Multiple sensor fault diagnosis for dynamic processes.
Modern industrial plants are usually large scaled and contain a great amount of sensors. Sensor fault diagnosis is crucial and necessary to process safety and optimal operation. This paper proposes a systematic approach to detect, isolate and identify multiple sensor faults for multivariate dynamic systems. The current work first defines deviation vectors for sensor observations, and further defines and derives the basic sensor fault matrix (BSFM), consisting of the normalized basic fault vectors, by several different methods. By projecting a process deviation vector to the space spanned by BSFM, this research uses a vector with the resulted weights on each direction for multiple sensor fault diagnosis. This study also proposes a novel monitoring index and derives corresponding sensor fault detectability. The study also utilizes that vector to isolate and identify multiple sensor faults, and discusses the isolatability and identifiability. Simulation examples and comparison with two conventional PCA-based contribution plots are presented to demonstrate the effectiveness of the proposed methodology.
Li
Cheng-Chih
CC
Department of Chemical Engineering, National Taiwan University, Taipei 106, Taiwan.
Jeng
Jyh-Cheng
JC
D000465:Algorithms; D016002:Discriminant Analysis; D004581:Electronics; D019544:Equipment Failure Analysis / Q000379:methods*; D007221:Industry / Q000295:instrumentation*; D025341:Principal Component Analysis; D011482:Protective Devices