuracy of fault diagnosis. We use system log data generated by two supercomputers to verify our method. The experimental results show that compared with the traditional K-means method, our method can improve the accuracy of fault diagnosis while ensuring the efficiency of fault diagnosis.