Excavating diagnosis method of failure data based on bayesian network

Abstract

Disclosed is an excavating diagnosis method of failure data based on a bayesian network. The method is used for diagnosis of equipment failures. Parameter points, capable of being obtained, of existing devices with failures are monitored in advance, and a monitoring signal and a failure type are used as input variables of the bayesian network; then, nodes are sorted at random, side adding or changing operation is carried out in the search problem, the generated bayesian network is graded and stored in a database, finally, the bayesian network with the highest score in the database is used as an equipment failure diagnosis inference graph, equipment data needed to be diagnosed are input into the diagnosis inference graph, and the diagnosis result can be obtained. The search density can be set according to the actual condition, and can be prevented from being caught in endless loop; a penalty term can be adjusted so as to prevent excessive fitting; an initial sequence is generated at random to enable the system to have a certain roughness; a user can see the equipment failure diagnosis inference graph, and therefore logic analysis can be carried out on the failure happening mechanism.

Description

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Login to view more

AI Technical Summary

Benefits of technology

This patented technology helps identify failures quickly by analyzing device performance based upon its previous symptoms before they happen. It also ranks devices for better maintenance planning accordingly. By comparing different parameters points taken during testing against their expected values, it generates a more accurate model than just looking at all possible causes rather than trying everything out manually. Additionally, this method allows users to customize how many times new hardware components will perform poorly without causing any problems later due to these issues being discovered earlier. Overall, this technology improves efficiency and accuracy in predictive asset management systems (PMS).

Problems solved by technology

This patents describes how machines work together with other components that may have similar characteristics but behave differently when they break down due to various causes like damage. However, this understanding helps predict future machine issues beforehand without having to manually inspect each component individually.

Images