A Novel Probabilistic Fault Detection Scheme with Adjustable Reliability Estimates. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Wang, Changren; Shang, Chao; Huang, Dexian; Yu, Bin
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 670
• Page End: 675
• Publication Date: 2020
• Subjects: Parity space -- Fault detection -- Scenario Approach -- A posteriori Probabilistic Bound; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.813
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: We propose a novel probabilistic fault detection scheme with adjustable reliability estimates. Our scheme consists of two phase, the first is the modelling phase, where a probabilistic fault detection design is devised, while the second is the validation phase, where reliability estimates of the design are adjusted online according to new operation records of the plant and the validated reliability. The modelling phase is based on two methods: residual generation, such as parity space, which is an important tool in fault detection problem, and scenario approach, which is a seminal trick to transfer intractable optimization problem into approximate tractable optimization problem and ensure reliability guarantees. The validation phase leverages the state-of-art posteriori probabilistic bounds of convex scenario programs with validation tests. Such a holistic design-and-validate scheme will can help technicians to make better decision. The efficacy of the proposed approach is illustrated on a simulated case study.