인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract 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 detection. 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 conditions, 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 multiple correlation analysis methods. The C-CLA network integrates convolutional and convolutional recurrent neural networks, 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.
#Autoencoder
#Anomaly detection
#Scale (ratio)
#Matrix (chemical analysis)
#Geology
#Pattern recognition (psychology)
#Correlation
#Atomic units
#Anomaly (physics)
#Artificial intelligence
#Computer science
#Artificial neural network
#Mathematics
#Chemistry
#Cartography
#Physics
#Geography
#Condensed matter physics
#Geometry
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