Equipment management method and system based on deep learning

Through deep learning models, the remaining service life of the device is predicted, the problem of inaccurate data in device management is solved, the availability and reliability of the device is improved, predictive maintenance and intelligent management are realized, and maintenance costs are reduced.

CN119128729APending Publication Date: 2024-12-13SHANDONG SONGGUO INFORMATION TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202411314950.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-09-20
Publication Date
2024-12-13

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Abstract

The invention discloses an equipment management method and system based on deep learning, and relates to the technical field of equipment maintenance, and the method comprises the steps: collecting equipment operation data; preprocessing the equipment operation data to obtain a sample data set; training a deep learning model by using the sample data set, wherein the deep learning model can learn a change rule of an equipment state; using the trained deep learning model to analyze the newly collected equipment operation data, and predicting the residual service life of the equipment; and performing equipment management based on the predicted residual service life of the equipment. In the specific implementation process, the equipment operation data comprises sensor data, environment data and maintenance data. According to the method, the equipment operation data are collected, and the fault occurrence time of the equipment is predicted through the deep learning model, so that predictive maintenance is realized, and the availability and reliability of the equipment are improved.
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Citation Information

Patent Citations

  • CNN-based power equipment fault judgment and early warning method, terminal and readable storage medium

    CN110110905A

  • Method for predicting residual life of equipment in industrial process

    CN113486578A

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    CN113742163A

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    CN117473864A

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    CN118625156A

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