Remaining Useful Life Prediction of High-Dimensional Kernel Density Estimation with Adaptive Relative Density Window Width Considering Multi-Source Information Fusion

For the remaining useful life prediction of complex systems, some data in a large amount of multi-sensor monitoring data do not effectively characterize the degradation of complex systems, while there is redundancy between sensor data, which leads to low accuracy of prediction results. Therefore, A high-dimensional kernel density estimation remaining useful life-prediction method was proposed with an adaptive relative density window width based on multi-source information fusion, which contains a multi-indicator sensor-evaluation algorithm based on the entropy weight method and the maximum relevance-minimum redundancy sensor selection algorithm. First, the trendability, monotonicity, predictability and robustness are proposed to evaluate the sensor data based on the mapping relationship between the sensor data and random degradation characteristics of the system. Moreover, a comprehensive evaluation indicator is constructed using the entropy weighting method to select sensors with higher comprehensive scores, which can better characterize the system degradation. Furthermore, a maximum relevance-minimum redundancy based on mutual information is proposed to select the sensor group that has the maximum relevance to the runtime and minimum redundancy of information between multi-sensor data. Moreover, a high-dimensional kernel density estimation remaining useful life-prediction model with adaptive relative density window width is established based on feature-level information fusion. Finally, the accuracy and effectiveness of the proposed method are verified by C-MAPSS data and N-MAPSS.