Method for realizing fault detection by using sequential clustering algorithm

Abstract

The invention belongs to the technical field of IT operation and maintenance and machine learning, and particularly relates to a method for realizing fault detection by using a time sequence clustering algorithm. The method comprises the following steps of acquiring the equipment performance index information according to a preset time frequency to obtain the time sequence data; normalizing the time series data; performing clustering analysis on the normalized time series data by using a DBSCAN algorithm, and calculating an abnormal value score of the clustered time series; and judging whethera fault exists or not according to whether the abnormal value score exceeds a set threshold value or not. According to the method for realizing the fault detection by using the sequential clusteringalgorithm, the DBSCAN algorithm is used for carrying out clustering analysis on the equipment time sequence data, and whether the equipment performance state is stable or not is judged by analyzing the difference value between all performance data indexes, so that the equipment operation health degree is measured, and the detection efficiency and accuracy can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of IT operation and maintenance and machine learning, and in particular relates to a method for realizing fault detection by using a time series clustering algorithm. Background technique [0002] With the development of the information age, IT operation and maintenance has become an important part of the connotation of IT services. In the face of increasingly complex businesses and increasingly diverse user needs, ever-expanding IT applications require more and more reasonable models to ensure that IT services can be continuously guaranteed in a flexible, convenient, safe and stable manner. The most important guarantee factor is IT operation and maintenance. From the initial few servers to a huge data center, manpower alone can no longer meet the requirements in terms of technology, business, management, etc., so standardization, automation, architecture optimization, process optimization and other factors...

AI Technical Summary

Problems solved by technology

From the initial few servers to a huge data center, manpower alone can no longer meet the requirements in terms of technology, business, management, etc., so standardization, automation, architecture optimization, process optimization and other factors to reduce IT service costs are becoming more and more valued by people

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