Equipment residual life prediction method and system based on industrial big data, and medium

Abstract

The invention discloses an industrial big data-based equipment residual life prediction method and system and a medium, and the method comprises the following steps: collecting the real-time operation data of equipment, carrying out the feature extraction and feature selection of the real-time operation data, and obtaining the data features; performing data drift detection according to the data features, and determining the health stage of the equipment; and obtaining a corresponding residual life prediction model according to the health stage, constructing a health factor according to the data features, inputting the health factor into the obtained residual life prediction model, and outputting an equipment residual life prediction result. According to the operation data generated in real time in industrial actual production, the health factor of the equipment is acquired, and the residual service life of the equipment at the current moment is predicted by using the health factor, so that the complete life cycle of the equipment can be more utilized, the service life waste and the time waste caused by sudden shutdown are reduced, the equipment health management is automatically carried out. The method can be widely applied to the field of prediction of the residual life of the equipment.

Description

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AI Technical Summary

Benefits of technology

This technology helps gather important data on how well an item works for its lifespan by continuously monitoring it's condition during operation or even when there are signs about damage caused due to factors like excessive heating or other environmental issues such as water leakage. By analyzing this data from different sources at once, we aimed towards developing better ways to manage their performance more effectively while reducing unnecessary maintenance costs associated with failure detection systems used earlier.

Problems solved by technology

This patents describes different ways how faults occur during manufacturing may affect their lifespan and reduce its cost efficiency when they cannot be detected beforehand due to lack of early detection capabilities. However, there has been no reliable way to accurately estimate future remaining usable life without relying on expensive instrumentations like temperature monitors or pressure gauges. Therefore, this problem needs to be solved through advanced techniques such as machine learning analysis and regression simulation.

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