A model-based time-to-failure prediction scheme for nonlinear systems via deterministic learning. Issue 6 (April 2020) - Wang, Qian; Wang, Cong; Sun, Qinghua
Abstract: Time-to-failure (TTF) prediction is one of the most difficult problems in the area of prognostic and health management. In this paper, a new model-based TTF prediction scheme is proposed. Based on deterministic learning theory, a system dynamical pattern bank consisting of health, sub-health and fault patterns is established, and a set of estimators associated with the learned system patterns is used to generate average L 1 norms of system residuals. Then, a TTF prediction model is derived based on the system residual generator with a predefined failure pattern. Once the first predicting time is obtained according to the incipient fault detection scheme, the system TTF can be predicted by projecting the learned fault dynamics at the current time against the failure threshold. Finally, an incipient fault detection and TTF prediction (IFDTP) algorithm is implemented by combining the established bank, the first predicting time and the TTF model. The novelty of this paper lies in that the new TTF prediction scheme can provide a more accurate system failure time for nonlinear dynamical systems, and the effectiveness of the proposed IFDTP algorithm is illustrated by simulation studies.