The residual useful lifetime prediction of complex engineering systems operating under dynamic multi-operational conditions is crucial yet challenging. One of the key challenges is that sensing signals consist of both degradation information that resulted from the physical deterioration of the system and amplitude jumps due to the switch of operational conditions. To accurately predict the failure times, we need to separate the degradation information from sensing signals and exclude jumps. Another challenge stems from the fact that complex engineering systems are typically monitored by multiple sensors that generate multi-stream sensing signals but not all the signals are informative for failure time prediction. Therefore, a systematical sensor selection method needs to be developed to identify informative sensors. The third challenge is that signals from the informative sensors are high-dimensional and may provide redundant information. As a result, a data fusion method is needed to fuse the multi-stream signals from the informative sensors. To address the aforementioned challenges, this paper focuses on developing a prognostic framework for systems that operate under dynamic multiple operational conditions. The framework will first extract the degradation signal from each sensor by removing the jump information resulted from the change of operational conditions. Next, informative sensors are selected using a sensor selection method. Finally, the degradation signals from the selected sensors are fused to predict the failure time of a partially degraded system. The effectiveness of the proposed prognostic framework is validated using a degradation data set of aircraft turbofan engines from NASA repository.

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