A Characterization Model for Vibration Hazards in Mining Equipment Based on Time-Frequency Mask and Sparse Representation
Journal Title: Journal of Industrial Intelligence - Year 2024, Vol 2, Issue 4
Abstract
A wide range of safety hazards exist in underground coal mines, characterized by unpredictability, randomness, and coupling effects. The increasing structural complexity and diversity of underground equipment present new challenges for fault state monitoring and diagnosis. To address the unique characteristics of underground equipment fault diagnosis, a characterization model of vibration hazards was proposed, integrating a time-frequency mask-based non-stationary filtering technique and sparse representation. Experimental analysis demonstrates that the time-frequency mask algorithm effectively filters out sharp non-stationary noise, restoring the original stationary healthy signal. Compared to Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Principal Component Analysis (PCA), the sparse representation algorithm exhibits superior performance in characterizing vibration hazards, achieving the highest accuracy.
Authors and Affiliations
Yunbo Li, Hongling Peng
Keywords
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- EP ID EP768669
- DOI 10.56578/jii020402
- Views 7
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