A Toolbox for Analysis and Design of Model Based Diagnosis Systems for Large Scale Models. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Frisk, Erik; Krysander, Mattias; Jung, Daniel
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 3287
• Page End: 3293
• Publication Date: 2017-07
• Subjects: Fault diagnosis -- software tool -- toolbox -- Matlab -- automotive engine; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.504
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: To facilitate the use of advanced fault diagnosis analysis and design techniques to industrial sized systems, there is a need for computer support. This paper describes a Matlab toolbox and evaluates the software on a challenging industrial problem, air-path diagnosis in an automotive engine. The toolbox includes tools for analysis and design of model based diagnosis systems for large-scale differential algebraic models. The software package supports a complete tool-chain from modeling a system to generating C-code for residual generators. Major design steps supported by the tool are modeling, fault diagnosability analysis, sensor selection, residual generator analysis, test selection, and code generation. Structural methods based on efficient graph theoretical algorithms are used in several steps. In the automotive diagnosis example, a diagnosis system is generated and evaluated using measurement data, both in fault-free operation and with faults injected in the control-loop. The results clearly show the benefit of the toolbox in a model-based design of a diagnosis system. Latest version of the toolbox can be downloaded at faultdiagnosistoolbox.github.io.