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

Abstract

With 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.