Remote equipment health prediction method based on machine learning and edge computing

Abstract

The invention discloses a remote equipment health prediction method based on machine learning and edge computing, and the method comprises the following steps: deploying an edge computing terminal integrating computing, storage, network and application core capabilities on the edge side close to equipment or a data source, realizing connection with production equipment in combination with a communication module, and acquiring operation element data of the production equipment in real time; providing intelligent data analysis services nearby; employing the intelligent analysis model based on machine learning for completing cleaning and preliminary analysis processing of a large amount of real-time data, triggering possible analysis service response according to a deployed prediction mode strategy, and uploading an analysis result to the cloud; and then completing comprehensive analysis and prediction of the data through the cloud intelligent model. The cloud management architecture realizes efficient management of the unattended equipment terminal, and the distributed attribute of the edge computing effectively reduces the data processing load of the cloud platform and ensures the data security at the same time. Operation health state management and fault prediction of remote equipment are effectively realized.

Description

technical field [0001] The present invention relates to a remote device health prediction method, in particular to a remote device health prediction method based on machine learning and edge computing. Background technique [0002] At present, with the development of modern industrial manufacturing, a large number of industrial production equipment are scattered in different geographical areas. After large-scale deployment of equipment, the problems of equipment operation and maintenance and centralized management also arise. With the increase in the number of equipment deployed, the maintenance cost increases sharply, and the management efficiency is greatly reduced. In addition, due to the lack of remote management methods, abnormalities in equipment operation cannot be detected early, and sudden failures often have a major impact on production activities, causing major property losses and production safety accidents. [0003] The current remote maintenance methods mainly...

AI Technical Summary

Problems solved by technology

With the increase in the number of equipment deployed, the maintenance cost increases sharply, and the management efficiency is greatly reduced

In addition, due to the lack of remote management methods, abnormalities in equipment operation cannot be detected early, and sudden failures often have a major impact on production activities, causing major property losses and production safety accidents

[0003] The current remote maintenance methods mainly use remote network technology to monitor the current operating status of the equipment. They can only judge the operating status of the equipment based on changes in real-time data, and cannot predict the health status of the equipment in a timely and effective manner. Unplanned downtime and catastrophic failures cannot be avoided. After the occurrence of the event, the maintenance cost has not been substantially reduced

Traditional remote equipment maintenance does not provide early warning of possible problems and avoid serious impacts on production due to equipment life expiration and sudden failures

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