Industrial carrying equipment fault detection method and system based on cloud-side cooperation

By adopting a cloud-edge collaboration-based fault detection method on industrial carrier equipment, and using the collaborative work of edge-end and cloud-end equipment, the problems of low detection frequency and lagging response in traditional maintenance methods are solved, real-time monitoring and fault warning of industrial carrier equipment are achieved, and timely and accuracy of detection is improved.

CN119694343AInactive Publication Date: 2025-03-25SUN YAT SEN UNIV
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Patent Information

Application Number
CN202411812006.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-10
Publication Date
2025-03-25
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Traditional industrial carrying equipment maintenance has problems such as low detection frequency, lagging response, and high labor costs, making it difficult to detect potential faults in a timely manner.

Method used

The fault detection method of industrial carrier equipment based on cloud-edge collaboration is adopted, and audio data is obtained and preprocessed through edge devices, and the benchmark model is trained using cloud devices and set abnormal judgment thresholds to perform lightweight processing, ultimately real-time monitoring and fault warning of industrial carrier equipment.

Benefits of technology

It improves the timeliness and efficiency of fault detection, reduces equipment cost and maintenance difficulty, can fully identify various types of faults, and improves the accuracy and reliability of detection.

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Abstract

The invention discloses an industrial carrying equipment fault detection method and system based on cloud edge collaboration, and relates to the technical field of data processing, and the method comprises the steps: edge end equipment obtains initial audio data collected from industrial carrying equipment; preprocessing the initial audio data into standard audio data; performing gain enhancement on the standard audio data so as to extract audio features; the cloud device uses the audio features to train a reference model; setting an abnormal judgment threshold value for the reference model as an audio detection model; carrying out distillation lightweight on the audio detection model to obtain a quantitative detection model; the edge end equipment detects audio data acquired by the industrial carrying equipment by using the quantitative detection model; uploading the detected abnormal audio data to cloud equipment; and the cloud equipment performs fault analysis on the detected abnormal audio data. According to the invention, real-time monitoring and fault early warning of the operation state of the industrial carrying equipment are realized through cloud-side cooperation, and the timeliness and efficiency of fault detection are improved.
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