2021, Volume 5
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Volume 5 , Issue 1 , June 2021 , Pages: 17 - 37
The Role of Artificial Intelligence (AI) in Condition Monitoring and Diagnostic Engineering Management (COMADEM): A Literature Survey
B. K. Nagaraja Rao, Comadem International, 307 Tiverton Road, Selly Oak, Birmingham, UK
Received: Mar. 27, 2021;       Accepted: Apr. 9, 2021;       Published: Apr. 30, 2021
DOI: 10.11648/j.ajai.20210501.12        View        Downloads  
Artificial Intelligence (AI) is playing a dominant role in the 21st century. Organizations have more data than ever, so it’s crucial to ensure that the analytics team should differentiate between Interesting Data and Useful Data. Amongst the important aspects in Machine Learning are “Feature Selection” and “Feature Extraction”. We are now witnessing the emerging fourth industrial revolution and a considerable number of evolutionary changes in machine learning methodologies to achieve operational excellence in operating and maintaining the industrial assets efficiently, reliably, safely and cost-effectively. AI techniques such as, knowledge based systems, expert systems, artificial neural networks, genetic algorithms, fuzzy logic, case-based reasoning and any combination of these techniques (hybrid systems), machine learning, biomimicry such as swarm intelligence and distributed intelligence. are widely used by multi-disciplinarians to solve a whole range of hitherto intractable problems associated with the proactive maintenance management of industrial assets. In this paper, an attempt is made to review the role of artificial intelligence in condition monitoring and diagnostic engineering management of modern engineering assets. The paper also highlights that unethical and immoral misuse of AI is dangerous.
Artificial Intelligence (AI), Failure Modes, Feature Selection, Feature Extraction, Condition Monitoring, Smart Decision-making, Diagnosis and Prognosis, Potential Ethical and Moral Issues
To cite this article
B. K. Nagaraja Rao, The Role of Artificial Intelligence (AI) in Condition Monitoring and Diagnostic Engineering Management (COMADEM): A Literature Survey, American Journal of Artificial Intelligence. Vol. 5, No. 1, 2021, pp. 17-37. doi: 10.11648/j.ajai.20210501.12
Copyright © 2021 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
[ 1 ]
Arslan, T. S., Bottaci, L. and Taylor, G. E. (1993), “A fault dictionary based expert system for failure diagnosis in a multiple-PCB environment”, Engineering Applications of Artificial Intelligence, 6(5), pp. 447-456.
[ 2 ]
Ahmed S. Salama (2015). A Swarm Intelligence Based Model for Mobile Cloud Computing. I. J. Information Technology and Computer Science, 02, 28 -34.
[ 3 ]
Asish Ghosh (2015). Dynamic Systems for Everyone. Published by Springer.
[ 4 ]
Bostrom, Nick, and Eliezer Yudkowsky. “The Ethics of Artificial Intelligence.” In Cambridge Handbook of Artificial Intelligence, edited by Keith Frankish and William Ramsey. New York: Cambridge University Press.
[ 5 ]
Branicky, M. S. (1995) Studies in Hybrid Systems: Modeling, Analysis, and Control. ScD thesis, Massachusetts Institute of Technology, Cambridge, MA
[ 6 ]
N Belu, L M Ionescu and N Rachieru. (2019). Risk-cost model for FMEA approach through Genetic algorithms –A case study in automotive industry. IOP Conf. Series: Materials Science and Engineering564 (2019) 012102.
[ 7 ]
Batanov, D. B., Nagarur, N. and Nitikhunkasem, P. (1993), “EXPERT-MM: A knowledge-based system for maintenance management”, Artificial Intelligence in Engineering, Vol. 8, pp. 283-291.
[ 8 ]
Peter J. Bentley et al (2018). Should we fear artificial intelligence? Published by European Parlimentary Research Service (EPRS), Scientific Forecast Unit (STOA) in March 2018.
[ 9 ]
Miles Brundage et al (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation.
[ 10 ]
Binitha S & S Siva Sathya (2012). A Survey of Bio inspired Optimization Algorithms. International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2.
[ 11 ]
Blesson Varghese, Nan Wang, Sakil Barbhuiya, Peter Kilpatrick and Dimitrios S. Nikolopoulos (2016). Challenges and Opportunities in Edge Computing.
[ 12 ]
Cordelia Mattuvarkuzhali Ezhilarasu, Zakwan Skaf and Ian K Jennions (2019). Understanding the role of a Digital Twin in Integrated Vehicle Health Management (IVHM). Published in: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) held in Bari, Italy held during 6-9 Oct. 2019.
[ 13 ]
Claudia-Melania Chituc and Francisco José Restivo (2009). Challenges and Trends in Distributed Manufacturing Systems: Are wise engineering systems the ultimate answer? Second International Symposium on Engineering Systems MIT, Cambridge, Massachusetts, June 15-17.
[ 14 ]
Campos Jaime; Jantunen, Erkki; Prakash Om (2007). Development of a Maintenance System Based on Web and Mobile Technologies. Journal of International Technology and Information Management (
[ 15 ]
Cordelia Mattuvarkuzhali Ezhilarasu, ZakwanSkaf and Ian K. Jennions. (2019). The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities. Progress in Aerospace Sciences, Volume 105, February, Pages 60-73
[ 16 ]
Chang, S. I. and Ho, E. S. (1999), “A two-stage neural network approach for process variance change detection and classification”, International Journal of Production Research, 37 (7), pp. 1581-1599.
[ 17 ]
Clark, G., Mehta, P. and Thomson, T. (1992), “Application of knowledge-based systems to optimised building management maintenance”, Lecture notes in Artificial Intelligence, Vol. 604, pp. 69-78.
[ 18 ]
Dedeakayogullari, I. and Buma, K. N. (1999), “The determination of mean and/or variance shifts with artificial neural networks”, International Journal of Production Research, 37 (10), pp. 2191-2200.
[ 19 ]
M. Discenzo Kenneth, Kenneth A. Loparo, Dukki Chung and Allen Twarowski (2001). Intelligent Sensor Nodes Enable A New Generation of Machinery Diagnostics And Prognostics. Proceedings of the 55th Meeting of the Society for Machinery Failure Prevention Technology. Virginia Beach, Virginia, April 2 -5, 2001 (
[ 20 ]
Diego F. Garcia, Andres E. Perez, Hever Moncayo, Karina Rivera, Yomary Betancur, Michael DuPuis and Robert P. Mueller (2018). Spacecraft Heath Monitoring Using a Biomimetic Fault Diagnosis Scheme. Published Online:12 Mar 2018
[ 21 ]
Donghyun Park, Seulgi Kim, Yelin An, and Jae-Yoon Jung (2018). LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks. Sensors (Basel). Jul; 18 (7): 2110.
[ 22 ]
DazhongWu, Shaopeng Liu, Li Zhang, Janis Terpenny, Robert X. Gao, Thomas Kurfess, Judith A. Guzzo (2017). A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. Journal of Manufacturing Systems 43, 25–34.
[ 23 ]
ElisaNegri, LucaFumagalli and MarcoMacchi (2017). A Review of the Roles of Digital Twin in CPS-based Production Systems. Procedia Manufacturing. Volume 11, 2017, Pages 939-948.
[ 24 ]
Eduardo Gilabert & Alexandre Voisin. (2010). Semantic Web Services for Distributed Intelligence. Chapter 11 from a book on E-Maintenance Edited by Kenneth Holmberg, Adam Adgar, Aitor Arnaiz, Erkki Jantunen, Julien Mascolo, Samir Mekid, published by Springer.
[ 25 ]
Edmundas Kazimieras Zavadskas, Edmundas Kazimieras Zavadskas, Jurgita Antucheviciene, Hojjat Adeli, Zenonas Turskis, Hojjat Adeli. Hybrid Multiple Criteria Decision Making Methods: A Review of Applications in Engineering.
[ 26 ]
Erfan Ahadi, Mostafa Larky and Mohammad Riahi. (2018). Applications of Artificial Intelligence on Prognostics of Rotating Machineries. The 26th Annual International Conference of Iranian Society of Mechanical Engineers-ISME2018 24-26 April, 2018, School of Mechanical Engineering, Semnan University, Semnan, Iran.
[ 27 ]
Fei Tao, Qinglin Qi, Lihui Wang and A. Y. C. Ne (2019). Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison. Engineering, Volume 5, Issue 4, August 2019, Pages 653-661.
[ 28 ]
Fei Tao, Meng Zhang, Yushan Liu and Andrew Y C Nee (2018). Digital twin driven prognostics and health management for complex equipment. CIRP Annals, Volume 67, Issue 1, 2018, Pages 169-172.
[ 29 ]
Frank, P. M. and Koppen-Seliger, B. (1997), “New developments of AI in fault diagnosis”, Engineering Applications of Artificial Intelligence, 10 (1), pp. 3-14.
[ 30 ]
Fujikawa, S. and Ishii, K. (1995), “Diagnostic expert systems for defect in forged parts”, Journal of Intelligent Manufacturing, 6 (3), pp. 163-1733.
[ 31 ]
Guh, R.S. and Tannock, J. D. T. (1999), “Recognition of control chart concurrent patterns using a neural network approach”, International Journal of Production Research, 37(8), pp. 1745-1765.
[ 32 ]
Gollu, A., and Varaiya, P. P. (1989) Hybrid dynamical systems. In: Proc. IEEE Conf. Decision and Control, pp. 2708-2712. Tampa, FL.
[ 33 ]
Guilherme Guerreiro, Ruben Costa, Paulo Figueiras, Diogo Graça and Ricardo Jardim-Gonçalves (2019). A Self-Adapted Swarm Architecture to Handle Big Data for “Factories of the Future”. IFAC-Papers On Line, Volume 52, Issue 13, Pages 916-921.
[ 34 ]
Heena Rathore and Sushmita Jha (2013). Bio-inspired machine learning based Wireless Sensor Network security. Published in: 2013 World Congress on Nature and Biologically Inspired Computing. DOI: 10.1109/NaBIC.2013.6617852.
[ 35 ]
Hongxia Pan, Xiuye Wei & Jinying Huang. Application of PSO Algorithm to Gearbox Fault Diagnosis.. School of Mechanical Engineering and Automation, North University of China, Taiyuan 030051, P. R. China.
[ 36 ]
Hen Zhang, Shibo Zhang, Bingnan Wang, and Thomas G. Habetler. Machine Learning and Deep LearningAlgorithms for Bearing Fault Diagnostics– A Comprehensive Review.
[ 37 ]
Hosameldin Ahmed and Asoke K Nandi (2019). Condition Monitoring with Vibration Signals; Compressive Sampling and Learning Algorithms for Rotating Machines. Wiley. ISBN 9781119544623.
[ 38 ]
Haitham Ramadan (2017). New approach to power transformer asset management and life assessment using fuzzy logic techniques. Published in: 2017 Nineteenth International Middle East Power Systems Conference (MEPCON) held in Cairo, Egypt.
[ 39 ]
Hans R. DePold and F. Douglas Gass. (1998). The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics. Presented at the International Gas Turbine & Aeroengine Congress & Exhibition Stockholm, Sweden during June 2—June 5.
[ 40 ]
Ho, E. S. and Chang, S. I. (1999), “An integrated neural network approach for simultaneous monitoring of process mean and variance shift - a comparative study”, International Journal of Production Research, 37(8), pp. 1881-1901.
[ 41 ]
Ion Matei, Anurag Ganguli, Tomonori Honda and Johan de Kleer. The Case for a Hybrid Approach to Diagnosis: A Railway Switch. Proceedings of the 26th International Workshop on Principles of Diagnosis.
[ 42 ]
Ivica Petrović, Lajos Jozsalajos and Zoran Baus. (2015). Use of Fuzzy Logic Systems for Assessment of Primary Faults. Journal of Electrical Engineering, Volume 66: Issue 5.
[ 43 ]
Jianbin Xiong, Qinghua Zhang, Qiong Liang, Hongbin Zhu and Haiying L. (2018). Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings. Shock and Vibration. Volume 2018 |Article ID 3091618 | 13 pages.
[ 44 ]
Jun-jie Chen, Jiang-li Duan, Bao-lu Gao and Ting-ting Lu (2010). The application of CBR and grey correlation in fault diagnosis system. Published in: 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).
[ 45 ]
Jan Lunze and Françoise Lamnabhi-Lagarrigue (2009) Handbook of Hybrid Systems Control: Theory, Tools, Applications,. Published by Cambridge University Press.
[ 46 ]
J. Jeon (2000). The development of a hybrid intelligent maintenance optimization system (HIMOS).
[ 47 ]
Jinjiang Wang, Lunkuan Ye, Robert X. Gao, Chen Li & Laibin Zhang (2019). Digital Twin for rotating machinery fault diagnosis in smart manufacturing. International Journal of Production Research. Volume 57, 2019 - Issue 12: Special Issue: Sustainable Cybernetic Manufacturing.
[ 48 ]
Khoo, L. P., Ang, C. L. and Zhang, J. (1999), “An IDEF0 model-based intelligent fault diagnosis system for manufacturing systems”, International Journal of Production Research, 37 (1), pp. 35-48.
[ 49 ]
Kim, T. and Kumara, S. R. T. (1997), “Boundary defect recognition using neural networks”, International Journal of Production Research, 35(9), pp. 2397-2412.
[ 50 ]
Kobbacy, K. A. H., Proudlove, N. C. and Harper, M. A. (1995), “Towards an intelligent maintenance optimisation system”, Journal of the Operational Research Society, Vol.46, pp. 831-853.
[ 51 ]
Lucija Brezočnik, Iztok Fister, Jr. and Vili Podgorelec. (2018). Swarm Intelligence Algorithms for Feature Selection: A Review., Appl. Sci. 2018, 8(9), 1521;
[ 52 ]
M. Luo, D. H. Zhang, L. L. Aw and F. L. Lewis (2010), "Identification of Precursory Alarm Sequence Patterns for Predicting Equipment Failures Using Ant Colony-Based Algorithm ", International Journal of COMADEM, vol. 13(2), pp. 34-45 (APR-2010).
[ 53 ]
Linxia Liao and Hyung-il Ahn. Combining Deep Learning and Survival Analysis for Asset Health Management. International Journal of Prognostics and Health Management, ISSN2153-2648, 2016 020.
[ 54 ]
W. Labib, G. B. Williams and R. F. O'Connor (1998) An Intelligent Maintenance Model (System): An Application of the Analytic Hierarchy Process and a Fuzzy Logic Rule-Based Controller. The Journal of the Operational Research Society. Vol. 49, No. 7, Intelligent Management Systems in Operations (Jul., 1998).
[ 55 ]
Lei Lu, JihongYan and YueMeng. (2016). Dynamic Genetic Algorithm-based Feature Selection Scheme for Machine Health Prognostics. Procedia CIRP, Volume 56, Pages 316-320.
[ 56 ]
María Matilde García Lorenzo and Rafael Estebán Bello Pérez. (1996). A model and its different applications to case-based reasoning. Publication: Knowledge-Based Systems November 1996
[ 57 ]
Mehul Ved (2018). Feature Selection and Feature Extraction in Machine Learning: An Overview.
[ 58 ]
Matteo D. L. Dalla Vedova, Alfio Germanà, Pier Carlo Berri and Paolo Maggiore. (2019). Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms. Aerospace 2019, 6(9), 94.
[ 59 ]
M Muharam and M Latif. (2019). Design of poka-yoke system based on fuzzy neural network for rotary-machinery monitoring. IOP Conf. Series: Materials Science and Engineering602 (2019) 012003IOP Publishingdoi:10.1088/1757-899X/602/1/012003
[ 60 ]
Maurizio Bevilacqua, Marcello Braglia, Marco Frosolini and Roberto Montanari (2005). Failure rate prediction with artificial neural networks, al of Quality in Maintenance Engineering 11(3):279-294 • September
[ 61 ]
Mateusz Dybkowski1 and Kamil Klimkowski. (2019). Artificial Neural Network Application for Current Sensors Fault Detection in the Vector Controlled Induction Motor Drive. Sensors (Basel). 2019 Feb; 19(3): 571
[ 62 ]
Marek B. Zaremba, Gérard Morel (2003). Integration and control of intelligence in distributed manufacturing. Journal of Intelligent Manufacturing, Volume 14, Issue 1, pp 25-42.
[ 63 ]
Maryam M Najafabadi, Flavio Villanustre, Taghi M Khoshgoftaar, Naeem Seliya, Randall Wald & Edin Muharemagic (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data volume 2, Article number: 1 (2015).
[ 64 ]
Vincent C. Müller (2015). ‘Editorial: Risks of artificial intelligence’, in Vincent C. Müller (ed.), Risks of general intelligence (London: CRC Press - Chapman & Hall).
[ 65 ]
Oluseun Omotola Aremu, Adrià Salvador Palau, Ajith Kumar Parlikad, David Hyland-Wood (2018). Structuring Data for Intelligent Predictive Maintenance in Asset Management, IFAC-Papers On Line, Volume 51, Issue 11, 2018, Pages 514-519.
[ 66 ]
Octavian Niculita, Obinna Nwora and Zakwan Skaf (2017). Towards Design of Prognostics and Health Management Solutions for Maritime Asset. Procedia CIRP. Volume 59, 2017, Pages 122-132.
[ 67 ]
Ouarda Zedadra, Antonio Guerrieri, Nicolas Jouandeau and Giandomenico Spezzano. (2018). Swarm intelligence-based algorithms within IoT-based systems: A review, J. Parallel Distributed Computing. 122, August,
[ 68 ]
Paolo Gunet, Andrew Mills and Haydn Thompson. A Distributed Intelligent Agent Architecture for Gas-Turbine Engine Health Management. Published by American Institute of Aeronautics and Astronautics.
[ 69 ]
Paulo Leitãoa, José Barbosa & Damien Trentesaux (2012). Bio-inspired multi-agent systems for reconfigurable manufacturing systems. Engineering Applications of Artificial Intelligence, Volume 25, Issue 5, August, Pages 934-944.
[ 70 ]
Qinglin Qi, FeiTao, Ying Zuo and Dongming Zhao (2018). Digital Twin Service towards Smart Manufacturing. Procedia CIRP, Volume 72, 2018, Pages 237-242.
[ 71 ]
Qian-jin Guoa, Hai-bin Yu & Ai-dong Xu (2006). A hybrid PSO-GD based intelligent method for machine diagnosis. Digital Signal Processing, Volume 16, Issue 4, July, Pages 402–418.
[ 72 ]
Rory Cellan-Jones (2014), Stephen Hawking warns artificial intelligence could end mankind.
[ 73 ]
Rafik Mahdaoui and Leila Hayet Mouss. (2012). A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis. Journal of Software Engineering and Applications, 2012, 5, 477-482.
[ 74 ]
Ransing, R. S. and Lewis, R. W. (1997), “A semantically constrained neural network for manufacturing diagnosis”, International Journal of Production Research, 35(9), pp.2639-2660.
[ 75 ]
Rene Peinl (2017). Knowledge Management 4.0 - Lessons Learned from IT Trends. Conference: Professional Knowledge Management (WM 2017), At Karlsruhe.
[ 76 ]
Rowland, J. G. and Jain L. C. (1993), “Knowledge-based systems for instrumentation diagnosis, system configuration and circuit and system design”, Engineering Applications of Artificial Intelligence, 6 (5), pp. 437-446.
[ 77 ]
Timothy. J Ross (2009). Fuzzy Logic With Engineering Applications.
[ 78 ]
Shu-hsien Liao (2003). Knowledge management technologies and applications—literature review from 1995 to 2002. Expert Systems with Applications. Volume 25, Issue 2, August 2003, Pages 155-164.
[ 79 ]
Su, C. T., Chang, C. A. and Tien, F. C. (1995), “Neural networks for precision measurement in computer vision systems”, Computers in Industry, 27, pp. 225-236.
[ 80 ]
A. Soliman, G. Rizzoni and Y. W. Kim (1999). Diagnosis of an automotive emission control system using fuzzy inference. Control Engineering Practice, Volume 7, Issue 2, February 1999, Pages 209-216.
[ 81 ]
B. Samanta, K. R. Al-Balushi and S. A. Al-Araimi (2003). Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence. Volume 16, Issues 7–8, October–December 2003, Pages 657-665.
[ 82 ]
SHI Ding-ding and PAN Hong-xia (2009). Ant Colony Algorithm Application to the Fault Diagnosis of Motor. Large Electric Machine and Hydraulic Turbine. 2009-01
[ 83 ]
Rui G. Silva, Steve J. Wilcox and Robert L. Reuben. (2001). Development of a system for monitoring tool wear using artificial intelligence techniques. Published in the proceedings of the ASME International Mechanical Engineering Congress and Exposition.
[ 84 ]
Samir Khan and Takehisa Yairi (2018) A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, Volume 107, July 2018, Pages 241-265.
[ 85 ]
P. S. Shelokar, Patrick Siarry, V. K. Jayaraman and B. D. Kulkarni. (2007). Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Applied Mathematics and Computation, Volume 188, Issue 1, 1 May 2007, Pages 129-142.
[ 86 ]
Shrawasti Ganesh Sahare (2020). Inverse design of functional surfaces for prescribed simple and complex flow characteristics. PhD Thesis. University of Huddersfield. UK.
[ 87 ]
Tien, F. C. and Chang, C. A. (1999), “Using neural networks for 3D measurement in stereo-vision inspection systems”, International Journal of Production Research, 37(9), pp. 1935-1948.
[ 88 ]
Toyosi T. Ademujimi, Michael P. Brundage and Vittaldas V. Prabhu. A review of current machine learning techniques used in manufacturing diagnosis.
[ 89 ]
Taylor Meek et al (2016), Managing the Ethical and Risk Implications of Rapid Advances in Artificial Intelligence: A Literature Review. 2016 Proceedings of PICMET '16: Technology Management for Social Innovation.
[ 90 ]
Urko Leturiondo Zubizarreta. (2016). Hybrid modelling in condition monitoring. PhD Thesis. Lulea University of Technology, Sweden.
[ 91 ]
Urko Leturiondo, OscarSalgado, Lorenzo Ciani, Diego Galar and Marcantonio Catelani. (2017). Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data. Measurement, Volume 108, October 2017, Pages 152-162.
[ 92 ]
Usman Rauf (2018). A Taxonomy of Bio-Inspired Cyber Security Approaches: Existing Techniques and Future Directions. Computer Engineering and Computer Science, February.
[ 93 ]
Venkatesh Mahadevan and Frank Chiang (2010). iACO: A Bio-inspired Power Efficient Routing Scheme for Sensor Networks. International Journal of Computer Theory and Engineering, Vol. 2, No. 6, December, 1793-8201
[ 94 ]
Wang, C. and Huang, S Z. (1997), “A refined flexible inspection method for identifying surface flaws using the skeleton and neural network”, International Journal of Production Research, 35 (9), pp. 2493-2507.
[ 95 ]
Wilson Q. Wang, M. Farid, Golnaraghi and FathyIsmail (2004). Prognosis of machine health condition using neuro-fuzzy systems. Mechanical Systems and Signal Processing. Volume 18, Issue 4, July 2004, Pages 813-831.
[ 96 ]
Wang Xiaodong, Liu Feng, Ren Junhua and Liang Rongyu (2019). A Survey of Digital Twin Technology. Published by Springer Nature Singapore Pte Ltd in Recent Trends in Intelligent Computing, Communication and Devices. Edited by V. Jain, Srikanta Patnaik and Florin Popentiu Vladicescu.
[ 97 ]
Xia, Q. J. and Rao, M. (1999), Dynamic case-based reasoning for process operation support systems, Engineering Applications of Artificial Intelligence, 12 (3), pp. 343-361.
[ 98 ]
Yang Lu (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration Volume 6, June 2017, Pages 1-10.
[ 99 ]
Zhang, F. and Jardine, A. K. S. (1997), “A smart maintenance decision system”, Proceedings of the European Conference on Intelligent Management Systems Operations, pp. 79-86.
[ 100 ]
Zhang, H. C. and Huang S. H (1995), “Application of neural networks in manufacturing: a state of the art”, International Journal of Production Research, 33(3), pp. 705-728.
[ 101 ]
Zhe Li, Yi Wang & Ke-Sheng Wang. (2017). Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Advances in Manufacturing volume 5, pages 377–387.
[ 102 ]
Ziyan Wen, J. Cardillo, Jacob Crossman and Yi Murphey (2003). Case Base Reasoning in Vehicle Fault Diagnostics. Conference: Neural Networks, 2003. Proceedings of the International Joint Conference on, Volume: 4.
[ 103 ]
Zhiwang Zhong, Tianhua Xu, Feng Wang, and Tao Tang (2018). Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing.
[ 104 ]
Mathematical Problems in Engineering, Article ID 9464971, 10 pages Zhe Li, Jingyue Li, Yi Wang and Kesheng Wang (2019). A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment. The International Journal of Advanced Manufacturing Technology volume 103, pages499–510(2019).
[ 105 ]
Zheng Liu, Norbert Meyendorf and Nezih Mrad (2018). The Role of Data Fusion in Predictive Maintenance Using Digital Twin. 44th Annual Review of Progress in Quantitative Non-destructive Evaluation, Volume 37.
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