research-article

Ontology-based Fault Diagnosis: A Decade in Review

Authors:

Zheyuan DingAuthors Info & Claims

ICCMS '19: Proceedings of the 11th International Conference on Computer Modeling and Simulation

Pages 112 - 116

https://doi.org/10.1145/3307363.3307381

Published: 16 January 2019 Publication History

Abstract

Fault diagnosis is a critical activity in the comprehensive support for equipment operation. Relevant research about it is becoming more and more popular. Ontology-based fault diagnosis is an important method for diagnosing faults. Due to the expressive ability, knowledge sharing and knowledge reuse based on deep semantics, and supporting for logical reasoning, ontology-based fault diagnosis has received more and more attention from researchers in the past decade. The fault diagnosis ontology describes the core concepts involved in diagnosing faults and the relationship among the concepts. Its quality and usage determine the efficiency and effectiveness of fault diagnosis, thus has a great impact on fault diagnosis. However, its knowledge source and usage have not been analyzed comprehensively to give us a deep insight into this area yet. This paper investigates and studies the ontology-based fault diagnosis during the past decade, from the perspective of knowledge source and usage of fault diagnosis ontology. In summary, it can be seen that combining ontology with other methods improves the efficiency and the effect of fault diagnosis, and ontology-based fault diagnosis needs to be integrated into the whole process of fault diagnosis. To make knowledge of fault diagnosis complete, machine learning shall be used for the ontological engineering.

References

[1]

AKBARI, A., SETAYESHMEHR, A., BORSI, H., GOCKENBACH, E., and FOFANA, I., 2010. Intelligent agent-based system using dissolved gas analysis to detect incipient faults in power transformers. IEEE Electrical Insulation Magazine 26, 6, 27--40.

[2]

CASTRO, A., SEDANO, A.A., GARC A, F.J., VILLOSLADA, E., and VILLAGR, V.A., 2018. Application of a Multimedia Service and Resource Management Architecture for Fault Diagnosis. Sensors 18, 1, 68.

[3]

CHEN, J. and PATTON, R.J., 2012. Robust model-based fault diagnosis for dynamic systems. Springer Science & Business Media.

Digital Library

[4]

CHEN, R., ZHOU, Z., LIU, Q., and PHAM, D.T., 2015. Knowledge modeling of fault diagnosis for rotating machinery based on ontology. In IEEE International Conference on Industrial Informatics, 1050--1055.

[5]

DENDANI-HADIBY, N. and KHADIR, M.T., 2012. A case based reasoning system based on domain ontology for fault diagnosis of steam turbines. International Journal of Hybrid Information Technology 5, 3, 89--104.

[6]

ELHDAD, R., CHILAMKURTI, N., and TORABI, T., 2013. An ontology-based framework for process monitoring and maintenance in petroleum plant. Journal of Loss Prevention in the Process Industries 26, 1, 104--116.

[7]

FERRARI, R., DIBOWSKI, H., and BALDI, S., 2017. A Message Passing Algorithm for Automatic Synthesis of Probabilistic Fault Detectors from Building Automation Ontologies. In IFAC 2017 World Congress, 4184--4190.

[8]

FRANK, P.M., 1990. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results. automatica 26, 3, 459--474.

Digital Library

[9]

GAO, Z., CECATI, C., and DING, S.X., 2015. A Survey of Fault Diagnosis and Fault-Tolerant Techniques-Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches. IEEE Transactions on Industrial Electronics 62, 6, 3757--3767.

[10]

GAO, Z., CECATI, C., and DING, S.X., 2015. A Survey of Fault Diagnosis and Fault-Tolerant Techniques-Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches. IEEE Transactions on Industrial Electronics 62, 6, 3768--3774.

[11]

LI, X.Q., JING, S.K., YANG, H.C., and ZHOU, J.T., 2014. Ontology-Based Knowledge Modeling for Power Source Subsystem of Satellite Fault Diagnosis. Applied Mechanics & Materials 456, 220--224.

[12]

LIPOL, L.S. and HAQ, J., 2011. Risk analysis method: FMEA/FMECA in the organizations. International Journal of Basic & Applied Sciences 11, 5, 74--82.

[13]

MEDINA-OLIVA, G., VOISIN, A., MONNIN, M., and LEGER, J.B., 2014. Predictive diagnosis based on a fleet-wide ontology approach. Knowledge-Based Systems 68, 3, 40--57.

Digital Library

[14]

MELIK-MERKUMIANS, M., ZOITL, A., and MOSER, T., 2010. Ontology-based fault diagnosis for industrial control applications. In Emerging Technologies and Factory Automation, 1--4.

[15]

RAJPATHAK, D. and CHOUGULE, R., 2011. A generic ontology development framework for data integration and decision support in a distributed environment. International Journal of Computer Integrated Manufacturing 24, 2, 154--170.

Digital Library

[16]

RAJPATHAK, D., SIVA SUBRAMANIA, H., and BANDYOPADHYAY, P., 2012. Ontology-driven data collection and validation framework for the diagnosis of vehicle healthmanagement. International Journal of Computer Integrated Manufacturing 25, 9, 774--789.

Digital Library

[17]

RAJPATHAK, D.G., 2013. An ontology based text mining system for knowledge discovery from the diagnosis data in the automotive domain. Computers in Industry 64, 5, 565--580.

[18]

SAMIRMI, F.D., TANG, W., and Q., W.U., 2015. Fuzzy Ontology Reasoning for Power Transformer Fault Diagnosis. Advances in Electrical & Computer Engineering 15, 4, 107--114.

[19]

SAYED, M.S. and LOHSE, N., 2014. Ontology-driven generation of Bayesian diagnostic models for assembly systems. International Journal of Advanced Manufacturing Technology 74, 5--8, 1033--1052.

[20]

SCHRICK, D.V., 1997. Remarks on Terminology in the Field of Supervision, Fault Detection and Diagnosis 30, 18, 959--964.

[21]

VALIENTEROCHA, P.A. and LOZANOTELLO, A., 2010. Ontology-Based Expert System for Home Automation Controlling. In International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, 661--670.

Digital Library

[22]

WANG, S., WAN, J., LI, D., and LIU, C., 2018. Knowledge Reasoning with Semantic Data for Real-Time Data Processing in Smart Factory. Sensors 18, 2, 471.

[23]

WEN, H., ZHEN, Y., ZHANG, H., CHEN, A., and LIU, D., 2009. An ontology modeling method of mechanical fault diagnosis system based on RSM. In Semantics, Knowledge and Grid, 2009. SKG 2009. Fifth International Conference on IEEE, 408--409.

Digital Library

[24]

XU, F., LIU, X., CHEN, W., ZHOU, C., and CAO, B., 2018. Ontology-Based Method for Fault Diagnosis of Loaders. Sensors 18, 3, 729.

[25]

ZHONG, M., XUE, T., and DING, S.X., 2018. A survey on model-based fault diagnosis for linear discrete time-varying systems. Neurocomputing 306, 51--60.

Digital Library

[26]

ZHONG, Z., XU, T., WANG, F., and TANG, T., 2018. Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing. Mathematical Problems in Engineering(2018).

[27]

ZHOU, A., YU, D., and ZHANG, W., 2015. A research on intelligent fault diagnosis of wind turbines based on ontology and FMECA. Advanced Engineering Informatics 29, 1, 115--125.

Digital Library

[28]

ZHOU, Q., YAN, P., LIU, H., and XIN, Y., 2017. A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis. Journal of Intelligent Manufacturing, 6, 1--23.

[29]

ZHOU, Q., YAN, P., LIU, H., XIN, Y., and CHEN, Y., 2018. Research on a configurable method for fault diagnosis knowledge of machine tools and its application. International Journal of Advanced Manufacturing Technology 95, 1--4 (Mar), 937--960.

[30]

ZHOU, Q., YAN, P., and XIN, Y., 2017. Research on a knowledge modelling methodology for fault diagnosis of machine tools based on formal semantics. Advanced Engineering Informatics 32, 92--112.

Digital Library

Cited By

Hollerer SSauter TKastner W(2024)A Survey of Ontologies Considering General Safety, Security, and Operation Aspects in OTIEEE Open Journal of the Industrial Electronics Society10.1109/OJIES.2024.34411125(861-885)Online publication date: 2024
https://doi.org/10.1109/OJIES.2024.3441112
Ludwig BDiedrich ANiggemann O(2024)Using Ontologies to Create Logical System Descriptions for Fault Diagnosis2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)10.1109/ETFA61755.2024.10710695(1-8)Online publication date: 10-Sep-2024
https://doi.org/10.1109/ETFA61755.2024.10710695
Fernández del Amo IErkoyuncu JBułka DFarsi MAriansyah DKhan SWilding S(2024)Advancing Fault Diagnosis Through Ontology-Based Knowledge Capture and ApplicationIEEE Access10.1109/ACCESS.2024.343341212(144599-144620)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3433412
Show More Cited By

Index Terms

Ontology-based Fault Diagnosis: A Decade in Review
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Ontology engineering

Recommendations

A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis

Fault diagnosis of mechanical components has been attracting increasing attention. Researches have been carried out to reduce unnecessary breakdowns of machinery. Signal processing approaches are the most commonly used techniques for fault diagnosis ...
On undetectable faults and fault diagnosis

The presence of an undetectable fault u_i may modify the response of a detectable fault d_j to a test set used for fault diagnosis. This may impact the accuracy of fault diagnosis based on the responses of single faults. Many state-of-the-art diagnosis ...
Selection of a fault model for fault diagnosis based on unique responses

In this paper, we describe a preprocessing step to fault diagnosis of an observed response obtained from a faulty chip. In this step, a fault model for diagnosing the observed response is selected. This step allows fault diagnosis to be performed based ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICCMS '19: Proceedings of the 11th International Conference on Computer Modeling and Simulation

January 2019

253 pages

ISBN:9781450366199

DOI:10.1145/3307363

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

University of Wollongong, Australia
College of Technology Management, National Tsing Hua University, Taiwan
Swinburne University of Technology
University of Technology Sydney

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 January 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICCMS 2019

ICCMS 2019: The 11th International Conference on Computer Modeling and Simulation

January 16 - 19, 2019

QLD, North Rockhampton, Australia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
495
Total Downloads

Downloads (Last 12 months)45
Downloads (Last 6 weeks)2

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Hollerer SSauter TKastner W(2024)A Survey of Ontologies Considering General Safety, Security, and Operation Aspects in OTIEEE Open Journal of the Industrial Electronics Society10.1109/OJIES.2024.34411125(861-885)Online publication date: 2024
https://doi.org/10.1109/OJIES.2024.3441112
Ludwig BDiedrich ANiggemann O(2024)Using Ontologies to Create Logical System Descriptions for Fault Diagnosis2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)10.1109/ETFA61755.2024.10710695(1-8)Online publication date: 10-Sep-2024
https://doi.org/10.1109/ETFA61755.2024.10710695
Fernández del Amo IErkoyuncu JBułka DFarsi MAriansyah DKhan SWilding S(2024)Advancing Fault Diagnosis Through Ontology-Based Knowledge Capture and ApplicationIEEE Access10.1109/ACCESS.2024.343341212(144599-144620)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3433412
Li QCao SZhang L(2024)A Domain Knowledge-Based Railway Equipment Fault Diagnosis FrameworkKnowledge and Systems Sciences10.1007/978-981-96-0178-3_23(330-344)Online publication date: 9-Nov-2024
https://doi.org/10.1007/978-981-96-0178-3_23
Tang XChi GCui LIp AYung KXie X(2023)Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault DiagnosisSensors10.3390/s2311529523:11(5295)Online publication date: 2-Jun-2023
https://doi.org/10.3390/s23115295
Rübel PMoarefvand NMotsch WWagner ARuskowski M(2023)Enabling Fault Diagnosis in Skill-Based Production Environments2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)10.1109/ETFA54631.2023.10275464(1-8)Online publication date: 12-Sep-2023
https://doi.org/10.1109/ETFA54631.2023.10275464
Nagy LRuppert TAbonyi J(2023)Towards an Ontology-Based Fault Detection and Diagnosis Framework - A Semantic Approach2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)10.1109/CoDIT58514.2023.10284094(1267-1272)Online publication date: 3-Jul-2023
https://doi.org/10.1109/CoDIT58514.2023.10284094
Okazaki SShirafuji SYasui TOta J(2023)A Framework to Support Failure Cause Identification in Manufacturing Systems through Generalization of Past FMEAs2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)10.1109/AIM46323.2023.10196264(858-865)Online publication date: 28-Jun-2023
https://doi.org/10.1109/AIM46323.2023.10196264
Chen JHu ZLu JZheng XZhang HKiritsis D(2022)A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path DiagnosisApplied Sciences10.3390/app1212596112:12(5961)Online publication date: 11-Jun-2022
https://doi.org/10.3390/app12125961
Chi YDong YWang ZYu FLeung V(2022)Knowledge-Based Fault Diagnosis in Industrial Internet of Things: A SurveyIEEE Internet of Things Journal10.1109/JIOT.2022.31636069:15(12886-12900)Online publication date: 1-Aug-2022
https://doi.org/10.1109/JIOT.2022.3163606
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten