Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network
This addresses bearing failures in mining ball mills to reduce production interruptions and maintenance costs, but it is incremental as it applies an existing deep learning approach to a specific domain.
The paper tackles ball mill bearing fault detection by proposing a Deep Convolutional Auto-encoding Neural Network (DCAN) method, which uses vibration data for anomaly detection and is validated on real-world and NASA datasets, showing reliability in recognizing fault patterns.
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.