A multi-sensor fusion-based prognostic model for systems with partially observable failure modes

AbstractWith the rapid development of sensor and communication technology, multi-sensor data is available to monitor the degradation of complex systems and predict the failure modes. However, two huge challenges remain to be resolved: (i) how to predict the failure modes with limited failure mode labeled systems to alleviate the heavy dependence on expert experience; (ii) how to effectively fuze the useful information from the multi-sensor data to achieve an accurate estimation of the degradation status automatically. To address these issues, we propose a novel semi-supervised prognostic model for the systems with partially observable failure modes, where only a small fraction of the systems in the training set are known for their failure modes. First, we develop a graph-based semi-supervised learning method to extract features characterizing the failure modes. Then, we input these features as well as the multi-sensor streams into an elastic net functional regression model to predict the residual useful lifetime. The proposed model is validated by extensive simulation studies and a case study of aircraft turbofan engines available from the NASA repository.Keywords: Data fusionfailure modesfunctional principal components analysissemi-supervised learning AcknowledgmentsThe authors would like to thank the editors and referees for their many constructive and insightful comments, which have promoted significant improvements of this article.Additional informationFundingThe work described in this paper was supported by National Natural Science Foundation of China (No.71731008). Notes on contributorsHui WuHui Wu is an associate professor with the School of Economics and Management, Harbin Institute of Technology, Weihai, China. She received a BS degree in statistics from Shandong University, Jinan, China, in 2018, and a PhD degree in management science and engineering from Tsinghua University, Beijing, China, in 2022. Her current research focuses on developing statistical learning and artificial intelligence methods for large-scale complex system modeling, online monitoring, anomaly detection, and reliability analysis.Yan-Fu LiYan-Fu Li is a professor with Department of Industrial Engineering, Tsinghua University, Beijing, China. He received a BS degree in software engineering from Wuhan University, China in 2005, and a PhD degree in industrial and systems engineering from the National University of Singapore in 2010. His current research interests include RAMS (reliability, availability, maintainability, safety) assessment and optimization with the applications onto various industrial systems.