Diagnostic reasoning strategies for means-end models

Larsson, Jan

doi:10.1016/0005-1098(94)90168-6

Cited by 33 publications

(8 citation statements)

References 36 publications

(17 reference statements)

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“…The approaches to design suitable residuals are: parity space design (Chow and Willsky, 1984;Ding et al, 1999;Gertler, 1997), unknown input observer (Hong et al, 1997;Hou and Patton, 1998;Wang and Daley, 1996;Wunnenberg, 1990;Yang and Saif, 1998) and in the frequency domain (Frank and Ding, 199413;Sauter and Hamelin, 1999). A completely different approach is based on reasoning and computer science, and examples here are A r z h (1996), Blanke et al (1997), Larsson (1994) and Larsson (1999). In the latter approach, Boolean logics and object-orientation are keywords.…”

Section: Literaturementioning

confidence: 99%

Adaptive Filtering and Change Detection

Gustafsson¹

2001

596

699

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Section: Literaturementioning

confidence: 99%

Adaptive Filtering and Change Detection

Gustafsson¹

2001

596

699

View full text Add to dashboard Cite

“…Three types of connectional relations such as (i) achieve relations, (ii) condition relations and (iii) realize relations are used to connect objects. Diagnostic reasoning strategies based on the MFM model can be found in (Larsson, 1994).…”

Section: Qualitative Model-based Methodsmentioning

confidence: 99%

“…Overviews of using PCA and PLS in process analysis and control, fault detection and diagnosis were given by MacGregor et al (1991;1994) and MacGregor and Kourti (1995).…”

Section: Process History-based Methodsmentioning

confidence: 99%

“…In order to handle nonlinearity in batch processes, Dong and McAvoy (1996) utilized a nonlinear principal component analysis method. To facilitate the diagnosis procedure in very large processes, new hierarchical multivariate monitoring methods based on multiblock PLS algorithm was presented by MacGregor et al (1991;1994). Raich and Cinar (1996) proposed an integral statistical methodology combining principal component analysis and discrimination analysis techniques.…”

Section: Process History-based Methodsmentioning

confidence: 99%

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Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities

Venkatasubramanian

2005

Computers & Chemical Engineering

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Modern technological advances have resulted in a myriad of complex systems, processes and products. These increasingly complicated processes, systems and products pose considerable challenges in their design, analysis, manufacturing and management for successful operation and use over their life cycles. In the process industries, for example, the maintenance and management of complex process equipment and processes, and their integrated operation, play a cruicial role in ensuring the safety of plant personnel and the environment as well as the timely delivery of quality products. In the discrete parts industries, such as the auto industry, many product malfunctions are due to unanticipated dynamic interactions, due to repeated use or misuse of components. These interactions thrive in complex systems when the combined effects of uncertainty and operational adversity are not properly addressed either in design or in operation. Given the size, scope, and complexity of the systems and inretactions it is becoming increasingly difficult for plant personnel to anticipate, diagnose and control serious abnormal events in a timely manner. Hence, it should come as no surprise that human operators tend to make erroneous decisions and take actions which make matters even worse, as reported in the literature. All these cost the companies and consumers in billions of dollars every year in product lifecycle management. Businesses and federal organizations are increasingly required to manage their entire products' life cycles to avoid costly failure or degradation in performance through service/maintenance, more robust design and control, and so on. These product life cycle management (PLM) issues present us with both major challenges and opportunities. There exist considerable incentives in developing appropriate prognostic and diagnostic methodologies for monitoring, analyzing, interpreting, and controlling such abnormal events in complex systems and processes. People in the process and product industries view this as the next major challenge in control systems research and application. In this paper, we will present an overview of these challenges and the opportunities. Recent progress has promising implications on the use of intelligent systems for product lifecycle management applications in the chemical, petrochemical, pharmaceutical and discrete parts manufacturing industries.

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“…The modeling constructs used in MFM make it highly suitable for diagnosis algorithms like alarm analysis. Since its introduction in 1990, Larsson has contributed several new algorithms and implementations of MFM (Larsson 1992(Larsson , 1994(Larsson , 1996. However, the first successful application of MFM in failure mode analysis was demonstrated by Ö hman (1999).…”

Section: Motivation and Outline Of Proposed Workmentioning

confidence: 99%

Cognitive map-based system modeling for identifying interaction failure modes

et al. 2011

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Past few decades have seen an upsurge in failure analysis techniques capable of dealing with reliability issues up front in the early stages of the product development process. Most of these approaches are centered on component-specific failures. However, with the advent of highly complex systems that derive functionalities from multiple physical phenomena domains, more emphasis is required on identifying failures arising due to various system interactions, which is largely absent in existing failure analysis approaches. Owing to the causal nature of system interaction failures, the use of cognitive maps in system modeling and simulation for failure analysis is highly suitable. This paper proposes a structured framework for the development and use of cognitive mapbased system models capable of capturing all types of failure modes, including interaction failures. The applicability of the proposed framework is demonstrated with the example of an electric water heater.

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Diagnostic reasoning strategies for means-end models

Cited by 33 publications

References 36 publications

Adaptive Filtering and Change Detection

Adaptive Filtering and Change Detection

Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities

Cognitive map-based system modeling for identifying interaction failure modes

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