Intelligent identification method and device for poor lubrication of equipment for equipment integrity management

By combining dynamic wavelet packet denoising and multi-scale convolution modules with the spatiotemporal attention mechanism, the recognition deficiencies of traditional equipment fault detection methods under complex working conditions are solved, efficient and accurate identification of poor lubrication is achieved, and the intelligence and real-time performance of equipment operation and maintenance are improved.

CN120744587AActive Publication Date: 2025-10-03SHANDONG ENERGY DIGITAL CLOUD TECH CO LTD
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Patent Information

Application Number
CN202511195501.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-26
Publication Date
2025-10-03
Estimated Expiration
2045-08-26

AI Technical Summary

Technical Problem

Traditional equipment fault detection methods rely on manual inspections and fixed thresholds, making it difficult to accurately identify faults such as insufficient lubrication under complex working conditions. This leads to delayed responses and inaccurate fault identification, and cannot meet the modern coal mine's needs for real-time, intelligent, and precise operation and maintenance.

Method used

Dynamic wavelet packet denoising and multi-scale convolution module are combined with spatiotemporal attention mechanism to filter out non-steady-state noise and retain the high-frequency transient impact and harmonic characteristics unique to poor lubrication. Key features are enhanced and fused through multi-scale convolution module and spatiotemporal attention weighted module, and accurate recognition is achieved in combination with classifier.

Benefits of technology

It achieves efficient identification of poor lubrication under complex working conditions, avoids false filtering of high-frequency fault signals, provides high-quality data support, improves the accuracy and response speed of fault identification, and reduces repeated training costs.

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Abstract

The invention provides an equipment integrity management-oriented equipment poor lubrication intelligent identification method and device, and relates to the technical field of signal processing, and the method comprises the steps: obtaining a vibration signal of coal mine key rotating equipment, inputting the vibration signal into a preprocessing model, and carrying out the noise reduction and standardization processing, and obtaining a standardized signal; wherein the preprocessing model is used for carrying out noise reduction processing on the vibration signal based on a self-adaptive noise reduction strategy of dynamic wavelet packet decomposition; inputting the standardized signal into a state recognition model to obtain the lubrication state of the coal mine key rotating equipment; the state recognition model comprises a multi-scale convolution module, a space-time attention weighting module, a feature fusion module and a classifier; and the state recognition model is used for performing selective enhancement and fusion on the multi-scale signal features of the standardized signals in the time domain and the frequency domain, and performing classification based on the fused signal features to obtain the lubrication state of the coal mine key rotating equipment. The embodiment of the invention can improve the model stability and the state recognition accuracy.
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