From Online Systems Modeling to Fault Detection for a Class of Unknown High-Dimensional Distributed Parameter Systems

Fault detection for distributed parameter systems (DPSs) reported so far is model based in general, and the performance heavily relies on the prior known model information. This restricts the usability of these methods in industrial applications. In this article, we make the first attempt to establish a brand-new framework that contains both online systems modeling and the fault detection of unknown high-dimensional DPSs. These two parts interact with each other in the sense that the systems modeling error is transformed into the residual signal for fault detection while the online modeling switches to offline mode depending on the fault-detection results. The high-dimensional DPSs are first decomposed into spatial features and temporal sequences. Then a receding-horizon scheme is applied for the temporal dynamics learning and the residual signal is converted by the temporal validation error. Experiments on sensor faults diagnosis for the thermal process of a 2-D battery cell are provided for method validation.

Automated summary

So far, fault detection for distributed parameter systems (DPSs) has been mostly model-based and heavily reliant on prior known model information, limiting their usability in industrial applications. In this article, a brand-new framework is proposed for online systems modeling and fault detection of unknown high-dimensional DPSs. The framework includes an interaction between the two parts, where the systems modeling error is transformed into residual signals for fault detection and the online modeling switches to offline mode based on fault-detection results. The effectiveness of the proposed method is validated through experiments on sensor fault diagnosis for the thermal process of a 2-D battery cell.