Intelligent Case Based Decision Support System for Online Diagnosis of Automated Production System

Rabah, Nourhene Ben; Saddem, Ramla; Hmida, Faten Ben; Carré-Ménétrier, Véronique; Tagina, Moncef

doi:10.1088/1742-6596/783/1/012009

Cited by 2 publications

(4 citation statements)

References 9 publications

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“…The case base can include the diagnostician's expertise, starting from a limited amount of cases (that is, an incomplete knowledge), and its size can increase incrementally over time, as such an expertise is growing. The human diagnostician is assisted in decision making about diagnosis, maintenance and reconfiguration by the interactive case‐based reasoning system, for instance in dealing with automated production systems …”

Section: Diagnosis and The Myth Of Total Knowledge Compilationmentioning

confidence: 99%

“…The human diagnostician is assisted in decision making about diagnosis, maintenance and reconfiguration by the interactive case-based reasoning system, for instance in dealing with automated production systems. 8 In some contexts, such as rotating machinery, which is among the most important equipments in modern industry, fault diagnosis can be regarded as a pattern recognition problem. Due to the variability and richness of the response signals relevant to the rotating machinery condition, it is almost impossible to recognize fault patterns directly: Artificial Intelligence techniques, encompassing a preprocessing step for feature extraction and an online step for fault recognition, are very promising.…”

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

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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: Diagnosis and The Myth Of Total Knowledge Compilationmentioning

confidence: 99%

Section: Diagnosis and The Myth Of Total Knowledge Compilationmentioning

confidence: 99%

Twin‐engined diagnosis of discrete‐event systems

Bertoglio

Lamperti

Zanella

et al. 2019

Engineering Reports

View full text Add to dashboard Cite

show abstract

“…To achieve such an objective, it is necessary to choose a case presentation format that is able to represent all the system faults. For this purpose, we propose in the following a case representation format to overcome the limitations of the case representation previously introduced [2].…”

Section: Data Formatting Modulementioning

confidence: 99%

“…Based on these assumptions, we propose an improvement and an extension of the previously proposed approach [2] in order to meet our main objective which is the online diagnosis of all internal faults in APS. This new approach presents two contributions: (a) a case representation format inspired by Causal Temporal Signatures (CTS) formalism which allows to describe all internal faults.…”

Section: Introductionmentioning

confidence: 99%

CBR-ODAF: A Case-Based Reasoning for the Online Diagnosis of All internal Faults in Automated Production Systems

Rabah¹,

Tekaya²,

Tagina³

EPiC Series in Computing

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In this paper, we focus on the online diagnosis of Automated Production Systems (APS) equipped with sensors and actuators emitting binary signals. These systems can be considered as Discrete Event Systems (DES). The paper presents a Case-Based Reasoning for the Online Diagnosis of All types of Faults in APS (CBR-ODAF). It is an improvement of our approach presented previously in order to remedy its limitations. Firstly, it proposes a new case representation format that describes all the faults to diagnose, adapts to the dynamic aspect of APS, is quite expressive and is easy to understand by human operators. Secondly, it allows to classify in real time each new observation as a ’normal case’, ’faulty case’ or ’unidentified case’ based on a new dissimilarity index which is not intrinsic to the numerical type. It is an index that adapts to our proposed case representation format and describes the degree of difference between cases represented by data of different types (i.e. quantitative and qualitative).

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Intelligent Case Based Decision Support System for Online Diagnosis of Automated Production System

Cited by 2 publications

References 9 publications

Twin‐engined diagnosis of discrete‐event systems

Twin‐engined diagnosis of discrete‐event systems

CBR-ODAF: A Case-Based Reasoning for the Online Diagnosis of All internal Faults in Automated Production Systems

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