Data processing method based on predictive maintenance of industrial equipment

Abstract

The embodiment of the invention provides a data processing method based on the predictive maintenance of industrial equipment. The data processing method comprises the following steps: establishing a Hadoop framework-based storage platform, wherein the Hadoop framework-based storage platform comprises an HBase database for storing mass equipment data; establishing an R language-based analysis platform, wherein the R language-based analysis platform is used for analyzing the equipment data in the HBase database; and creating a Hadoop interface program, wherein the Hadoop interface program is used for providing a port for storing business data into the HBase database of the Hadoop framework-based storage platform in a specified format and enabling the R language-based analysis platform to read the equipment data in the Hadoop framework-based storage platform. According to the technical scheme, a data analysis technique based on a Hadoop+R language platform is provided.

Description

technical field [0001] The invention belongs to the field of information technology, in particular to a data processing method based on predictive maintenance of industrial equipment. Background technique [0002] In recent years, predictive maintenance strategies have gained increasing attention from plant facility managers. The predictive maintenance strategy is to monitor and analyze the operating status of equipment online, in order to find signs of equipment performance degradation or failure at an early stage, and give executable suggestions for handling measures, and notify production line maintenance personnel to perform maintenance or troubleshoot. Since predictive maintenance usually checks the status of the equipment online, it will not interfere with the normal operation of the equipment; at the same time, it can realize on-demand maintenance, which can not only reduce downtime, increase production, but also eliminate unnecessary Time and resources spent on main...

AI Technical Summary

Problems solved by technology

[0005] 1. The data storage pressure is high, and the historical data of the equipment cannot be saved for a long time, and only a small amount of recent data can be saved, which affects the accuracy of analysis and prediction;

[0006] 2. The storage bottleneck will also lead to low data analysis efficiency, which obviously cannot meet the needs of accurate prediction

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