A generic framework for multisensor degradation modeling based on supervised classification and failure surface

In condition monitoring, multiple sensors are widely used to simultaneously collect measurements from the same unit to estimate the degradation status and predict the remaining useful life. In this article, we propose a generic framework for multisensor degradation modeling, which can be viewed as an extension of the degradation models from one-dimensional space to multi-dimensional space. Specifically, we model each sensor signal based on random-effect models and characterize failure events by a multi-dimensional failure surface, which is an extension of the conventional definition of the failure threshold for a single sensor signal. To overcome the challenges in estimating the failure surface, we transform the degradation modeling problem into a supervised classification problem, where a variety of classifiers can be incorporated to estimate the degradation status of the unit based on the underlying signal paths, i.e., the collected sensor signals after removing the noise. As a result, the proposed method gains great flexibility. It can also be used for sensor selection, can handle asynchronous sensor signals, and is easy to implement in practice. Simulation studies and a case study on the degradation of aircraft engines are conducted to evaluate the performance of the proposed framework in parameter estimation and prognosis.