Fault prediction method, device, system and equipment

Abstract

The invention discloses a fault prediction method, a fault prediction device, a fault prediction system and fault prediction equipment, and belongs to the field of equipment management. The method is applied to a system comprising at least one functional module. The method comprises the following steps of: acquiring operating data of a current sampling time slice of the functional module; according to the operating area of the current sampling time slice and a health tendency chart of the functional module, obtaining the position of the operating area of the current sampling time slice on the health tendency chart, and according to a probability event generating area in which the position of the position of the operating area on the health tendency chart is positioned, using a health score corresponding to the probability event generating area as a health score of a current operating state of the functional module; and according to the health score, predicting the operating state of the functional module. According to the invention, when the operating state of the functional module is possibly abnormal, a system user is timely reminded, so that the breakdown of the functional module is avoided and the stability of the operating process of the system is ensured.

Description

technical field [0001] The invention relates to the field of equipment management, in particular to a fault prediction method, device, system and equipment. Background technique [0002] The minicomputer system can be divided into several functional modules according to the functions. When the minicomputer is operating, the health status of the hardware resources of each functional module affects the health status of the entire system. During the operation of the system, the health status of the hardware resources of the minicomputer functional modules will change Presents healthy, sub-healthy and unhealthy states. Healthy and unhealthy states are relatively easy to identify at present, but for sub-healthy states, existing methods are difficult to judge, and there are often two extremes, judging sub-healthy as unhealthy or simply ignoring them. If the sub-health is judged as unhealthy, once the operation of the minicomputer is stopped, the normal business operation will be ...

AI Technical Summary

Problems solved by technology

Healthy and unhealthy states are relatively easy to identify at present, but for sub-healthy states, existing methods are difficult to judge, and there are often two extremes, judging sub-healthy as unhealthy or simply ignoring them

If the sub-health is judged as unhealthy, once the operation of the minicomputer is stopped, the normal business operation will be affected. The functional modules in the minicomputer system collapse, which in turn affects the stability and performance of business operations

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