A cognitive fault-detection design architecture

Milis, George; Ηλιάδης, Δημήτριος Γ.; Panayiotou, Christos G.

doi:10.1109/ijcnn.2016.7727555

Cited by 3 publications

(8 citation statements)

References 17 publications

(17 reference statements)

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“…Noise discrimination is required since the differences may be small when the device is appearing to perform nominally [43,44]. However, creating models is labor-intensive and they require careful calibration to enable them to determine when the ICPS has deviated from expected behaviors [41,45].…”

Section: Fault Identification and Diagnosis In Icpsmentioning

confidence: 99%

“…Agents were originally conceived as a way of computerizing tasks and processes in the way that humans typically might address them. Milis et al discuss cognitive agents who are able to apply expert reasoning, mimicking the activities of human investigators [45]. Like humans, agents are able to adapt their strategies autonomously to cope with changes in the environment they perceive.…”

Section: Intentional Agents For Fault Findingmentioning

confidence: 99%

“…Diagnostic approaches encountered employed agents in both Model-Based [41,45] and Model-Free Artificial Intelligence fault-finding techniques [44,51]. The most common scenarios describe agents capturing diagnostic data, reasoning about evidence and then facilitating fault management [52,53].…”

Section: Intentional Agents For Fault Findingmentioning

confidence: 99%

“…The fault diagnostic engine adopts a model-based rather than a machine-learning approach. Manually creating models is labour and time-intensive, since models need to be calibrated [41,45]. IEC 61499 offers a formally-defined application structure file that allows the engine to autonomously construct an in-memory model of the function block application it has been assigned to monitor and diagnose.…”

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confidence: 99%

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Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems

2021

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IEC 61499 is a reference architecture for constructing Industrial Cyber-Physical Systems (ICPS). However, current function block development environments only provide limited fault-finding capabilities. There is a need for comprehensive diagnostic tools that help engineers identify faults, both during development and after deployment. This article presents the software architecture for an agent-based fault diagnostic engine that equips agents with domain-knowledge of IEC 61499. The engine encourages a Model-Driven Development with Diagnostics methodology where agents work alongside engineers during iterative cycles of design, development, diagnosis and refinement. Attribute-Driven Design (ADD) was used to propose the architecture to capture fault telemetry directly from the ICPS. A Views and Beyond Software Architecture Document presents the architecture. The Architecturally-Significant Requirement (ASRs) were used to design the views while an Architectural Trade-off Analysis Method (ATAM) evaluated critical parts of the architecture. The agents locate faults during both early-stage development and later provide long-term fault management. The architecture introduces dynamic, low-latency software-in-loop Diagnostic Points (DPs) that operate under the control of an agent to capture fault telemetry. Using sound architectural design approaches and documentation methods, coupled with rigorous evaluation and prototyping, the article demonstrates how quality attributes, risks and architectural trade-offs were identified and mitigated early before the construction of the engine commenced.

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Section: Fault Identification and Diagnosis In Icpsmentioning

confidence: 99%

Section: Intentional Agents For Fault Findingmentioning

confidence: 99%

Section: Intentional Agents For Fault Findingmentioning

confidence: 99%

mentioning

confidence: 99%

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Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems

2021

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“…In the general case, the feedback control scheme implementation, can be considered as composed of sub-components, some of which are basic (mandatory) for all implementations of feedback control schemes while others are required only in certain cases. These are discussed in the sequel, adopting the approach introduced in [15] for the component-based analysis of fault-detection schemes.…”

Section: Problem Formulationmentioning

confidence: 99%

A cognitive agent architecture for feedback control scheme design

Milis

Ηλιάδης

Panayiotou

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

Self Cite

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We present a novel architecture for the design of feedback control schemes, aiming to automate the cognitive process performed by human experts when designing control schemes for certain systems. The work starts with the identification of types of cyber-physical components participating in a feedback control scheme. Each component is defined as a functional mapping between inputs-vectors and an output vector. The inputs and outputs are then mapped to a semantic space formed by linguistic variables that model the expert knowledge. These semantic annotations of the components are then exploited by a Cognitive Agent with semantic reasoning capabilities, to achieve the online configuration of a feedback control scheme, given a set of specifications and a database of available components' implementations. The adopted knowledge modelling and reasoning techniques are driven by past efforts of the World Wide Web Consortium on the semantic composition of Web services. The applicability of the method is tested via a paper-based enactment of a use-case from the smart buildings domain.

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Fault Handling in Industry 4.0: Definition, Process and Applications

Webert

Döß

Kaupp

et al. 2022

Sensors

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The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and classification. Data-driven methods can support fault management. The increasing usage of sensors to monitor machine health status in production lines leads to large amounts of data and high complexity. Machine Learning methods exploit this data to support fault management. This paper reviews literature that presents methods for several steps of fault management and provides an overview of requirements for fault handling and methods for fault detection, fault classification, and fault prioritization, as well as their prerequisites. The paper shows that fault prioritization lacks research about available learning methods and underlines that expert opinions are needed.

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A cognitive fault-detection design architecture

Cited by 3 publications

References 17 publications

Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems

Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems

A cognitive agent architecture for feedback control scheme design

Fault Handling in Industry 4.0: Definition, Process and Applications

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