A Posteriori Diagnosis of Discrete-Event Systems with Symptom Dictionary and Scenarios

Bertoglio, Nicola; Lamperti, Gianfranco; Zanella, Marina

doi:10.1007/978-3-030-22999-3_29

Cited by 2 publications

(11 citation statements)

References 5 publications

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“…Moreover, the method in the aforementioned for extending the open dictionary performs a determinization, whereas no such operation is carried out by the method described in this paper. Finally, the open dictionary generated by the algorithm Dictionary Extension in Section 5 is always a subgraph of the symptom dictionary, whereas this is not guaranteed by the method presented in the work of Bertoglio et al…”

Section: Discussionmentioning

confidence: 99%

“…However, Bertoglio et al addressed the task of diagnosis during monitoring and enforced this task to fulfill a so‐called consistency requirement, whereas the task considered in the current paper is a posteriori diagnosis. Consequently, the dictionaries handled by the two papers are slightly different: a temporal dictionary is managed for diagnosis during monitoring, whereas a symptom dictionary is managed both here and in our different work …”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the method in the aforementioned 44 for extending the open dictionary performs a determinization, whereas no such operation is carried out by the method described in this paper. Finally, the open dictionary generated by the algorithm Dictionary Extension in Section 5 is always a subgraph of the symptom dictionary, whereas this is not guaranteed by the method presented in the work of Bertoglio et al 44 The notion of a symptom pattern has been introduced in another work of Bertoglio et al, 45 where a method analogous to that presented in this current paper is adopted to expand the open dictionary. However, Bertoglio et al 45 addressed the task of diagnosis during monitoring and enforced this task to fulfill a so-called consistency requirement, whereas the task considered in the current paper is a posteriori diagnosis.…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, the dictionaries handled by the two papers are slightly different: a temporal dictionary is managed for diagnosis during monitoring, whereas a symptom dictionary is managed both here and in our different work. 44 The symptom dictionary resembles the compiled knowledge structure called diagnoser in the diagnoser approach 14,15 ; however, several differences can be singled out. First of all, it is worth underlining that the diagnoser approach and the active system approach (which the work presented in this paper stems from) make different modeling assumptions.…”

Section: Discussionmentioning

confidence: 99%

“…In order to face this question, recent work by the authors has tried to combine the two mentioned approaches and to make them cooperate with each other, both for a posteriori diagnosis and diagnosis during monitoring: this paper moves a further step in this direction as far as a posteriori diagnosis is concerned. The main findings can be summarized as follows.…”

Section: Diagnosis and The Myth Of Total Knowledge Compilationmentioning

confidence: 99%

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Twin‐engined diagnosis of discrete‐event systems

Bertoglio

Lamperti

Zanella

et al. 2019

Engineering Reports

Self Cite

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Diagnosis of discrete‐event systems (DESs) is computationally complex. This is why a variety of knowledge compilation techniques have been proposed, the most notable of them rely on a diagnoser. However, the construction of a diagnoser requires the generation of the whole system space, thereby making the approach impractical even for DESs of moderate size. To avoid total knowledge compilation while preserving efficiency, a twin‐engined diagnosis technique is proposed in this paper, which is inspired by the two operational modes of the human mind. If the symptom of the DES is part of the knowledge or experience of the diagnosis engine, then Engine 1 allows for efficient diagnosis. If, instead, the symptom is unknown, then Engine 2 comes into play, which is far less efficient than Engine 1. Still, the experience acquired by Engine 2 is then integrated into the symptom dictionary of the DES. This way, if the same diagnosis problem arises anew, then it will be solved by Engine 1 in linear time. The symptom dictionary can also be extended by specialized knowledge coming from scenarios, which are the most critical/probable behavioral patterns of the DES, which need to be diagnosed quickly.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Diagnosis and The Myth Of Total Knowledge Compilationmentioning

confidence: 99%

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Twin‐engined diagnosis of discrete‐event systems

Bertoglio

Lamperti

Zanella

et al. 2019

Engineering Reports

Self Cite

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Temporal-Fault Diagnosis for Critical-Decision Making in Discrete-Event Systems

Bertoglio

Lamperti

Zanella

et al. 2020

Procedia Computer Science

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Since its appearance in AI, model-based diagnosis is intrinsically set-oriented. Given a sequence of observations, the diagnosis task generates a set of diagnoses, or candidates, each candidate complying with the observations. What all the approaches in the literature have in common is that a candidate is invariably a set of faulty elements (components, events, or otherwise). In this paper, we consider a posteriori diagnosis of discrete-event systems (DESs), which are described by networks of components that are modeled as communicating automata. The diagnosis problem consists in generating the candidates involved in the trajectories of the DES that conform with a given temporal observation. Oddly, in the literature on diagnosis of DESs, a candidate is still a set of faulty events, despite the temporal dimension of trajectories. In our view, when dealing with critical domains, such as power networks or nuclear plants, set-oriented diagnosis may be less than optimal in explaining the supposedly abnormal behavior of the DES, owing to the lack of any temporal information relevant to faults, along with the inability to discriminate between single and multiple occurrences of the same fault. Embedding temporal information in candidates may be essential for critical-decision making. This is why a temporal-oriented approach is proposed for diagnosis of DESs, where candidates are sequences of faults. This novel perspective comes with the burden of unbounded candidates and infinite collections of candidates, though. To cope with, a notation based on regular expressions on faults is adopted. The diagnosis task is supported by a temporal diagnoser, a flexible data structure that can grow over time based on new observations and domain-dependent scenarios.

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A Posteriori Diagnosis of Discrete-Event Systems with Symptom Dictionary and Scenarios

Cited by 2 publications

References 5 publications

Twin‐engined diagnosis of discrete‐event systems

Twin‐engined diagnosis of discrete‐event systems

Temporal-Fault Diagnosis for Critical-Decision Making in Discrete-Event Systems

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