Subspace-Aided Sensor Fault Diagnosis and Compensation for Industrial Systems

Sensors are ubiquitous in automatized industrial systems. To ensure the safety of the process control, the fault diagnosis and fault-tolerant control of sensors is necessary. This article proposes subspaceaided sensor fault diagnosis and compensation control approaches based on the data-driven stable kernel representation (SKR) and stable image representation (SIR) identified by the process data decompositions. First, this article obtains data-driven SKR and SIR through the mapping relationship of the subspaces of signals and proposes a series of fault diagnosis and compensation approaches. Furthermore, considering the accuracy and timeliness, this article presents an accurate online fault diagnosis and compensation approach by the online updating LQ decomposition. These approaches can perform fault diagnosis, fault estimation, and fault compensation for the multiple and different types of additive sensor faults. The effectiveness of the strategies has been verified by the numerical study and the three-tank experimental system, which has a specific engineering significance.

Automated summary

This article proposes subspace-aided sensor fault diagnosis and compensation control approaches based on data-driven stable kernel representation (SKR) and stable image representation (SIR) identified by process data decompositions. The article obtains data-driven SKR and SIR through the mapping relationship of signal subspaces and presents a series of fault diagnosis and compensation approaches. Furthermore, an accurate online fault diagnosis and compensation approach is presented using online updating LQ decomposition for improved accuracy and timeliness. These approaches can diagnose, estimate, and compensate for multiple and different types of additive sensor faults. The effectiveness of the strategies has been verified through numerical study and experimentation with a three-tank system, demonstrating specific engineering significance.