Abstract
With the rising complexity of deteriorating systems and availability of advanced sensors, the need for more robust and reliable methods for condition monitoring and dynamic maintenance decision-making has significantly increased. To generate more reliable results for reliability analysis of complex systems, we propose a new generative framework for failure prognosis utilizing a hybrid state-space model (SSM) that represents the evolution of the system's operating condition and its degradation over time. The proposed model can employ a set of real-time sensor measurements to: 1) diagnose the hidden degradation level of the system and 2) predict the likelihood and the uncertainty of failure without imposing unrealistic heavy distributional assumptions. We provide analytical results for the prediction and update steps of the associated particle filter, as well as for the estimation of model parameters. A single-layer feed-forward neural network (Extreme Learning Machine) was used to model the nonparametric relationship between the multi-dimensional observation process and the rest of system's dynamics. We demonstrate the application of our framework through numerical experiments on a set of simulated data and a turbofan engine degradation data set. Note to Practitioners-The prognosis of the future health status in degrading systems using sensor data generally requires many distributional assumptions, such as a parametric relationship between the hidden degradation level and sensor measurements, and a predefined degradation threshold. This paper proposes a new model that formulates the relationship between degradation level, sensor measurements, and operating conditions in a multi-layer generative manner that helps accommodate interpretability and uncertainty. Results obtained utilizing simulated and real-life data prove that the developed method can yield reasonable prognostic estimates for important measures, such as the remaining useful life of the system.
