Bridges between Diagnosis Theories from Control and AI Perspectives

Travé-Massuyès, Louise

doi:10.1007/978-3-642-39881-0_1

Cited by 9 publications

(6 citation statements)

References 68 publications

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“…Column reasoning, also known as signature-based reasoning [9,11], or parallel reasoning [13]; • Row reasoning, also known as reasoning based on symptoms [24] or sequential reasoning [13].…”

Section: Types Of Algorithmmentioning

confidence: 99%

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Diagnostic Column Reasoning Based on Multi-Valued Evaluation of Residuals and the Elementary Symptoms Sequence

2022

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The paper concerns a significant problem in the diagnostics of industrial processes, which is the need to achieve high fault distinguishability. High distinguishability results in the generation of precise diagnoses that enable making appropriate security decisions. In the known approaches, the efforts to obtain high distinguishability are focused on the selection of an appropriate set of generated residuals. The paper presents a new method of diagnostic reasoning using the notation of faults/diagnostic signals’ relations in the form of a Fault Isolation System (FIS), which enables the use of multivalent diagnostic signals. In addition, the proposed method uses knowledge (usually incomplete) about the sequence of symptoms. Reasoning was carried out on the basis of simple, physically possible signatures, resulting from the FIS. Assumptions and a diagnostic algorithm are given. The reasoning algorithm works in a step-by-step manner, after observing further symptoms. In each reasoning step, two diagnoses are generated in parallel. A more accurate, but less certain diagnosis is formulated on the basis of the value of all diagnostic signals, and the diagnosis is less accurate, but more reliable, solely on the basis of symptoms. An example of using the method for diagnosing a set of connected liquid tanks is given. The method was compared with other reasoning methods based on columns (signatures) and, also, with row-based reasoning methods. It is shown that the proposed method allows the increase of the distinguishability of faults compared to other methods. The distinguishability grows with the knowledge of elementary symptom sequences. It is also noted that the proposed approach makes possible diagnosing not only faults, but also cyber attacks.

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Section: Types Of Algorithmmentioning

confidence: 99%

“…The simplest form of this relationship is the Fault Signature Matrix (FSM) [9][10][11], in which rows represent binary diagnostic signals and columns correspond to faults. A signature is a set of diagnostic signal values corresponding to a given fault.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Column Reasoning Based on Multi-Valued Evaluation of Residuals and the Elementary Symptoms Sequence

2022

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“…The basic solution is by only using symptoms during reasoning. Zero values of signals are ignored, as they may change their value due to possible delays in response to faults [18]. This kind of approach is used in the DX environment.…”

Section: Related Workmentioning

confidence: 99%

“…The exception is the notation of residuals in the internal form. The most commonly used is a Binary Diagnostic Matrix (BDM) [5,11], also referred to as the structure of residual sets [3], Boolean decision table [2], coding set [6,16], Boolean fault signature matrix [14,17,18] and the effect of the faults on the residuals [1]. The reasoning about faults based on BDM is conducted in two ways:…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Row Reasoning Method Based on Multiple-Valued Evaluation of Residuals and Elementary Symptoms Sequence

2021

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The paper analyses the research problem of conducting diagnostic reasoning for dynamic objects to eliminate the possibility of formulating false diagnoses resulting from different delays of the symptoms related to a particular fault while simultaneously striving to obtain high distinguishability. The research aimed to develop a new diagnostic inference method robust to symptom delays and characterised by high accuracy of generated diagnosis. Known methods ensuring the correctness of inference in the case of symptom delays but at the cost of reducing distinguishability of faults have been characterised. A new inference method was developed, which uses the three-valued residual evaluation and knowledge regarding elementary symptom sequences. A formal description of the diagnosing system and the proposed method are given. The method of obtaining the knowledge about the order of symptoms based on a cause-and-effect graph and was characterised. The method’s effectiveness was presented in simulation studies on the example of diagnosing a set of serially connected tanks. The comparison of the fault distinguishability obtained using the proposed method and other approaches illustrates the new method’s advantages.

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“…For example, frequently, the assumption regarding the infallibility of measurement devices or the credibility of observations is adopted [10]. It is a case in both branches of the model-based diagnostics developed by the FDI and DX research communities [11]- [13]. Later, in this paper, we assume the infallibility of measurement instruments.…”

Section: Introductionmentioning

confidence: 99%

Fault compensation effect in fault detection and isolation

Bartyś

2021

ACTA IMEKO

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<p class="Abstract">This paper discusses the origin and problem of the fault compensation effect. The fault compensation effect is an underrated common side effect of the fault isolation approaches developed within the Fault Detection and Isolation (FDI) community. In part, this is justified due to the relatively low probability of such an effect. On the other hand, there is a common belief that the inability to isolate faults due to this effect is the evident drawback of model-based diagnostics. This paper shows how, and under which conditions, the fault compensation effect can be identified. In this connection, the necessary and sufficient conditions for the fault compensation effect are formulated and exemplified by diagnosing a single buffer tank system in open and closed-loop arrangements. In this regard, we also show the drawbacks of a bi-valued residual evaluation for fault isolation. In contrast, we outline the advantages of a three-valued residual evaluation. This paper also brings a series of conclusions allowing for a better understanding of the fault compensation effect. In addition, we show the difference between fault compensation and fault-masking effects.</p>

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Bridges between Diagnosis Theories from Control and AI Perspectives

Cited by 9 publications

References 68 publications

Diagnostic Column Reasoning Based on Multi-Valued Evaluation of Residuals and the Elementary Symptoms Sequence

Diagnostic Column Reasoning Based on Multi-Valued Evaluation of Residuals and the Elementary Symptoms Sequence

Diagnostic Row Reasoning Method Based on Multiple-Valued Evaluation of Residuals and Elementary Symptoms Sequence

Fault compensation effect in fault detection and isolation

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