Fault diagnosis of industrial systems with bayesian networks and mutual information

Verron, Sylvain; Tiplica, Téodor; Kobi, Abdessamad

doi:10.23919/ecc.2007.7068252

“…However, as remarked by Arnborg and Sjödin in [Arnborg and Sjödin, 2000], the "authors advocating standard Bayesianism have not been strengthened or weakened" by their analysis.…”

Section: A31 Consistency and Common Sensementioning

confidence: 99%

“…Black box models, or data driven models, are learned from training data, and can for example be various classification methods [Duda et al, 2001, Russell et al, 2000, Chiang et al, 2001, Sorsa et al, 1991, among which we find for example Support Vector Machines (SVM) , Ge et al, 2004, Saunders et al, 2000, methods for Case Based Reasoning (CBR) [Bregon et al, 2007], and Bayesian networks learned from data [Verron et al, 2007. Since data driven models are learned from data, they require no explicit knowledge about the process under diagnosis.…”

Section: Black Box Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Automotive Applications

Udomoto¹,

Matsuzuka²,

Chaki³

et al. 2009

Sliding Mode Control in Electro-Mechanical Systems, Second Edition

1

0

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The aim of this thesis is to contribute to improved diagnosis of automotive vehicles. The work is driven by case studies, where problems and challenges are identified. To solve these problems, theoretically sound and general methods are developed. The methods are then applied to the real world systems.To fulfill performance requirements automotive vehicles are becoming increasingly complex products. This makes them more difficult to diagnose. At the same time, the requirements on the diagnosis itself are steadily increasing. Environmental legislation requires that smaller deviations from specified operation must be detected earlier. More accurate diagnostic methods can be used to reduce maintenance costs and increase uptime. Improved diagnosis can also reduce safety risks related to vehicle operation. Fault diagnosis is the task of identifying possible faults given current observations from the systems. To do this, the internal relations between observations and faults must be identified. In complex systems, such as automotive vehicles, finding these relations is a most challenging problem due to several sources of uncertainty. Observations from the system are often hidden in considerable levels of noise. The systems are complicated to model both since they are complex and since they are operated in continuously changing surroundings. Furthermore, since faults typically are rare, and sometimes never described, it is often difficult to get hold of enough data to learn the relations from.Due to the several sources of uncertainty in fault diagnosis of automotive systems, a probabilistic approach is used, both to find the internal relations, and to identify the faults possibly present in the system given the current observations. To do this successfully, all available information is integrated in the computations.Both on-board and off-board diagnosis are considered. The two tasks may seem different in nature: on-board diagnosis is performed without human integration, while the off-board diagnosis is mainly based on the interactivity with a mechanic. On the other hand, both tasks regard the same vehicle, and information from the on-board diagnosis system may be useful also for off-board diagnosis. The probabilistic methods are general, and it is natural to consider both tasks.The thesis contributes in three main areas. First, in Paper 1 and 2, methods are developed for combining training data and expert knowledge of different kinds to compute probabilities for faults. These methods are primarily developed with on-board diagnosis in mind, but are also applicable to off-board diagnosis. The methods are general, and can be used not only in diagnosis of technical system, but also in many other applications, including medical diagnosis and econometrics, where both data and expert knowledge are present.The second area concerns inference in off-board diagnosis and troubleshooting, and the contribution consists in the methods developed in Paper 3 and 4.ii The methods handle probability computations in systems subject...

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“…This probabilistic model may be estimated from data, as e.g. in [Verron et al, 2007, Pernestål et al, 2006, or set up using expert knowledge as e.g. in [Schwall and Gerdes, 2002, Narasimhan and Biswas, 2007.…”

Section: Relation To Model-based Probabilistic Methodsmentioning

confidence: 99%

Automotive Applications

2009

Optical Science and Engineering

0

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The aim of this thesis is to contribute to improved diagnosis of automotive vehicles. The work is driven by case studies, where problems and challenges are identified. To solve these problems, theoretically sound and general methods are developed. The methods are then applied to the real world systems.To fulfill performance requirements automotive vehicles are becoming increasingly complex products. This makes them more difficult to diagnose. At the same time, the requirements on the diagnosis itself are steadily increasing. Environmental legislation requires that smaller deviations from specified operation must be detected earlier. More accurate diagnostic methods can be used to reduce maintenance costs and increase uptime. Improved diagnosis can also reduce safety risks related to vehicle operation. Fault diagnosis is the task of identifying possible faults given current observations from the systems. To do this, the internal relations between observations and faults must be identified. In complex systems, such as automotive vehicles, finding these relations is a most challenging problem due to several sources of uncertainty. Observations from the system are often hidden in considerable levels of noise. The systems are complicated to model both since they are complex and since they are operated in continuously changing surroundings. Furthermore, since faults typically are rare, and sometimes never described, it is often difficult to get hold of enough data to learn the relations from.Due to the several sources of uncertainty in fault diagnosis of automotive systems, a probabilistic approach is used, both to find the internal relations, and to identify the faults possibly present in the system given the current observations. To do this successfully, all available information is integrated in the computations.Both on-board and off-board diagnosis are considered. The two tasks may seem different in nature: on-board diagnosis is performed without human integration, while the off-board diagnosis is mainly based on the interactivity with a mechanic. On the other hand, both tasks regard the same vehicle, and information from the on-board diagnosis system may be useful also for off-board diagnosis. The probabilistic methods are general, and it is natural to consider both tasks.The thesis contributes in three main areas. First, in Paper 1 and 2, methods are developed for combining training data and expert knowledge of different kinds to compute probabilities for faults. These methods are primarily developed with on-board diagnosis in mind, but are also applicable to off-board diagnosis. The methods are general, and can be used not only in diagnosis of technical system, but also in many other applications, including medical diagnosis and econometrics, where both data and expert knowledge are present.The second area concerns inference in off-board diagnosis and troubleshooting, and the contribution consists in the methods developed in Paper 3 and 4.ii The methods handle probability computations in systems subject...

show abstract

“…As the efficiency of bayesian network for the diagnosis of systems has already been demonstrated (Verron et al, 2006;Verron et al, 2007), the evident outlook of this work is the full study of the use of bayesian network in order to monitor and control a multivariate process (detection and diagnosis in the same network).…”

Section: Conclusion and Outlooksmentioning

confidence: 99%