Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding
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Xinkun Ai Kun Liu Wei Zheng Yonggang Fan Xinwu Wu Peilong Zhang LiYe Wang JanFeng Zhu Yuan Pan
Ball mills play a critical role in modern mining operations, making their
bearing failures a significant concern due to the potential loss of production
efficiency and economic consequences. This paper presents an anomaly detection
method based on Deep Convolutional Auto-encoding Neural Networks (DCAN) for
addressing the issue of ball mill bearing fault detection. The proposed
approach leverages vibration data collected during normal operation for
training, overcoming challenges such as labeling issues and data imbalance
often encountered in supervised learning methods. DCAN includes the modules of
convolutional feature extraction and transposed convolutional feature
reconstruction, demonstrating exceptional capabilities in signal processing and
feature extraction. Additionally, the paper describes the practical deployment
of the DCAN-based anomaly detection model for bearing fault detection,
utilizing data from the ball mill bearings of Wuhan Iron & Steel Resources
Group and fault data from NASA's bearing vibration dataset. Experimental
results validate the DCAN model's reliability in recognizing fault vibration
patterns. This method holds promise for enhancing bearing fault detection
efficiency, reducing production interruptions, and lowering maintenance costs.