Construction of Interpretable Health Indicators for System-Level Fault Prognosis Driven by Inductive Generative Learning

The construction of health indicators (HIs) is critical for the system-level fault prognosis. Due to its high interpretability and credible identifiability, the model-driven approaches are more welcome. However, for a complex system, it exists always some intercomponent coupling mechanism among model-driven HIs, which makes the fault representations difficult to distinguish based on these HIs. Therefore, this article proposes a model-driven construction method of interpretable HI driven by inductive generative learning, aiming to optimize the accuracy of the system-level fault prognosis via the decoupling among the model-driven HIs. The inductive architecture attempts to treat automatically the high coupling performance knowledge inside the mathematical model to extract the correspondent identifiable HIs, while the integrated generative architecture realizes an efficient identification of the extracted HIs in order to guarantee a real-time fault prognosis. Experiments on a simplified electrical hydrostatic actuator (EHA) model and a high-quality electromechanical actuator (EMA) model are conducted to demonstrate the state-of-the-art level of the proposed approach on the HI construction for the system-level fault prognosis under intercomponent coupling circumstances.