Unsupervised anomaly detection in shearers via autoencoder networks and multi-scale correlation matrix reconstruction
Yang Song1
Weidong Wang2
Yuxin Wu1
Yuhan Fan1
Xuan Zhao1
1.China University of Mining&Technology-Beijing,Beijing 100083,China2.China University of Mining&Technology-Beijing,Beijing 100083,China;School of Chemical&Environmental Engineering,China University of Mining&Technology-Beijing,No.11 Xueyuan Rd.,Haidian District,Beijing 100083,China
摘要：As the main equipment of coal mining production,the anomaly detection of shearer is important to ensure production efficiency and coal mine safety.One key challenge lies in the limited or even absence of labeled monitoring data for the equipment,coupled with the high costs associated with manual annotation.Another challenge stems from the complex structure of the mining machines,making it difficult to reflect the overall operational state through local anomaly detec-tion.Consequently,the application of decoupled local anomaly detection for mining machines in practical production remains challenging.This paper presents an unsupervised learning-based method for detecting anomalies in shearer.The method includes a module for constructing a Multi-scale Correlation Matrix(MSCM)of mining machine operating condi-tions,as well as the CNN-ConvLSTM Autoencoder(C-CLA)network.The module for constructing an MSCM enhances the representation of interrelationships between various features of the equipment from different perspectives using mul-tiple correlation analysis methods.The C-CLA network integrates convolutional and convolutional recurrent neural net-works,with the convolutional structure extracting local spatial features and the ConvLSTM structure further capturing information from different time scales and feature scales,thereby enhancing the model's perceptual capabilities towards changes in equipment status.Finally,shearer anomaly detection is achieved through the analysis of reconstructed residual matrices.The rationality and practicality of the proposed method have been validated on our dataset,and the model's generalization capability has been verified through repeated experiments in similar scenarios.However,due to variations in the working environment of different mining faces and differences in equipment models,implementing detection on other mining faces often requires retraining the model with new data.Furthermore,we compared our method with other anomaly detection techniques,and our detection efficiency was superior by approximately 3％.This method effectively detects anomalies in the shearer.
机标关键词：autoencodercorrelationmatrixdetectionanomalymulti-scalenetworksreconstruction
论文发表日期：2024-12-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：11( 54-64 )
英文信息