Abnormal diagnosis method and device based on pca margin space

Abstract

The embodiment of the invention provides an abnormity diagnosis method and device based on a PCA (Principle Component Analysis) residual space. The method and the device are applied in a system comprising M state variables; the values of the M state variables at a same moment form a state vector; and N state vectors in the normal working state of the system form an original space. The method comprises following steps of when it is detected that the system is abnormal, obtaining K first base vectors, wherein the K first base vectors are obtained through carrying out sparsification processing to K second base vectors, wherein the second base vectors are used for indicating the residual space obtained through carrying out PCA dimension reduction to the original space, the K is the number of dimensions of the residual space, the K is smaller than or equal to N, the number of the elements in each first base vector is M; and diagnosing the system abnormity causes according to the projections of the corresponding state vectors in the K first base vectors when the system is abnormal. According to the embodiment of the invention, because the first base vectors have sparseness, the abnormity causes can be diagnosed rapidly and effectively.

Description

technical field [0001] Embodiments of the present invention relate to information technology, and in particular to a PCA margin space-based abnormality diagnosis method and device. Background technique [0002] As computing demands grow, larger and more complex distributed computing systems are required. But more complex distributed computing systems are more prone to failure. In addition, because faults are often caused by abnormal behaviors of the system, the detection and diagnosis of abnormalities during system operation is the basis for fault detection and diagnosis. [0003] Using machine learning methods, such as anomaly detection and diagnosis based on various state quantities and logs in a distributed computing system, can effectively troubleshoot. Among them, the combination of Principal Component Analysis (Principle Component Analysis, PCA) anomaly detection algorithm and decision tree model is an effective anomaly detection and diagnosis method. Specifically, ...

AI Technical Summary

Problems solved by technology

But more complex distributed computing systems are more prone to failure

Images