Motor Health Status Prediction Method Based on Information from Multi-sensor and Multi-feature Parameters

Health status prediction is of great significance for a motor system’s safe operation and lifecycle management. The object of this work is to achieve better information fusion performance for information obtained from X-, Y-, and Z-axial and existed in the multi-feature parameter, and therefore gain more comprehensively and effectively prediction results of health status. First, a UAV power motor is chosen as the test item to obtain the original vibration data. Then, the multi-feature parameters are fused and chosen based on quality and quantity method considering the diagnosis results and degradation path descriptive ability. Next, the health status prediction is achieved with Bayesian updating algorithm. Finally, a DS theory and information entropy weight-based granulation fusion method of multi-source health status information for the electric motor is proposed. The method can achieve the fusion of multiple prediction results obtained from multi-feature parameters to gain the optimal health status prediction result for the motor. The result is compared with actual data and also verified by information entropy. Meanwhile, according to the prediction results, its application in risk assessment and maintenance planning were discussed.