Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems’ decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper’s main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.

[1]  Rajkumar Buyya,et al.  iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments , 2016, Softw. Pract. Exp..

[2]  Johan A. K. Suykens,et al.  A flexible alarm prediction system for smart manufacturing scenarios following a forecaster–analyzer approach , 2020, Journal of Intelligent Manufacturing.

[3]  Y. Tsao,et al.  Imperfect economic production quantity models under predictive maintenance and reworking , 2020, International Journal of Systems Science: Operations & Logistics.

[4]  Yuqian Lu,et al.  IoT-enabled smart appliances under industry 4.0: A case study , 2020, Adv. Eng. Informatics.

[5]  Fazel Naghdy,et al.  Coordination in wireless sensor-actuator networks: A survey , 2012, J. Parallel Distributed Comput..

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

[7]  Ion Vornicu,et al.  CMOS Vision Sensors: Embedding Computer Vision at Imaging Front-Ends , 2018, IEEE Circuits and Systems Magazine.

[8]  Mikael Gidlund,et al.  Future research challenges in wireless sensor and actuator networks targeting industrial automation , 2011, 2011 9th IEEE International Conference on Industrial Informatics.

[9]  Petr Skobelev,et al.  Multi-Agent System Smart Factory for Real-Time Workshop Management in Aircraft Jet Engines Production , 2013 .

[10]  Der-Jiunn Deng,et al.  Concept Drift Detection and Adaption in Big Imbalance Industrial IoT Data Using an Ensemble Learning Method of Offline Classifiers , 2019, IEEE Access.

[11]  Jan M. van Noortwijk,et al.  A survey of the application of gamma processes in maintenance , 2009, Reliab. Eng. Syst. Saf..

[12]  Kevin Robbie,et al.  Nanomaterials and nanoparticles: Sources and toxicity , 2007, Biointerphases.

[13]  Chia-Yu Hsu,et al.  An Autoencoder Gated Recurrent Unit for Remaining Useful Life Prediction , 2020, Processes.

[14]  Athanasios V. Vasilakos,et al.  A review of industrial wireless networks in the context of Industry 4.0 , 2015, Wireless Networks.

[15]  Michele Magno,et al.  Biodegradable and Highly Deformable Temperature Sensors for the Internet of Things , 2017 .

[16]  Hao Zhang,et al.  Attention-Based LSTM Network for Rotatory Machine Remaining Useful Life Prediction , 2020, IEEE Access.

[17]  Thomas Friedli,et al.  The smart factory as a key construct of industry 4.0: A systematic literature review , 2020 .

[18]  Salvatore Cavalieri,et al.  A Model for Predictive Maintenance Based on Asset Administration Shell , 2020, Sensors.

[19]  Marco Morana,et al.  Motion sensors for activity recognition in an ambient-intelligence scenario , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[20]  Q. Fu,et al.  New understanding of miniaturized VOCs monitoring device: PID-type sensors performance evaluations in ambient air , 2020 .

[21]  Rui L. Aguiar,et al.  Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance , 2020, Inf..

[22]  Claudio Zunino,et al.  Factory Communications at the Dawn of the Fourth Industrial Revolution , 2020, Comput. Stand. Interfaces.

[23]  Ludo Waltman,et al.  Software survey: VOSviewer, a computer program for bibliometric mapping , 2009, Scientometrics.

[24]  Yi Wang,et al.  Industry 4.0: a way from mass customization to mass personalization production , 2017 .

[25]  Tiedo Tinga,et al.  Principles of Loads and Failure Mechanisms: Applications in Maintenance, Reliability and Design , 2013 .

[26]  Jang‐Yeon Hwang,et al.  Sodium-ion batteries: present and future. , 2017, Chemical Society reviews.

[27]  Giuseppe Landolfi,et al.  A MaaS platform architecture supporting data sovereignty in sustainability assessment of manufacturing systems , 2019, Procedia Manufacturing.

[28]  Farhad Kolahan,et al.  A reliability-based approach to optimize preventive maintenance scheduling for coherent systems , 2014, Reliab. Eng. Syst. Saf..

[29]  Rekha Jain,et al.  Wireless Sensor Network -A Survey , 2013 .

[30]  Antonio Visioli,et al.  A virtual force sensor for interaction tasks with conventional industrial robots , 2018 .

[31]  Navar Medeiros M. e Nascimento,et al.  Multi-sensor edge computing architecture for identification of failures short-circuits in wind turbine generators , 2021, Appl. Soft Comput..

[32]  Åsa Fast-Berglund,et al.  Human-Centred Dissemination of Data, Information and Knowledge in Industry 4.0 , 2019, Procedia CIRP.

[33]  Sule Selcuk,et al.  Predictive maintenance, its implementation and latest trends , 2017 .

[34]  Michael Sony,et al.  Industry 4.0 and lean management: a proposed integration model and research propositions , 2018 .

[35]  Paulo Leitão,et al.  Agent-based distributed manufacturing control: A state-of-the-art survey , 2009, Eng. Appl. Artif. Intell..

[36]  Aaqib Saeed,et al.  Predictive maintenance using tree-based classification techniques: A case of railway switches , 2019, Transportation Research Part C: Emerging Technologies.

[37]  Yibin Ying,et al.  Development of an electrochemically reduced graphene oxide modified disposable bismuth film electrode and its application for stripping analysis of heavy metals in milk. , 2014, Food chemistry.

[38]  Samsul Bahari Mohd Noor,et al.  Efficient Soft Sensor Modelling for Advanced Manufacturing Systems by Applying Hybrid Intelligent Soft Computing Techniques , 2019 .

[39]  Chao-Chung Peng,et al.  Graphical histogram algorithm for integrated-circuit-piezoelectric-type accelerometer for health condition diagnosis and monitoring , 2017 .

[40]  Hamid Reza Shaker,et al.  Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges , 2020, Sensors.

[41]  Fei Qiao,et al.  Structure-Property Relationships in Graphene-Based Strain and Pressure Sensors for Potential Artificial Intelligence Applications , 2019, Sensors.

[42]  A. Nightingale Bounding difference: Intersectionality and the material production of gender, caste, class and environment in Nepal , 2011 .

[43]  Andrew A. West,et al.  A data-driven simulation to support remanufacturing operations , 2019, Comput. Ind..

[44]  Robert Schmitt,et al.  Sensor information as a service – component of networked production , 2018 .

[45]  Petr Stodola,et al.  Model of Predictive Maintenance of Machines and Equipment , 2019 .

[46]  Ray Y. Zhong,et al.  Data-driven smart production line and its common factors , 2019, The International Journal of Advanced Manufacturing Technology.

[47]  Oliver Antons,et al.  Designing decision-making authorities for smart factories , 2020 .

[48]  Álvaro Alonso,et al.  Industrial Data Space Architecture Implementation Using FIWARE , 2018, Sensors.

[49]  Hyuncheol Park,et al.  Recent advancements in the Internet-of-Things related standards: A oneM2M perspective , 2016, ICT Express.

[50]  Sanjay E. Sarma,et al.  Auto ID systems and intelligent manufacturing control , 2003 .

[51]  G. Manikandan,et al.  Symmetric cryptography for secure communication in IoT , 2020 .

[52]  Vladimir Migalin,et al.  Smart management of technologies: predictive maintenance of industrial equipment using wireless sensor networks , 2018 .

[53]  Marcello Braglia,et al.  The analytic hierarchy process applied to maintenance strategy selection , 2000, Reliab. Eng. Syst. Saf..

[54]  Michael Sony,et al.  Ten Lessons for Managers While Implementing Industry 4.0 , 2019, IEEE Engineering Management Review.

[55]  Eulogio Cordón-Pozo,et al.  Social Entrepreneurship in the Conduct of Responsible Innovation: Analysis Cluster in Mexican SMEs , 2019, Sustainability.

[56]  Anders Skoogh,et al.  An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study , 2020, Advances in Mechanical Engineering.

[57]  Daniele Marioli,et al.  Wired and wireless sensor networks for industrial applications , 2009, Microelectron. J..

[58]  S. O. Reza Moheimani,et al.  Spatial resonant control of flexible structures-application to a piezoelectric laminate beam , 2001, IEEE Trans. Control. Syst. Technol..

[59]  Khanh T.P. Nguyen,et al.  A new dynamic predictive maintenance framework using deep learning for failure prognostics , 2019, Reliab. Eng. Syst. Saf..

[60]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[61]  Žiga Turk,et al.  Interoperability in construction – Mission impossible? , 2020 .

[62]  Willem D. van Driel,et al.  An approach to "Design for Reliability" in solid state lighting systems at high temperatures , 2012, Microelectron. Reliab..

[63]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[64]  Sangkee Min,et al.  Machine health management in smart factory: A review , 2018 .

[66]  Paul Hawking Big Data Analytics and IoT in logistics: a case study , 2018 .

[67]  S.D.J. McArthur,et al.  The design of a multi-agent transformer condition monitoring system , 2004, IEEE Transactions on Power Systems.

[68]  Julio Molleda,et al.  Infrared Thermography for Temperature Measurement and Non-Destructive Testing , 2014, Sensors.

[69]  Maria José Sousa,et al.  Innovation Trends for Smart Factories: A Literature Review , 2019, WorldCIST.

[70]  Dmitry K. Polyushkin,et al.  Ultrafast machine vision with 2D material neural network image sensors , 2020, Nature.

[71]  Chun Jiang,et al.  Key Technologies of Real-time Visualization System for Intelligent Manufacturing Equipment Operating State under IIOT Environment , 2020 .

[72]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[73]  Tara Betts Humidity , 1912, Buffalo medical journal.

[74]  Andrew Starr,et al.  Data fusion applications in intelligent condition monitoring , 2002 .

[75]  Anabela Carvalho Alves,et al.  Smart products development approaches for Industry 4.0 , 2017 .

[76]  Qasim Zeeshan,et al.  Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0 , 2020, Sustainability.

[77]  Frank Herrmann,et al.  The Smart Factory and Its Risks , 2018, Syst..

[78]  Xianrong Zheng,et al.  Manufacturing upgrading in industry 4.0 era , 2020 .

[79]  C. C. Tripathi,et al.  Significance of nano-materials, designs consideration and fabrication techniques on performances of strain sensors - A review , 2020 .

[80]  Carlo Noe,et al.  Literature review on the ‘Smart Factory’ concept using bibliometric tools , 2017, Int. J. Prod. Res..

[81]  Soukaina Sadiki,et al.  Running Smart Monitoring Maintenance Application Using Cooja Simulator , 2019 .

[82]  Youn Sung Kim,et al.  The quality management ecosystem for predictive maintenance in the Industry 4.0 era , 2019, International Journal of Quality Innovation.

[83]  Marina Schroder,et al.  Structural Control Past Present And Future , 2016 .

[84]  Mu-Yen Chen,et al.  Remaining useful life prediction based on state assessment using edge computing on deep learning , 2020, Comput. Commun..

[85]  Andreja Rojko,et al.  Industry 4.0 Concept: Background and Overview , 2017, Int. J. Interact. Mob. Technol..

[86]  Gangbing Song,et al.  A Review of Rock Bolt Monitoring Using Smart Sensors , 2017, Sensors.

[87]  Rabindra Nath Shaw,et al.  Predictive Data Analysis for Energy Management of a Smart Factory Leading to Sustainability , 2021 .

[88]  Chen-Fu Chien,et al.  Data-Driven Framework for Tool Health Monitoring and Maintenance Strategy for Smart Manufacturing , 2020, IEEE Transactions on Semiconductor Manufacturing.

[89]  Xianping Liu,et al.  Theoretical analysis of detection sensitivity in nano-resonator-based sensors for elasticity and density measurement , 2021 .

[90]  Kahiomba Sonia Kiangala,et al.  Initiating predictive maintenance for a conveyor motor in a bottling plant using industry 4.0 concepts , 2018 .

[91]  Eckart UHLMANN,et al.  Smart wireless sensor network and configuration of algorithms for condition monitoring applications , 2017 .

[92]  T. Swager,et al.  Carbon Nanotube Chemical Sensors. , 2018, Chemical reviews.

[93]  Xiaobo Xu,et al.  Smart factory of Industry 4.0 , 2019 .

[94]  Gerhard P. Hancke,et al.  Industrial Wireless Sensor Networks: Challenges, Design Principles, and Technical Approaches , 2009, IEEE Transactions on Industrial Electronics.

[95]  Lin Li,et al.  Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance , 2017, IEEE Access.

[96]  Yun Li,et al.  PID control system analysis, design, and technology , 2005, IEEE Transactions on Control Systems Technology.

[97]  Robert X. Gao,et al.  Cloud-enabled prognosis for manufacturing , 2015 .

[98]  N. Ganesan,et al.  Free vibration behaviour of multiphase and layered magneto-electro-elastic beam , 2007 .

[99]  Christoph Herwig,et al.  Current and future requirements to industrial analytical infrastructure—part 2: smart sensors , 2020, Analytical and Bioanalytical Chemistry.

[100]  T. Gutowski,et al.  Material efficiency: providing material services with less material production , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[101]  Marina Indri,et al.  Smart Sensors Applications for a New Paradigm of a Production Line , 2019, Sensors.

[102]  Mohamed ElHelw,et al.  Remote Diagnosis, Maintenance and Prognosis for Advanced Driver Assistance Systems Using Machine Learning Algorithms , 2016 .

[103]  Chao-Chung Peng,et al.  IEPE accelerometer fault diagnosis for maintenance management system information integration in a heavy industry , 2020 .

[104]  Ting Wang,et al.  Flexible Transparent Electronic Gas Sensors. , 2016, Small.

[105]  Tomohiko Sakao,et al.  Environmental assessment of consequences from predictive maintenance with artificial intelligence techniques: Importance of the system boundary , 2020 .

[106]  Enzo Morosini Frazzon,et al.  Potential of a Multi-Agent System Approach for Production Control in Smart Factories , 2018 .

[107]  Semin Ryu,et al.  Impact Sound-Based Surface Identification Using Smart Audio Sensors With Deep Neural Networks , 2020, IEEE Sensors Journal.

[108]  Lucas Santos Dalenogare,et al.  Industry 4.0 technologies: Implementation patterns in manufacturing companies , 2019, International Journal of Production Economics.

[109]  Christoph Jan Bartodziej The concept Industry 4.0 , 2017 .

[110]  Azzedine Boukerche,et al.  Emulating Smart City Sensors Using Soft Sensing and Machine Intelligence: A Case Study in Public Transportation , 2018, 2018 IEEE International Conference on Communications (ICC).

[111]  Masood Ur Rehman,et al.  Advances in Sensor Technologies in the Era of Smart Factory and Industry 4.0 † , 2020, Sensors.

[112]  B. B. Zaidan,et al.  Smart Home-based IoT for Real-time and Secure Remote Health Monitoring of Triage and Priority System using Body Sensors: Multi-driven Systematic Review , 2019, Journal of Medical Systems.

[113]  Thyago P. Carvalho,et al.  A systematic literature review of machine learning methods applied to predictive maintenance , 2019, Comput. Ind. Eng..

[114]  Jon M. Kleinberg,et al.  Bursty and Hierarchical Structure in Streams , 2002, Data Mining and Knowledge Discovery.

[115]  Oleg Sergiyenko,et al.  Machine Vision Sensors , 2018, J. Sensors.

[116]  Sufang Zhang,et al.  The impacts of GDP, trade structure, exchange rate and FDI inflows on China's carbon emissions , 2018, Energy Policy.

[117]  Nikolaos G. Bourbakis,et al.  A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[118]  John M Colford,et al.  Systematic reviews and meta-analyses: an illustrated, step-by-step guide. , 2004, The National medical journal of India.

[119]  J. Vrchota,et al.  Critical Success Factors of the Project Management in Relation to Industry 4.0 for Sustainability of Projects , 2020, Sustainability.

[120]  D. Vargas,et al.  An empirical analysis of Total Quality Management and Total Productive Maintenance in Industry 4.0 , 2018 .

[121]  Jinping Ou,et al.  Structural Health Monitoring in mainland China: Review and Future Trends , 2010 .

[122]  Jie Yang,et al.  Achieving a just–in–time supply chain: The role of supply chain intelligence , 2021 .

[123]  Nazmus Sakib,et al.  Challenges and Opportunities of Condition-based Predictive Maintenance: A Review , 2018 .

[124]  Lei Shu,et al.  Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges , 2018, IEEE Access.

[125]  Edward Kozlowski,et al.  Machining sensor data management for operation-level predictive model , 2020, Expert Syst. Appl..

[126]  Md. Zakirul Alam Bhuiyan,et al.  A Survey on Deep Learning in Big Data , 2017, 22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC).

[127]  Sinan Q. Salih,et al.  Internet of things assisted condition‐based support for smart manufacturing industry using learning technique , 2020, Comput. Intell..

[128]  Yann Garcia,et al.  Pressure and Temperature Sensors Using Two Spin Crossover Materials , 2016, Sensors.

[129]  S. Ebrahim,et al.  Reduced dietary salt for prevention of cardiovascular disease. , 2003, The Cochrane database of systematic reviews.

[130]  Matteo Barbieri,et al.  RUL prediction for automatic machines: a mixed edge-cloud solution based on model-of-signals and particle filtering techniques , 2020, Journal of Intelligent Manufacturing.

[131]  Dragan Djurdjanovic,et al.  Tension monitoring in a belt-driven automated material handling system , 2012 .

[132]  Bumsoo Park,et al.  Experimental study on the life prediction of servo motors through model-based system degradation assessment and accelerated degradation testing , 2018 .

[133]  A. Khademhosseini,et al.  Hydrogels in Biology and Medicine: From Molecular Principles to Bionanotechnology , 2006 .

[134]  Deepalekshmi Ponnamma,et al.  Review on exploration of graphene in the design and engineering of smart sensors, actuators and soft robotics , 2020 .

[135]  Vijay Paidi,et al.  Smart parking sensors, technologies and applications for open parking lots: a review , 2018 .

[136]  Helen Durand,et al.  Real‐time preventive sensor maintenance using robust moving horizon estimation and economic model predictive control , 2015 .

[137]  Abdulrahman Al-Ahmari,et al.  Requirements of the Smart Factory System: A Survey and Perspective , 2018, Machines.

[138]  Paul J. M. Havinga,et al.  Fusion of Smartphone Motion Sensors for Physical Activity Recognition , 2014, Sensors.

[139]  S. Ruhi,et al.  Selecting statistical model and optimum maintenance policy: a case study of hydraulic pump , 2016, SpringerPlus.

[140]  Ludovic F. Dumée,et al.  Nano-Enabled sensors for detection of arsenic in water. , 2020, Water research.

[141]  Yang Xu,et al.  Review on Smart Gas Sensing Technology , 2019, Sensors.

[142]  Ting Lie,et al.  Advances in Intelligent Systems and Computing , 2014 .

[143]  Chen Bin,et al.  Literature Review: Framework of Prognostic Health Management for Airline Predictive Maintenance , 2020, 2020 Chinese Control And Decision Conference (CCDC).

[144]  A. Gunasekaran,et al.  Big data analytics in logistics and supply chain management: Certain investigations for research and applications , 2016 .

[145]  Stephen T. Newman,et al.  Making CNC machine tools more open, interoperable and intelligent - a review of the technologies , 2006, Comput. Ind..

[146]  Salvatore Cavalieri,et al.  Towards interoperability between OPC UA and OCF , 2019 .

[147]  Fernando Romero,et al.  A review of the meanings and the implications of the Industry 4.0 concept , 2017 .

[148]  Jay Lee,et al.  INTRODUCTION OF WATCHDOG PROGNOSTICS AGENT AND ITS APPLICATION TO ELEVATOR HOISTWAY PERFORMANCE ASSESSMENT , 2005 .

[149]  G. Büchi,et al.  Smart factory performance and Industry 4.0 , 2020, Technological Forecasting and Social Change.

[150]  Sébastien Guillet,et al.  Using a Virtual Plant to Support the Development of Intelligent Gateway for Sensors/Actuators Security , 2017 .

[151]  Divas Karimanzira,et al.  Enhancing aquaponics management with IoT-based Predictive Analytics for efficient information utilization , 2019, Information Processing in Agriculture.

[152]  Jyrki Kullaa,et al.  Robust damage detection using Bayesian virtual sensors , 2020 .

[153]  Jay Lee,et al.  Watchdog Agent - an infotronics-based prognostics approach for product performance degradation assessment and prediction , 2003, Adv. Eng. Informatics.

[154]  Jie Li,et al.  A Data-Driven Predictive Maintenance Approach for Spinning Cyber-Physical Production System , 2020 .

[155]  Marin Lujak,et al.  Spillover Algorithm: A Decentralized Coordination Approach for Multi-Robot Production Planning in Open Shared Factories , 2021, Robotics Comput. Integr. Manuf..

[156]  Andreas Kamilaris,et al.  Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..

[157]  Ratna Babu Chinnam,et al.  An HMM and polynomial regression based approach for remaining useful life and health state estimation of cutting tools , 2019, Comput. Ind. Eng..

[158]  Ying Peng,et al.  Current status of machine prognostics in condition-based maintenance: a review , 2010 .

[159]  Rodrigo da Rosa Righi,et al.  Predictive maintenance in the Industry 4.0: A systematic literature review , 2020, Comput. Ind. Eng..

[160]  José María de Fuentes García Romero de Tejada,et al.  Industry 4.0: Managing the digital transformation , 2018 .

[161]  Carlos A. F. Marques,et al.  Optical Fiber Magnetic Field Sensors Based on Magnetic Fluid: A Review , 2018, Sensors.

[162]  Mehmed Mahmić,et al.  The Role of Smart Sensors in Production Processes and the Implementation of Industry 4.0 , 2019, Journal of Engineering Sciences.

[163]  Soundar Kumara,et al.  Machinery Fault Diagnosis and Prognosis: Application of Advanced Signal Processing Techniques , 1999 .

[164]  Cristina Alcaraz,et al.  Security of industrial sensor network-based remote substations in the context of the Internet of Things , 2013, Ad Hoc Networks.

[165]  He Xu,et al.  Teaching Management System with Applications of RFID and IoT Technology , 2018 .

[166]  Kristofer S. J. Pister,et al.  MEMS for distributed wireless sensor networks , 2002, 9th International Conference on Electronics, Circuits and Systems.

[167]  Mohd Nizar Hamidon,et al.  Humidity Sensors Principle, Mechanism, and Fabrication Technologies: A Comprehensive Review , 2014, Sensors.

[168]  Daqiang Zhang,et al.  Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination , 2016, Comput. Networks.

[169]  Jiafu Wan,et al.  Implementing Smart Factory of Industrie 4.0: An Outlook , 2016, Int. J. Distributed Sens. Networks.

[170]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. , 2010, International journal of surgery.

[171]  Bruno Francois,et al.  Energy Management and Operational Planning of a Microgrid With a PV-Based Active Generator for Smart Grid Applications , 2011, IEEE Transactions on Industrial Electronics.

[172]  L. Jia,et al.  A stick-like intelligent multicolor nano-sensor for the detection of tetracycline: The integration of nano-clay and carbon dots. , 2021, Journal of hazardous materials.

[173]  Lars Michael Kristensen,et al.  An Industrial Perspective on Wireless Sensor Networks — A Survey of Requirements, Protocols, and Challenges , 2014, IEEE Communications Surveys & Tutorials.

[174]  Teófilo Rojo,et al.  Na-ion batteries, recent advances and present challenges to become low cost energy storage systems , 2012 .

[175]  Wim J. C. Verhagen,et al.  Predictive maintenance for aircraft components using proportional hazard models , 2018, J. Ind. Inf. Integr..