A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications

Decision-making for manufacturing and maintenance operations is benefiting from the advanced sensor infrastructure of Industry 4.0, enabling the use of algorithms that analyze data, predict emerging situations, and recommend mitigating actions. The current paper reviews the literature on data-driven decision-making in maintenance and outlines directions for future research towards data-driven decision-making for Industry 4.0 maintenance applications. The main research directions include the coupling of decision-making with augmented reality for seamless interfacing that combines the real and virtual worlds of manufacturing operators; methods and techniques for addressing uncertainty of data, in lieu of emerging Internet of Things (IoT) devices; integration of maintenance decision-making with other operations such as scheduling and planning; utilization of the cloud continuum for optimal deployment of decision-making services; capability of decision-making methods to cope with big data; incorporation of advanced security mechanisms; and coupling decision-making with simulation software, autonomous robots, and other additive manufacturing initiatives.

[1]  Vladimir Polotski,et al.  Production and maintenance planning for a failure-prone deteriorating manufacturing system: a hierarchical control approach , 2015 .

[2]  Gregoris Mentzas,et al.  Prescriptive analytics: Literature review and research challenges , 2020, Int. J. Inf. Manag..

[3]  Basilio Sierra,et al.  Predictive Maintenance on the Machining Process and Machine Tool , 2019 .

[4]  Lihui Wang,et al.  Cloud-enhanced predictive maintenance , 2018 .

[5]  Nikolaos Nikolakis,et al.  SERENA: Versatile Plug-and-Play Platform Enabling Remote Predictive Maintenance , 2018, Enterprise Interoperability.

[6]  Ali Azadeh,et al.  Condition-based maintenance effectiveness for series-parallel power generation system - A combined Markovian simulation model , 2015, Reliab. Eng. Syst. Saf..

[7]  Sangje Cho,et al.  Maintenance Planning Support Tool Based on Condition Monitoring with Semantic Modeling of Systems , 2018, Enterprise Interoperability.

[8]  Wu He,et al.  Internet of Things in Industries: A Survey , 2014, IEEE Transactions on Industrial Informatics.

[9]  Fan Wu,et al.  A cost effective degradation-based maintenance strategy under imperfect repair , 2015, Reliab. Eng. Syst. Saf..

[10]  Enrico Macii,et al.  A Fog Computing Approach for Predictive Maintenance , 2019, CAiSE Workshops.

[11]  Frédéric Mérienne,et al.  Evaluating Added Value of Augmented Reality to Assist Aeronautical Maintenance Workers - Experimentation on On-field Use Case , 2019, EuroVR.

[12]  Xiangyu Wang,et al.  Enhancing smart shop floor management with ubiquitous augmented reality , 2019, Int. J. Prod. Res..

[13]  Valéry Bourny,et al.  Towards improving the future of manufacturing through digital twin and augmented reality technologies , 2018 .

[14]  Khac Tuan Huynh,et al.  Multi-Level Decision-Making for The Predictive Maintenance of $k$ -Out-of-$n$ :F Deteriorating Systems , 2015, IEEE Transactions on Reliability.

[15]  Lei Ren,et al.  Cloud manufacturing: key characteristics and applications , 2017, Int. J. Comput. Integr. Manuf..

[16]  Dimitris Apostolou,et al.  A RAMI 4.0 View of Predictive Maintenance: Software Architecture, Platform and Case Study in Steel Industry , 2019, CAiSE Workshops.

[17]  Viliam Makis,et al.  An optimal condition-based maintenance policy for a degrading system subject to the competing risks of soft and hard failure , 2015, Comput. Ind. Eng..

[18]  Viliam Makis,et al.  Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring , 2015, Reliab. Eng. Syst. Saf..

[19]  Joao M. C. Sousa,et al.  A Literature Survey on Open Platform Communications (OPC) Applied to Advanced Industrial Environments , 2019, Electronics.

[20]  Li Da Xu,et al.  Industry 4.0: state of the art and future trends , 2018, Int. J. Prod. Res..

[21]  Vincenzo Moscato,et al.  Deep Learning for HDD Health Assessment: An Application Based on LSTM , 2022, IEEE Transactions on Computers.

[22]  Seungchul Lee,et al.  Joint decision making for maintenance and production scheduling of production systems , 2013 .

[23]  W. Klingenberg,et al.  Typology of condition based maintenance , 2011 .

[24]  Xiaojun Zhou,et al.  Condition-based maintenance for intelligent monitored series system with independent machine failure modes , 2013 .

[25]  Heping Li,et al.  A condition-based maintenance policy for multi-component systems with Lévy copulas dependence , 2016, Reliab. Eng. Syst. Saf..

[26]  Antoine Grall,et al.  Multi-level predictive maintenance for multi-component systems , 2015, Reliab. Eng. Syst. Saf..

[27]  Mariano Frutos,et al.  Industry 4.0: Smart Scheduling , 2018, Int. J. Prod. Res..

[28]  Cher Ming Tan,et al.  Optimal maintenance strategy of deteriorating system under imperfect maintenance and inspection using mixed inspection scheduling , 2013, Reliab. Eng. Syst. Saf..

[29]  Ruud H. Teunter,et al.  Clustering condition-based maintenance for systems with redundancy and economic dependencies , 2016, Eur. J. Oper. Res..

[30]  Gregoris Mentzas,et al.  Enabling condition-based maintenance decisions with proactive event-driven computing , 2018, Comput. Ind..

[31]  Eduardo Alves Portela Santos,et al.  Industrial maintenance decision-making: A systematic literature review , 2017 .

[32]  Gregoris Mentzas,et al.  A Framework for Integrated Proactive Maintenance Decision Making and Supplier Selection , 2017, APMS.

[33]  Andrea Barni,et al.  An ANN Based Decision Support System Fostering Production Plan Optimization Through Preventive Maintenance Management , 2016, Advances in Neural Networks.

[34]  Shahrul Kamaruddin,et al.  Maintenance policy optimization—literature review and directions , 2015 .

[35]  S. G. Deshmukh,et al.  A literature review and future perspectives on maintenance optimization , 2011 .

[36]  Mladen Kezunovic,et al.  Fuzzy Logic Approach to Predictive Risk Analysis in Distribution Outage Management , 2016, IEEE Transactions on Smart Grid.

[37]  Mayorkinos Papaelias,et al.  Condition monitoring of wind turbines: Techniques and methods , 2012 .

[38]  Gregoris Mentzas,et al.  A Proactive Model for Joint Maintenance and Logistics Optimization in the Frame of Industrial Internet of Things , 2019 .

[39]  Meng Zhang,et al.  Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing , 2017, IEEE Access.

[40]  Prashant M. Ambad,et al.  Industry 4.0 – A Glimpse , 2018 .

[41]  James Gao,et al.  Web-based Process Planning for Machine Tool Maintenance and Services , 2015 .

[42]  Xiao Han,et al.  Product quality oriented predictive maintenance strategy for manufacturing systems , 2017, 2017 Prognostics and System Health Management Conference (PHM-Harbin).

[43]  Nagi Z. Gebraeel,et al.  Sensor-Driven Condition-Based Generator Maintenance Scheduling—Part I: Maintenance Problem , 2016, IEEE Transactions on Power Systems.

[44]  Jun-Ho Huh,et al.  Simulation and Test Bed of a Low-Power Digital Excitation System for Industry 4.0 , 2018 .

[45]  Yuguo Xu,et al.  Uncertain generalized remaining useful life prediction-driven predictive maintenance decision , 2015, 2015 Prognostics and System Health Management Conference (PHM).

[46]  Gregoris Mentzas,et al.  Machine Learning for Predictive and Prescriptive Analytics of Operational Data in Smart Manufacturing , 2020, CAiSE Workshops.

[47]  Christian Brecher,et al.  Industrial Internet of Things and Cyber Manufacturing Systems , 2017 .

[48]  Zhiliang Ma,et al.  Data-driven decision-making for equipment maintenance , 2020 .

[49]  Liliane Pintelon,et al.  A Joint Predictive Maintenance and Inventory Policy , 2015 .

[50]  Rong Pan,et al.  Predictive maintenance of complex system with multi-level reliability structure , 2017, Int. J. Prod. Res..

[51]  Dimitris Mourtzis,et al.  Integrated Production and Maintenance Scheduling Through Machine Monitoring and Augmented Reality: An Industry 4.0 Approach , 2017, APMS.

[52]  Fuhai Duan,et al.  Optimization of reliability centered predictive maintenance scheme for inertial navigation system , 2015, Reliab. Eng. Syst. Saf..

[53]  Dimitris Mourtzis,et al.  Cloud-Based Augmented Reality Remote Maintenance Through Shop-Floor Monitoring: A Product-Service System Approach , 2017 .

[54]  D. Tranfield,et al.  Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review , 2003 .

[55]  Khanh T.P. Nguyen,et al.  Joint optimization of monitoring quality and replacement decisions in condition-based maintenance , 2019, Reliab. Eng. Syst. Saf..

[56]  Marco Conti,et al.  Emerging Trends in Hybrid Wireless Communication and Data Management for the Industry 4.0 , 2018 .

[57]  Wenxing Zhou,et al.  Optimal condition-based maintenance decisions for systems with dependent stochastic degradation of components , 2014, Reliab. Eng. Syst. Saf..

[58]  Dirk Cattrysse,et al.  Joint maintenance and inventory optimization systems: A review , 2013 .

[59]  Yong Wang,et al.  Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework , 2020 .

[60]  Lizhi Wang,et al.  Maintenance grouping optimization with system multi-level information based on BN lifetime prediction model , 2019, Journal of Manufacturing Systems.

[61]  Viliam Makis,et al.  Joint optimal lot sizing and preventive maintenance policy for a production facility subject to condition monitoring , 2015 .

[62]  Antonio Picariello,et al.  Model-based vehicular prognostics framework using Big Data architecture , 2020, Comput. Ind..

[63]  Benoît Iung,et al.  A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions , 2015, Reliab. Eng. Syst. Saf..

[64]  Gregoris Mentzas,et al.  A Proactive Event-driven Decision Model for Joint Equipment Predictive Maintenance and Spare Parts Inventory Optimization , 2017 .

[65]  Liliane Pintelon,et al.  A dynamic predictive maintenance policy for complex multi-component systems , 2013, Reliab. Eng. Syst. Saf..

[66]  Gian Antonio Susto,et al.  Machine Learning for Predictive Maintenance: A Multiple Classifier Approach , 2015, IEEE Transactions on Industrial Informatics.

[67]  Wenbin Wang,et al.  A real-time variable cost-based maintenance model from prognostic information , 2012, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing).

[68]  Douglas D. Gemmill,et al.  Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics , 2017, Expert Syst. Appl..

[69]  Chunming Ye,et al.  Single-machine-based joint optimization of predictive maintenance planning and production scheduling , 2018, Robotics and Computer-Integrated Manufacturing.

[70]  Robert X. Gao,et al.  A new paradigm of cloud-based predictive maintenance for intelligent manufacturing , 2015, Journal of Intelligent Manufacturing.

[71]  Mitra Fouladirad,et al.  On-line change detection and condition-based maintenance for systems with unknown deterioration parameters , 2014 .

[72]  Fazel Ansari,et al.  PriMa: a prescriptive maintenance model for cyber-physical production systems , 2019, Int. J. Comput. Integr. Manuf..

[73]  Tarun Gupta,et al.  A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance , 2018, 2018 5th International Conference on Industrial Engineering and Applications (ICIEA).

[74]  Torben Bach Pedersen,et al.  Prescriptive analytics: a survey of emerging trends and technologies , 2019, The VLDB Journal.

[75]  Enrico Zio,et al.  A reinforcement learning framework for optimal operation and maintenance of power grids , 2019, Applied Energy.

[76]  Åsa Fast-Berglund,et al.  The Operator 4.0: Human Cyber-Physical Systems & Adaptive Automation Towards Human-Automation Symbiosis Work Systems , 2016, APMS.

[77]  Tullio Tolio,et al.  A virtual factory approach for in situ simulation to support production and maintenance planning , 2015 .

[78]  Donghua Zhou,et al.  Joint optimization of preventive maintenance and inventory policies for multi-unit systems subject to deteriorating spare part inventory , 2015 .

[79]  Napsiah Ismail,et al.  Maintenance optimization models: a review and analysis , 2011 .

[80]  Gunther Reinhart,et al.  Formulation and Solution for the Predictive Maintenance Integrated Job Shop Scheduling Problem , 2019, 2019 IEEE International Conference on Prognostics and Health Management (ICPHM).

[81]  Lin Ma,et al.  Maintenance optimisation of a multi-state series-parallel system considering economic dependence and state-dependent inspection intervals , 2012, Reliab. Eng. Syst. Saf..