Degradation index construction and learning-based prognostics for stochastically deteriorating feedback control systems

Degradation-based prognostics is crucial for the health management of technological systems. In this work, we are interested in the degradation index construction and remaining useful life prognostics for stochastically deteriorating feedback control systems. The main challenges reside in the lack of knowledge about the system structure and the latent internal damage, as well as in the fault tolerance nature of feedback control systems. Our solution is to consider the whole system as a black-box, and use its easy-to-observe reference input/time response output to estimate the system transfer function. The associated H∞ norm, also called maximum energy gain, is defined as a system degradation index. Since the system fault tolerance does not allow to efficiently model the index evolution by common stochastic processes, traditional prognostics based on degradation processes are no longer applicable. To remedy, we propose to fit the system remaining useful life population to the versatile Birnbaum–Saunders distribution, and adopt a segmenting piecewise polynomials algorithm to learn the mapping between the distribution parameters and degradation index from degradation and failure data of similar systems. By this way, the remaining useful life distribution of deteriorating feedback control systems can be predicted in real-time given the system input/output at an inspection time. We numerically experiment our method on a stabilization loop control device driven by proportional–integral–differential controller in an inertial platform. Numerous sensitivity results under various configurations of system characteristics and training data corroborate the outperformance of proposed degradation index and the learning-based prognostics method.