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 subspace-aided 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.