Prejudgment analysis method for equipment defects

Abstract

A pre-judgment analysis method for equipment defects comprises the following steps: completing fixed asset registration ledger, and classifying and marking equipment in the fixed asset registration ledger; an equipment ledger information input management module; screening and detecting equipment defects; screening out the state quantity of which the value of the effective detection efficiency is greater than a preset threshold value as a concerned state quantity; the processing module sets a plan according to the attention state quantity of each device in the device operation and maintenance management module; and the management monitoring module is used for managing equipment deviation and defects. Omnibearing machine account automatic monitoring and management are carried out on power grid equipment defects, online defect elimination assignment and auditing can be carried out, the equipment operation and maintenance defect elimination efficiency is improved, performance data of equipment are comprehensively monitored, accurate early warning of the equipment defects is achieved, accidents are effectively restrained, early discovery, early prevention and early processing can be carried out, and the system is high in practicability and easy to popularize. Therefore, further expansion of accidents is prevented, the effect of emergency rescue work is improved, and accident loss is reduced.

Description

technical field [0001] The invention relates to the technical field of power equipment security, in particular to a predictive analysis method for equipment defects. Background technique [0002] With the current improvement of enterprise security awareness, in order to reduce production responsibility and reduce the probability of production liability accidents, enterprises have paid more attention to equipment safety issues. The installed amount of equipment has grown exponentially, and the condition-based maintenance work based on routine power failure routine tests has been difficult to meet the requirements of the development of the power grid. The elimination of possible defects in the equipment connected to the power grid needs to be monitored from regular monitoring. The idea is changed to a dynamic supervision idea, real-time monitoring and early warning of equipment, and the establishment of a dynamic supervision and monitoring and early warning system for equipmen...

AI Technical Summary

Problems solved by technology

[0002] With the current improvement of corporate security awareness, in order to reduce production responsibilities and reduce the probability of production-related accidents, companies have increased their attention to equipment safety issues, especially the rapid development of national power, and the growth rate of national grid expansion is obvious. Connected to the grid The amount of equipment installed and used has grown exponentially. The conventional condition-based maintenance work based on routine power outage tests has been difficult to meet the requirements of power grid development. The elimination of possible defects in equipment connected to the power grid needs to be monitored regularly. The idea is changed to a dynamic supervision idea, real-time monitoring and early warning of equipment, and construction of a dynamic supervision and monitoring and early warning system for equipment defects, which can realize early detection, early reporting, and early control of hidden dangers, which is an effective way to prevent corporate safety accidents and reduce economic losses. It can cooperate with the increasingly mature uninterrupted detection technology such as live detection and online monitoring, build and continuously improve the condition maintenance work system based on uninterrupted detection technology, and use the existing 5G mobile communication technology, artificial intelligence, big data analysis And other new technologies, comprehensively improve the ability to discover hidden dangers of equipment defects and the level of intelligent diagnosis and decision-making, improve the scientificity, pertinence and effectiveness of equipment maintenance, reduce the risk of power outage on-site operations and operation and maintenance costs, and ensure the intrinsic safety of equipment and the safe and stable operation of large power grids , is of great significance