BBMG (China)

Coal mill is an important equipment in cement production line, and also the focus of personnel inspection. The inspection and maintenance of coal mills rely on the experience and system of personnel. Daily maintenance still stays in the state of post-maintenance, and lacks real-time dynamic fault risk assessment for equipment abnormalities. Aiming at this problem, a comprehensive evaluation method of mill fault risk integrating process parameters and equipment parameters is proposed. Firstly, a CNN-LSTM model based on process parameters is established to diagnose the typical faults of coal mill. Then, based on the electrical data, temperature data, vibration data, pressure data and sound data of coal mill, the health state model of coal mill equipment is established. Finally, we use the analytic hierarchy process(AHP) to comprehensively evaluate the risk of coal mill. The experiment results show that the proposed coal mill risk assessment method effectively integrates the advantages of process experience to realize real-time dynamic comprehensive assessment and fault location of coal mill risk.