Toward a better integration of residual generation and diagnostic decision

Combastel, Christophe; Gentil, Sylviane; Rognon, Jean-Pierre

doi:10.1016/s1474-6670(17)36472-8

Cited by 18 publications

(22 citation statements)

References 1 publication

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“…This has already been stated by many other authors (e.g. [1]) and some efforts have been made to improve fault isolation for dynamic systems.…”

Section: Model-based Fault Detectionsupporting

confidence: 57%

“…After this time has been elapsed, a diagnosis is proposed and the memory component is reset being ready to start the diagnosis of a new fault. Following [1], inside this diagnosis window, the maximum activation value of the memory-table a i,max is updated at the time k 0 only if the actual activation a i (k 0 ) is superior to the previous ones:…”

Section: B the Memory Componentmentioning

confidence: 99%

“…Then, fault isolation consists on matching the actual fault signature with some of the theoretical fault signatures. It is well known [1] that the performance of the whole fault diagnosis system can be highly augmented by improving such interface between fault detection and isolation modules. In particular, such interface can be improved taking into account the following information:…”

Section: Introductionmentioning

confidence: 99%

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A New Fault Diagnosis Algorithm that Improves the Integration of Fault Detection and Isolation

Puig

Schmid

Quevedo

et al.

Proceedings of the 44th IEEE Conference on Decision and Control

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This work proposes a new model-based fault diagnosis method that improves the integration of the fault detection and isolation tasks. A new interface between fault detection and fault isolation is presented that contains information about the degree of fault signal activation and the occurrence time of fault signals. A combination of five fault signature matrices is used for the fault isolation process. The matrices store knowledge about faulty system behavior: boolean fault signal occurrence, signs of residual violation, sensitivities, time of fault signal activation and fault signal occurrence order. Finally, the new method is applied to the well-known two-tanks benchmark problem.

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“…This has already been stated by many other authors (e.g. [1]) and some efforts have been made to improve fault isolation for dynamic systems.…”

Section: Model-based Fault Detectionsupporting

confidence: 57%

Section: B the Memory Componentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Fault Diagnosis Algorithm that Improves the Integration of Fault Detection and Isolation

Puig

Schmid

Quevedo

et al.

Proceedings of the 44th IEEE Conference on Decision and Control

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show abstract

“…Mathematical system models for fault detection are a well‐established subject and a lot of survey papers and books have been published, such as Basseville (1988, 2003), Gertler (1988, 1998), Patton et al . (1989, 2000a), Frank (1990, 1996), Patton (1991, 1996, 1998), Basseville and Nikiforov (1993), Isermann (1993, 2005), Isermann and Balle (1997), Chen and Patton (1999), Simani et al . (2002), Blanke et al .…”

Section: Artificial Intelligence Fault Detection Methods Versus CLmentioning

confidence: 99%

Online expert systems for fault diagnosis in technical processes

Angeli

2008

Expert Systems

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It is generally accepted that there has been an increasing interest in online fault detection and diagnosis techniques for technical processes during the last few years. These techniques come from the artificial intelligence field or are classical numerical methods in combination with artificial intelligence methods. This paper presents a survey of recent research work in online expert systems for fault detection and diagnosis in technical processes. In addition, a short reference to other recent artificial intelligence methods for online fault detection is included and the main advantages and limitations of each method are illustrated.

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“…After this time has elapsed, a diagnosis is proposed and the memory component is reset in order to be ready to start the diagnosis of a new fault. Following the approach of Combastel et al (2003), inside this diagnosis time window, the maximum activation value of the memory-table ,max i r at time k 0 and for one residual i changes only if the current nominal residual is superior to the previous ones. Due to the max-operator activation values can only rise.…”

Section: 31memory Componentmentioning

confidence: 99%

Fault Detection and Isolation in Critical Infrastructure Systems

Puig

Escobet

Sarrate

et al. 2016

Critical Information Infrastructures Security

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Abstract. Critical infrastructure systems (CIS) are complex large-scale systems which in turn require highly sophisticated supervisory control systems to ensure that high performance can be achieved and maintained under adverse conditions. The global CIS Real-Time Control (RTC) need of operating in adverse conditions involves, with a high probability, sensor and actuator malfunctions (faults). This problem calls for the use of an on-line Fault Detection and Isolation (FDI) system able to detect such faults. This paper proposes a FDI mechanism that extends the classical Boolean fault signature matrix concept taking into account several fault signal properties to isolate faults in CIS. To exemplify the proposed FDI scheme in CIS, the Barcelona drinking water network is used as a case study.

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Toward a better integration of residual generation and diagnostic decision

Cited by 18 publications

References 1 publication

A New Fault Diagnosis Algorithm that Improves the Integration of Fault Detection and Isolation

A New Fault Diagnosis Algorithm that Improves the Integration of Fault Detection and Isolation

Online expert systems for fault diagnosis in technical processes

Fault Detection and Isolation in Critical Infrastructure Systems

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