Remaining useful life (RUL) prediction is an important issue in prognostics and health management (PHM). Numerous studies have been conducted to construct degradation models for RUL prediction. However, their models fail to handle the scenarios where multiple failure modes exist, especially when the failure modes are unknown (unlabeled) beforehand and need to be recognized. This paper develops a multimodal recognition and prognostic method based on features extracted via multisensor degradation modeling. Specifically, we assume the failure mode of a unit follows a multinomial distribution. Given the failure mode distribution, we characterize the degradation status of the unit via degradation models based on each sensor signal and a constructed health index (HI). Our innovative idea is to extract features as the derivatives of the degradation status to comprehensively utilize information from multiple sensors for more effective failure mode recognition and RUL prediction. We develop a fusion coefficient-integrated expectation-maximization (FCIEM) algorithm to estimate model parameters by using data from historical units. Finally, we recognize the failure mode and predict the RUL of in-service units based on their extracted features and degradation status. Numerical experiments and a case study of aircraft engines were conducted to evaluate the performance of our proposed method.

Remaining useful life (RUL) prediction is an important issue in prognostics and health management (PHM). Numerous studies have been conducted to construct degradation models for RUL prediction. However, their models fail to handle the scenarios where multiple failure modes exist, especially when the failure modes are unknown (unlabeled) beforehand and need to be recognized. This paper develops a multimodal recognition and prognostic method based on features extracted via multisensor degradation modeling. Specifically, we assume the failure mode of a unit follows a multinomial distribution. Given the failure mode distribution, we characterize the degradation status of the unit via degradation models based on each sensor signal and a constructed health index (HI). Our innovative idea is to extract features as the derivatives of the degradation status to comprehensively utilize information from multiple sensors for more effective failure mode recognition and RUL prediction. We develop a fusion coefficient-integrated expectation-maximization (FCIEM) algorithm to estimate model parameters by using data from historical units. Finally, we recognize the failure mode and predict the RUL of in-service units based on their extracted features and degradation status. Numerical experiments and a case study of aircraft engines were conducted to evaluate the performance of our proposed method.