Towards a Case-Based Reasoning Approach to Dynamic Adaptation for Large-Scale Distributed Systems

Purpose Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and isolate the faults at the earliest possible opportunity becomes a complex decision-making process that often requires experts’ opinions and judicious decisions. The purpose of this paper is to propose a framework to detect, isolate and to suggest appropriate maintenance tasks for large-scale complex machinery (i.e. gearboxes of steel processing plant) in a simplified and structured manner by utilizing the prior fault histories available with the organization in conjunction with case-based reasoning (CBR) approach. It is also demonstrated that the proposed framework can easily be implemented by using today’s graphical user interface enabled tools such as Microsoft Visual Basic and similar. Design/methodology/approach CBR, an amalgamated domain of artificial intelligence and human cognitive process, has been applied to carry out the task of fault detection and isolation (FDI). Findings The equipment failure history and actions taken along with the pertinent health indicators are sufficient to detect and isolate the existing fault(s) and to suggest proper maintenance actions to minimize associated losses. The complex decision-making process of maintaining such equipment can exploit the principle of CBR and overcome the limitations of the techniques such as artificial neural networks and expert systems. The proposed CBR-based framework is able to provide inference with minimum or even with some missing information to take appropriate actions. This proposed framework would alleviate from the frequent requirement of expert’s interventions and in-depth knowledge of various analysis techniques expected to be known to process engineers. Originality/value The CBR approach has demonstrated its usefulness in many areas of practical applications. The authors perceive its application potentiality to FDI with suggested maintenance actions to alleviate an end-user from the frequent requirement of an expert for diagnosis or inference. The proposed framework can serve as a useful tool/aid to the process engineers to detect and isolate the fault of large-scale complex machinery with suggested actions in a simplified way.

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Hybrid case-base maintenance approach for modeling large scale case-based reasoning systems

Human-centric Computing and Information Sciences ◽

10.1186/s13673-019-0171-z ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Malik Jahan Khan ◽

Hussain Hayat ◽

Irfan Awan

Keyword(s):

Large Scale ◽

Case Based Reasoning ◽

Reasoning Systems ◽

Case Base ◽

Case Based

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A case-based reasoning approach to fast optimization of travel routes for large-scale AS/RSs

Journal of Intelligent Manufacturing ◽

10.1007/s10845-017-1349-8 ◽

2017 ◽

Vol 30 (4) ◽

pp. 1765-1778 ◽

Cited By ~ 1

Author(s):

Jaeseok Huh ◽

Moon-jung Chae ◽

Jonghun Park ◽

Kwanho Kim

Keyword(s):

Large Scale ◽

Case Based Reasoning ◽

Fast Optimization ◽

Case Based

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Coordination Fault Diagnosis Based on RBF

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.329.278 ◽

2013 ◽

Vol 329 ◽

pp. 278-282

Author(s):

Rui Hua Xu ◽

Zheng Zhou Wang ◽

Ya Dong Yan ◽

Cai Wen Ma

Keyword(s):

Fault Diagnosis ◽

Large Scale ◽

High Accuracy ◽

Case Based Reasoning ◽

Rule Based ◽

Diagnosis System ◽

Fault Diagnosis System ◽

Simulation Results ◽

Self Learning ◽

Case Based

In large-scale complex system, The establishment of a fast, accurate fault diagnosis system is more difficult because there exist many uncertain elements between the fault cause and the fault sign .A fault diagnosis system is established based on RBF cloud neural network ,the RBR (rule-based reasoning) and the CBR (case-based reasoning).The fault diagnosis system not only has the advantages of self-learning, high accuracy, randomness, fuzziness, etc ,and has the advantages of independently of mathematical model ,rich knowledge representation, mighty problem solving ability, etc. Theoretical analysis and simulation results show that the system is feasible and effective for fast and accurate fault positioning of complex systems.

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A parallelized indexing method for large-scale case-based reasoning

Expert Systems with Applications ◽

10.1016/s0957-4174(02)00029-5 ◽

2002 ◽

Vol 23 (2) ◽

pp. 95-102 ◽

Cited By ~ 8

Author(s):

Wei-Chou Chen ◽

Shian-Shyong Tseng ◽

Lu-Ping Chang ◽

Tzung-Pei Hong ◽

Mon-Fong Jiang

Keyword(s):

Large Scale ◽

Case Based Reasoning ◽

Indexing Method ◽

Case Based

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Telederm

Biomedical Knowledge Management ◽

10.4018/978-1-60566-266-4.ch011 ◽

2010 ◽

pp. 154-176

Author(s):

Geoff West ◽

Mihai Lazarescu ◽

Monica Ou

Keyword(s):

Artificial Intelligence ◽

Knowledge Base ◽

General Practitioners ◽

Large Scale ◽

Case Based Reasoning ◽

Web Based ◽

Query Process ◽

Reasoning System ◽

Knowledge Validation ◽

Case Based

In this chapter we describe a web-based decision support system called Telederm that has been developed with the aim of helping general practitioners diagnose skin ailments from a knowledge base while allowing incremental updates of the knowledge base as cases occur. We outline the two major challenges in developing the Telederm system: developing a general practitioner query process that is easily accessible and building knowledge validation in a case-based reasoning system. We provide a detailed description of our approaches to address these problems which involve the use of artificial intelligence classification and reasoning techniques. The system was deployed in a large scale trial in the Eastern Goldfields of Western Australia and we present the results and feedback obtained from an evaluation by the general practitioners involved.

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