scholarly journals Fault diagnosis under uncertain situations within a Bayesian knowledge-intensive CBR system

2021 ◽  
Author(s):  
Hoda Nikpour ◽  
Agnar Aamodt
Keyword(s):  
Fault Diagnosis ◽  
Domain Knowledge ◽  
Semantic Networks ◽  
Efficient Solutions ◽  
Case Based Reasoning ◽  
Oil Well ◽  
Application Domain ◽  
Well Drilling ◽  
Knowledge Intensive ◽  
Case Based

AbstractThis paper presents fault diagnosis and problem solving under uncertainty by a Bayesian supported knowledge-intensive case-based reasoning (CBR) system called BNCreek. In this system, the main goal is to diagnose the causal failures behind the symptoms in complex and uncertain domains. The system’s architecture is described in three aspects: the general, structural, and functional architectures. The domain knowledge is represented by formally defined methods. An integration of semantic networks, Bayesian networks, and CBR is employed to deal with the domain uncertainty. An experiment is conducted from the oil well drilling domain, which is a complex and uncertain area as an application domain. The system is evaluated against the expert estimations to find the most efficient solutions for the problems. The obtained results reveal the capability of the system in diagnosing causal failures.

scholarly journals Inference and reasoning in a Bayesian knowledge-intensive CBR system

2021 ◽  
Author(s):  
Hoda Nikpour ◽  
Agnar Aamodt
Keyword(s):  
Network Inference ◽  
Semantic Network ◽  
Case Based Reasoning ◽  
Oil Well ◽  
Well Drilling ◽  
Knowledge Intensive ◽  
Inference Methods ◽  
Bayesian Network Inference ◽  
Weighted Error ◽  
Statistical Metrics

AbstractThis paper presents the inference and reasoning methods in a Bayesian supported knowledge-intensive case-based reasoning (CBR) system called BNCreek. The inference and reasoning process in this system is a combination of three methods. The semantic network inference methods and the CBR method are employed to handle the difficulties of inferencing and reasoning in uncertain domains. The Bayesian network inference methods are employed to make the process more accurate. An experiment from oil well drilling as a complex and uncertain application domain is conducted. The system is evaluated against expert estimations and compared with seven other corresponding systems. The normalized discounted cumulative gain (NDCG) as a rank-based metric, the weighted error (WE), and root-square error (RSE) as the statistical metrics are employed to evaluate different aspects of the system capabilities. The results show the efficiency of the developed inference and reasoning methods.

A novel adaptation approach for electromagnetic device optimization

2011 ◽  
Vol 60 (4) ◽  
pp. 473-483 ◽  
Author(s):  
Jun Ouyang ◽  
David Lowther
Keyword(s):  
Domain Knowledge ◽  
Low Frequency ◽  
Semantic Networks ◽  
Case Based Reasoning ◽  
Adaptation Algorithm ◽  
Inference System ◽  
New Device ◽  
Device Optimization ◽  
Electromagnetic Device ◽  
Case Based

A novel adaptation approach for electromagnetic device optimizationThe ability of case-based reasoning systems to solve new problems mainly depends on their case adaptation knowledge and adaptation strategies. In order to carry out a successful case adaptation in our case-based reasoning system for a low frequency electromagnetic device design, we make use of semantic networks to organize related domain knowledge, and then construct a rule-based inference system which is based on the network. Furthermore, based on the inference system, a novel adaptation algorithm is proposed to derive a new device case from a real-world induction motor case-base with high dimensionality.

scholarly journals A concept for fault diagnosis combining Case-Based Reasoning with topological system models

2020 ◽  
Vol 53 (2) ◽  
pp. 8217-8224
Author(s):  
Jonas Zinn ◽  
Birgit Vogel-Heuser ◽  
Felix Ocker
Keyword(s):  
Fault Diagnosis ◽  
Case Based Reasoning ◽  
System Models ◽  
Topological System ◽  
Case Based

An Hybrid Expert Fault Diagnosis System for CNC System

2011 ◽  
Vol 189-193 ◽  
pp. 1562-1566
Author(s):  
You Dong Chen ◽  
Jin Jun Ye ◽  
Hua Song Min ◽  
Mei Hua Han
Keyword(s):  
Fault Diagnosis ◽  
System Reliability ◽  
Domain Knowledge ◽  
Fault System ◽  
Rule Base ◽  
Case Based Reasoning ◽  
Cnc System ◽  
System Diagnosis ◽  
Fault Diagnosis System ◽  
Sqlite Database

The CNC system is a complex mechatronics system, which make it difficult to diagnose fault. Expert system for fault diagnosis that utilizes domain knowledge and the profiles of experts to fix the problem of the complex system has become an important issue. A hybrid expert fault system combining the rule-base reasoning (RBR) with case-based reasoning (CBR) for CNC system is proposed. The combination can trouble-shoot rapidly, improve the CNC system reliability and maintainability. The hybrid system is implemented by using QT and SQLITE database. The experiment result of the system shows that the system diagnosis efficiently and accurately.

Graphical Fault Diagnosis Expert System of CNC Machine Tool Based on B/S

2014 ◽  
Vol 945-949 ◽  
pp. 1707-1712
Author(s):  
Bin Shen ◽  
Shu Yu Zhao ◽  
Jia Hai Wang ◽  
Juergen Fleischer
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Machine Tool ◽  
Case Based Reasoning ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Rule Based ◽  
Detailed Case ◽  
Case Based ◽  
Remote Fault Diagnosis

Based on the authors previous work of developing an expert system for fault diagnosis of CNC machine tool, this paper studied the theory and method of CNC remote fault diagnosis expert system based on B/S, and presents schema and structure of the expert system in detailed. Case based reasoning is used for the multi-alarm diagnosis, and rule based reasoning is used for single-alarm diagnosis. At last fault diagnosis expert system was designed and developed making use of C# and ASP.NET.

scholarly journals Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiwang Zhong ◽  
Tianhua Xu ◽  
Feng Wang ◽  
Tao Tang
Keyword(s):  
Cloud Computing ◽  
Fault Diagnosis ◽  
Rough Set ◽  
Best Practice ◽  
Discrete Event ◽  
Scale Up ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Fault Features ◽  
Case Based

In Discrete Event System, such as railway onboard system, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis process. Efficient text mining of such maintenance data plays an important role in discovering the best-practice repair knowledge from millions of repair verbatims, which help to conduct accurate fault diagnosis and predication. This paper presents a text case-based reasoning framework by cloud computing, which uses the diagnosis ontology for annotating fault features recorded in the repair verbatim. The extracted fault features are further reduced by rough set theory. Finally, the case retrieval is employed to search the best-practice repair actions for fixing faulty parts. By cloud computing, rough set-based attribute reduction and case retrieval are able to scale up the Big Data records and improve the efficiency of fault diagnosis and predication. The effectiveness of the proposed method is validated through a fault diagnosis of train onboard equipment.

Research of CBR-Based Organizations on Aromourd Equipment Fault-Diagnosis

2014 ◽  
Vol 635-637 ◽  
pp. 715-721
Author(s):  
Hao Li ◽  
Yao Hui Zhang ◽  
Yi Zheng ◽  
Lin Hong Li
Keyword(s):  
Fault Diagnosis ◽  
Failure Mode ◽  
Complex Structure ◽  
Case Based Reasoning ◽  
Case Based ◽  
The Way

It is the complex structure of the armoured equipment that determines the traditional organizations of the case-warehouse cannot direct the case-based reasoning effectively. Adopting the way to analyse failure mode before the case-warehouse organizations, suming up for the classification. Building the apart index mechanism and establishing the basis of the effective organizations of the case-warehouse at last.

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