The Role of Artificial Intelligence (AI) in Condition Monitoring and Diagnostic Engineering Management (COMADEM): A Literature Survey

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.

[1]  C. A. Chang,et al.  Using neural networks for 3D measurement in stereo vision inspection systems , 1999 .

[2]  Khairy A.H. Kobbacy,et al.  Towards An Intelligent Maintenance Optimization System , 1995 .

[3]  Soundar Kumara,et al.  BOUNDARY DEFECT RECOGNITION USING NEURAL NETWORKS , 1997 .

[4]  Luca Fumagalli,et al.  Flexible Automation and Intelligent Manufacturing , FAIM 2017 , 27-30 June 2017 , Modena , Italy A review of the roles of Digital Twin in CPS-based production systems , 2017 .

[5]  Thomas G. Habetler,et al.  Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review , 2019, ArXiv.

[6]  Vittaldas V. Prabhu,et al.  A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis , 2017, APMS.

[7]  F. L. Lewis,et al.  Identification of precursory alarm sequence patterns for predicting equipment failures using ant colony-based algorithm , 2010 .

[8]  C. Wang,et al.  A refined flexible inspection method for identifying surface flaws using the skeleton and neural network , 1997 .

[9]  José Barbosa,et al.  Bio-inspired multi-agent systems for reconfigurable manufacturing systems , 2012, Eng. Appl. Artif. Intell..

[10]  Pan Hong-xia.  Ant Colony Algorithm Application to the Fault Diagnosis of Motor , 2009 .

[11]  Octavian Niculita,et al.  Towards design of prognostics and health management solutions for maritime assets , 2017 .

[12]  Andrew Y. C. Nee,et al.  Digital twin driven prognostics and health management for complex equipment , 2018 .

[13]  Pier Carlo Berri,et al.  Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms , 2019, Aerospace.

[14]  Hyrum S. Anderson,et al.  The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation , 2018, ArXiv.

[15]  J.D.T. Tannock,et al.  Recognition of control chart concurrent patterns using a neural network approach , 1999 .

[16]  L. C. Jain,et al.  Knowledge-based systems for instrumentation diagnosis, system configuration and circuit and system design , 1993 .

[17]  Zakwan Skaf,et al.  Understanding the role of a Digital Twin in Integrated Vehicle Health Management (IVHM)* , 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).

[18]  Patrick Siarry,et al.  Particle swarm and ant colony algorithms hybridized for improved continuous optimization , 2007, Appl. Math. Comput..

[19]  Urko Leturiondo,et al.  Hybrid modelling in condition monitoring , 2016 .

[20]  Zoran Baus,et al.  Use of Fuzzy Logic Systems for Assessment of Primary Faults , 2015 .

[21]  Robert X. Gao,et al.  Digital Twin for rotating machinery fault diagnosis in smart manufacturing , 2018, Int. J. Prod. Res..

[22]  Gary George Clark,et al.  Application of Knowledge-based Systems to Optimised Building Maintenance Management , 1992, IEA/AIE.

[23]  Jae-Yoon Jung,et al.  LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks , 2018, Sensors.

[24]  M. Farid Golnaraghi,et al.  Prognosis of machine health condition using neuro-fuzzy systems , 2004 .

[25]  Yi Lu Murphey,et al.  Case-base reasoning in vehicle fault diagnostics , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[26]  S. H. Huang,et al.  Applications of neural networks in manufacturing: a state-of-the-art survey , 1995 .

[27]  Nadia Belu,et al.  Risk-cost model for FMEA approach through Genetic algorithms ─ A case study in automotive industry , 2019 .

[28]  Esther Thelen.  Dynamic Systems for Everyone. , 1996 .

[29]  Michael S. Branicky,et al.  Studies in hybrid systems: modeling, analysis, and control , 1996 .

[30]  Tughrul Arslan,et al.  A fault dictionary based expert system for failure diagnosis in a multiple-PCB environment , 1993 .

[31]  M Latif,et al.  Design of poka-yoke system based on fuzzy neural network for rotary-machinery monitoring , 2019 .

[32]  I. Dedeakayogullari,et al.  The determination of mean and/or variance shifts with artificial neural networks , 1999 .

[33]  Hans R. Depold,et al.  The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics , 1998 .

[34]  Tao Tang,et al.  Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing , 2018 .

[35]  Ricardo Jardim-Goncalves,et al.  A Self-Adapted Swarm Architecture to Handle Big Data for “Factories of the Future” , 2019, IFAC-PapersOnLine.

[36]  Haydn A. Thompson,et al.  A Distributed Intelligent Agent Architecture for Gas-Turbine Engine Health Management , 2008 .

[37]  K. R. Al-Balushi,et al.  Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection , 2003 .

[38]  Mostafa Refaey,et al.  New approach to power transformer asset management and life assessment using fuzzy logic techniques , 2017, 2017 Nineteenth International Middle East Power Systems Conference (MEPCON).

[39]  Oluseun Omotola Aremu,et al.  Structuring Data for Intelligent Predictive Maintenance in Asset Management , 2018 .

[40]  Nezih Mrad,et al.  The role of data fusion in predictive maintenance using digital twin , 2018 .

[41]  Claudia-Melania Chituc,et al.  Challenges and Trends in Distributed Manufacturing Systems: Are wise engineering systems the ultimate answer? , 2009 .

[42]  Anurag Ganguli,et al.  The Case for a Hybrid Approach to Diagnosis: A Railway Switch , 2015, DX@Safeprocess.

[43]  Yi Wang,et al.  Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario , 2017 .

[44]  W. Ramsey,et al.  The Cambridge Handbook of Artificial Intelligence , 2014 .

[45]  Heena Rathore,et al.  Bio-inspired machine learning based Wireless Sensor Network security , 2013, 2013 World Congress on Nature and Biologically Inspired Computing.

[46]  Vincent C. Müller,et al.  Editorial: Risks of Artificial Intelligence , 2016 .

[47]  Haibin Yu,et al.  A hybrid PSO-GD based intelligent method for machine diagnosis , 2006, Digit. Signal Process..

[48]  Marcantonio Catelani,et al.  Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data , 2017 .

[49]  L. P. Khoo.  An IDEF0 model-based intelligent fault diagnosis system for manufacturing systems , 1999 .

[50]  Ming Rao,et al.  Dynamic case-based reasoning for process operation support systems , 1999 .

[51]  Fei Tao,et al.  Digital Twin Service towards Smart Manufacturing , 2018 .

[52]  Jurgita Antucheviciene,et al.  HYBRID MULTIPLE CRITERIA DECISION MAKING METHODS: A REVIEW OF APPLICATIONS IN ENGINEERING , 2016 .

[53]  Rafael Bello,et al.  A model and its different applications to case-based reasoning , 1996, Knowl. Based Syst..

[54]  Frank Chiang,et al.  iACO: A Bio-inspired Power Efficient Routing Scheme for Sensor Networks , 2010 .

[55]  Leila Hayet Mouss,et al.  A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis , 2012 .

[56]  Yi Wang,et al.  A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment , 2019, The International Journal of Advanced Manufacturing Technology.

[57]  L. Yang.  Fuzzy Logic with Engineering Applications , 1999 .

[58]  A W Labib,et al.  An intelligent maintenance model (system): an application of the analytic hierarchy process and a fuzzy logic rule-based controller , 1998, J. Oper. Res. Soc..

[59]  Linxia Liao,et al.  Combining Deep Learning and Survival Analysis for Asset Health Management , 2020, International Journal of Prognostics and Health Management.

[60]  Robert Lewis Reuben,et al.  Development of a system for monitoring tool wear using artificial intelligence techniques , 2001, Dynamic Systems and Control.

[61]  René Peinl.  Knowledge Management 4.0 - Lessons Learned from IT Trends , 2017, WM.

[62]  Kenneth A. Loparo,et al.  Intelligent Sensor Modes Enable a New Generation of Machinery Diagnostics and Prognostics , 2001 .

[63]  Eduardo Gilabert,et al.  Semantic Web Services for Distributed Intelligence , 2010 .

[64]  Andrew Y. C. Nee,et al.  Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison , 2019, Engineering.

[65]  Shing I. Chang,et al.  A two-stage neural network approach for process variance change detection and classification , 1999 .

[66]  Jun-jie Chen,et al.  The application of CBR and grey correlation in fault diagnosis system , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[67]  Om Prakash,et al.  Development of a Maintenance System Based on Web and Mobile Technologies , 2007, Journal of International Technology and Information Management.

[68]  S. Siva Sathya,et al.  A Survey of Bio inspired Optimization Algorithms , 2012 .

[69]  Hongxia Pan.  Application of PSO Algorithm to Gearbox Fault Diagnosis , 2007 .

[70]  Peter Kilpatrick,et al.  Challenges and Opportunities in Edge Computing , 2016, 2016 IEEE International Conference on Smart Cloud (SmartCloud).

[71]  Shu-Hsien Liao,et al.  Knowledge management technologies and applications - literature review from 1995 to 2002 , 2003, Expert Syst. Appl..

[72]  Shing I. Chang,et al.  An integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative study , 1999 .

[73]  Marcello Braglia,et al.  Failure rate prediction with artificial neural networks , 2005 .

[74]  Dazhong Wu,et al.  A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing , 2017 .

[75]  Jan Lunze,et al.  Handbook of hybrid systems control : theory, tools, applications , 2009 .

[76]  Karina Rivera,et al.  Spacecraft Heath Monitoring Using a Biomimetic Fault Diagnosis Scheme , 2018 .

[77]  Rajesh Ransing,et al.  A semantically constrained Bayesian network for manufacturing diagnosis , 1997 .

[78]  J. Jeon,et al.  The development of a hybrid intelligent maintenance optimisation system (HIMOS) , 2001, J. Oper. Res. Soc..

[79]  Qinghua Zhang,et al.  Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings , 2018 .

[80]  Vili Podgorelec,et al.  Swarm Intelligence Algorithms for Feature Selection: A Review , 2018, Applied Sciences.

[81]  Marek B. Zaremba,et al.  Integration and control of intelligence in distributed manufacturing , 2003, J. Intell. Manuf..

[82]  Lei Lu,et al.  Dynamic Genetic Algorithm-based Feature Selection Scheme for Machine Health Prognostics , 2016 .

[83]  R. Sanfelice,et al.  Hybrid dynamical systems , 2009, IEEE Control Systems.

[84]  Nicolas Jouandeau,et al.  Swarm intelligence-based algorithms within IoT-based systems: A review , 2018, J. Parallel Distributed Comput..

[85]  Takehisa Yairi,et al.  A review on the application of deep learning in system health management , 2018, Mechanical Systems and Signal Processing.

[86]  Asoke K. Nandi,et al.  Condition Monitoring with Vibration Signals , 2019 .

[87]  Ian K. Jennions,et al.  The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities , 2019, Progress in Aerospace Sciences.

[88]  Yang Lu,et al.  Industry 4.0: A survey on technologies, applications and open research issues , 2017, J. Ind. Inf. Integr..

[89]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

[90]  Giorgio Rizzoni,et al.  Diagnosis of an automotive emission control system using fuzzy inference , 1997 .

[91]  Ahmed S. Salama.  A Swarm Intelligence Based Model for Mobile Cloud Computing , 2015 .

[92]  Mateusz Dybkowski,et al.  Artificial Neural Network Application for Current Sensors Fault Detection in the Vector Controlled Induction Motor Drive , 2019, Sensors.

[93]  Amani Kaadoor,et al.  Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review , 2016, 2016 Portland International Conference on Management of Engineering and Technology (PICMET).

[94]  Kosuke Ishii,et al.  Diagnostic expert systems for defects in forged parts , 1995, J. Intell. Manuf..

[95]  Dentcho N. Batanov,et al.  EXPERT-MM: A knowledge-based system for maintenance management , 1993, Artif. Intell. Eng..

[96]  Usman Rauf.  A Taxonomy of Bio-Inspired Cyber Security Approaches: Existing Techniques and Future Directions , 2018 .