Centrifugal machine equipment fault prediction and diagnosis method based on big data analysis

By acquiring multimodal big data of centrifuges and performing single-modal and multi-modal pre-diagnosis, and combining it with physical information neural networks for comprehensive pre-diagnosis, the problem of low accuracy in centrifuge fault prediction and diagnosis is solved, achieving more accurate fault prediction and effective maintenance.

CN120632728APending Publication Date: 2025-09-12SHANGHAI HUIDU INTELLIGENT SYST
0 Cites 0 Cited by

Patent Information

Application Number
CN202510756234.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-08
Publication Date
2025-09-12

AI Technical Summary

Technical Problem

The existing technology for predicting and diagnosing centrifuge faults has low accuracy, which results in an inability to effectively predict the failure risk of the centrifuge, affecting the safe operation and maintenance efficiency of the equipment.

Method used

By obtaining multimodal big data of the centrifuge, including single-modal and multi-modal data, the corresponding prognostic models are used to perform single-modal and multi-modal prognostics, and finally a comprehensive prognostic is performed, combined with the physical information neural network for fault prediction.

Benefits of technology

The accuracy of centrifuge fault prediction and diagnosis is improved, excessive or insufficient maintenance is avoided, the safe operation of the equipment is guaranteed, and the utilization efficiency is improved.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention belongs to the technical field of centrifugal machines, provides a centrifugal machine equipment fault prediction and diagnosis method based on big data analysis, and aims to solve the problem of low fault prediction and diagnosis accuracy of a centrifugal machine in the prior art. The multi-modal big data comprises different single-modal big data, and according to the single-modal big data, pre-breaking the running state of the centrifugal machine based on a corresponding preset single-modal pre-breaking model to obtain single-modal information data; pre-breaking the running state of the centrifugal machine according to the multi-modal big data and based on a preset multi-modal pre-breaking model to obtain a multi-modal information number; and finally, performing comprehensive pre-breaking based on a preset comprehensive pre-breaking model according to the single-mode and multi-mode information data to obtain a target pre-breaking result. According to the method, single-mode pre-disconnection and multi-mode pre-disconnection based on centrifugal machine big data are achieved respectively, then comprehensive pre-disconnection is conducted, and the accuracy of centrifugal machine equipment fault prediction and diagnosis can be improved.
Need to check novelty before this filing date? Find Prior Art