Theory-Driven Multi-Output Prognostics for Complex Systems Using Sparse Bayesian Learning

Complex systems often face significant challenges in both efficiency and performance when making long-term degradation predictions. To address these issues, this paper proposes a predictive architecture based on multi-output sparse probabilistic model regression. An adaptive health index (HI) extraction method was also introduced, which leverages unsupervised deep learning and variational mode decomposition to effectively extract health indicators from multiple measurements of complex systems. The effectiveness of the proposed method was validated using both the C-MAPSS and FLEA datasets. The case study results demonstrate that the proposed prognostic method delivered an outstanding performance. Specifically, the feature extraction method effectively reduced the measurement noise and produced robust HIs, while the multi-output sparse probabilistic model achieved lower prediction errors and a higher accuracy. Compared to traditional single-step forward-prediction methods, the proposed approach significantly reduced the time required for long-term predictions in complex systems, thus improving support for online status monitoring.