Predictive Maintenance and Intelligent Sensors in Smart Factory: Review
Status PubMed-not-MEDLINE Language English Country Switzerland Media electronic
Document type Journal Article, Review
Grant support
EF-150-GAJU 047/2019/S
University of South Bohemia in Ceske Budejovice
PubMed
33672479
PubMed Central
PMC7923427
DOI
10.3390/s21041470
PII: s21041470
Knihovny.cz E-resources
- Keywords
- Industry 4.0, intelligent sensors, maintenance, smart factory,
- Publication type
- Journal Article MeSH
- Review MeSH
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.
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Allwood J.M., Ashby M.F., Gutowski T.G., Worrell E. Material efficiency: Providing material services with less material production. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2013;371:20120496. doi: 10.1098/rsta.2012.0496. PubMed DOI PMC
Nightingale A.J. Bounding difference: Intersectionality and the material production of gender, caste, class and environment in Nepal. Geoforum. 2011;42:153–162. doi: 10.1016/j.geoforum.2010.03.004. DOI
Zhang Y., Zhang S. The impacts of GDP, trade structure, exchange rate and FDI inflows on China’s carbon emissions. Energy Policy. 2018;120:347–353. doi: 10.1016/j.enpol.2018.05.056. DOI
Song G., Li W., Wang B., Ho S.C.M. A review of rock bolt monitoring using smart sensors. Sensors. 2017;17:776. doi: 10.3390/s17040776. PubMed DOI PMC
Jin X., Feng C., Ponnamma D., Yi Z., Parameswaranpillai J., Thomas S., Salim N.V. Review on exploration of graphene in the design and engineering of smart sensors, actuators and soft robotics. Chem. Eng. J. Adv. 2020;4:100034. doi: 10.1016/j.ceja.2020.100034. DOI
Paidi V., Fleyeh H., Håkansson J., Nyberg R.G. Smart parking sensors, technologies and applications for open parking lots: A review. IET Intell. Transp. Syst. 2018;12:735–741. doi: 10.1049/iet-its.2017.0406. DOI
Talal M., Zaidan A.A., Zaidan B.B., Albahri A.S., Alamoodi A.H., AlSalem M.A., Lim C.K., Tan K.L., Shir W.L., Mohammed K.I. Smart home-based IoT for real-time and secure remote health monitoring of triage and priority system using body sensors: Multi-driven systematic review. J. Med. Syst. 2019;43:42. doi: 10.1007/s10916-019-1158-z. PubMed DOI
Sony M. Industry 4.0 and lean management: A proposed integration model and research propositions. Prod. Manuf. Res. 2018;6:416–432. doi: 10.1080/21693277.2018.1540949. DOI
Lee G.-Y., Kim M., Quan Y.-J., Kim M.-S., Kim T.J.Y., Yoon H.-S., Min S., Kim D.-H., Mun J.-W., Oh J.W., et al. Machine health management in smart factory: A review. J. Mech. Sci. Technol. 2018;32:987–1009. doi: 10.1007/s12206-018-0201-1. DOI
Strozzi F., Colicchia C., Creazza A., Noè C. Literature review on the ‘Smart Factory’ concept using bibliometric tools. Int. J. Prod. Res. 2017;55:6572–6591. doi: 10.1080/00207543.2017.1326643. DOI
Feng S., Farha F., Li Q., Wan Y., Xu Y., Zhang T., Ning H. Review on smart gas sensing technology. Sensors. 2019;19:3760. doi: 10.3390/s19173760. PubMed DOI PMC
Sony M., Naik S.S. Ten lessons for managers while implementing industry 4.0. IEEE Eng. Manag. Rev. 2019;47:45–52. doi: 10.1109/EMR.2019.2913930. DOI
Osterrieder P., Budde L., Friedli T. The smart factory as a key construct of industry 4.0: A systematic literature review. Int. J. Prod. Econ. 2020;221:107476. doi: 10.1016/j.ijpe.2019.08.011. DOI
Pereira A., Romero F. A review of the meanings and the implications of the Industry 4.0 concept. Procedia Manuf. 2017;13:1206–1214. doi: 10.1016/j.promfg.2017.09.032. DOI
Bahena-Álvarez I.L., Cordón-Pozo E., Delgado-Cruz A. Social entrepreneurship in the conduct of responsible innovation: Analysis cluster in Mexican smes. Sustainability. 2019;11:3714. doi: 10.3390/su11133714. DOI
Sousa M.J., Cruz R., Rocha Á., Sousa M. Innovation trends for smart factories: A literature review. In: Rocha Á., Adeli H., Reis L.P., Costanzo S., editors. New Knowledge in Information Systems and Technologies. Volume 930. Springer International Publishing; Cham, Switzerland: 2019. pp. 689–698. (Advances in Intelligent Systems and Computing).
Lee S., Kim J.-Y., Lee W. Smart factory literature review and strategies for korean small manufacturing firms. J. Inf. Technol. Appl. Manag. 2017;24:133–152. doi: 10.21219/JITAM.2017.24.4.133. DOI
Carvalho T.P., Soares F.A.A.M.N., Vita R., Francisco R.D.P., Basto J.P., Alcalá S.G.S. A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 2019;137:106024. doi: 10.1016/j.cie.2019.106024. DOI
Sakib N., Wuest T. Challenges and opportunities of condition-based predictive maintenance: A review. Procedia CIRP. 2018;78:267–272. doi: 10.1016/j.procir.2018.08.318. DOI
Zonta T., Da Costa C.A., Righi R.D.R., De Lima M.J., Da Trindade E.S., Li G.P. Predictive maintenance in the Industry 4.0: A systematic literature review. Comput. Ind. Eng. 2020;150:106889. doi: 10.1016/j.cie.2020.106889. DOI
Olesen J.F., Shaker H.R. Predictive maintenance for pump systems and thermal power plants: State-of-the-art review, trends and challenges. Sensors. 2020;20:2425. doi: 10.3390/s20082425. PubMed DOI PMC
Fei X., Bin C., Jun C., Shunhua H. Literature review: Framework of prognostic health management for airline predictive maintenance; Proceedings of the 2020 Chinese Control and Decision Conference (CCDC); Hefei, China. 22–24 August 2020; pp. 5112–5117. DOI
Salkin C., Oner M., Ustundag A., Cevikcan E. A conceptual framework for industry 4.0. In: Ustundag A., Cevikcan E., editors. Industry 4.0: Managing The Digital Transformation. Springer International Publishing; Cham, Switzerland: 2018. DOI
Bartodziej C.J. The Concept Industry 4.0. Springer Fachmedien Wiesbaden; Wiesbaden, Germany: 2017. The concept industry 4.0; pp. 27–50.
Chen B., Wan J., Shu L., Li P., Mukherjee M., Yin B. Smart factory of industry 4.0: Key technologies, application case, and challenges. IEEE Access. 2018;6:6505–6519. doi: 10.1109/ACCESS.2017.2783682. DOI
Eifert T., Eisen K., Maiwald M., Herwig C. Current and future requirements to industrial analytical infrastructure—Part 2: Smart sensors. Anal. Bioanal. Chem. 2020;412:2037–2045. doi: 10.1007/s00216-020-02421-1. PubMed DOI PMC
Tan P., Wu H., Li P., Xu H. Teaching management system with applications of RFID and IOT technology. Educ. Sci. 2018;8:26. doi: 10.3390/educsci8010026. DOI
Chen Y., Han Z., Cao K., Zheng X., Xu X. Manufacturing upgrading in industry 4.0 era. Syst. Res. Behav. Sci. 2020;37:766–771. doi: 10.1002/sres.2717. DOI
Karabegovic I., Mahmic M., Husak E. The role of smart sensors in production processes and the implementation of industry 4.0. J. Eng. Sci. 2019;6:b8–b13. doi: 10.21272/jes.2019.6(2).b2. DOI
Schmitt R.H., Voigtmann C. Sensor information as a service–component of networked production. J. Sens. Sens. Syst. 2018;7:389–402. doi: 10.5194/jsss-7-389-2018. DOI
Nguyen K.T., Medjaher K. A new dynamic predictive maintenance framework using deep learning for failure prognostics. Reliab. Eng. Syst. Saf. 2019;188:251–262. doi: 10.1016/j.ress.2019.03.018. DOI
Selcuk S. Predictive maintenance, its implementation and latest trends. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2017;231:1670–1679. doi: 10.1177/0954405415601640. DOI
Tortorella G.L. An empirical analysis of total quality management and total productive maintenance in industry 4.0; Proceedings of the International Conference on Industrial Engineering and Operations Management (IEOM); Pretoria/Johannesburg, South Africa. 29 October–1 November 2018; pp. 742–753.
Bukhsh Z.A., Saeed A., Stipanovic I., Doree A.G. Predictive maintenance using tree-based classification techniques: A case of railway switches. Transp. Res. Part C Emerg. Technol. 2019;101:35–54. doi: 10.1016/j.trc.2019.02.001. DOI
Li D., Landström A., Fast-Berglund Å., Almström P. Human-centred dissemination of data, information and knowledge in industry 4.0. Procedia CIRP. 2019;84:380–386. doi: 10.1016/j.procir.2019.04.261. DOI
Herrmann F. The smart factory and its risks. System. 2018;6:38. doi: 10.3390/systems6040038. DOI
Lee S.M., Lee D., Kim Y.S. The quality management ecosystem for predictive maintenance in the Industry 4.0 era. Int. J. Qual. Innov. 2019;5:4. doi: 10.1186/s40887-019-0029-5. DOI
Farooq B., Bao J., Li J., Liu T., Yin S. Data-driven predictive maintenance approach for spinning cyber-physical production system. J. Shanghai Jiaotong Univ. Sci. 2020;25:453–462. doi: 10.1007/s12204-020-2178-z. DOI
Shi Z., Xie Y., Xue W., Chen Y., Fu L., Xu X. Smart factory in Industry 4.0. Syst. Res. Behav. Sci. 2020;37:607–617. doi: 10.1002/sres.2704. DOI
Mabkhot M.M., Al-Ahmari A.M., Salah B., Alkhalefah H. Requirements of the smart factory system: A survey and perspective. Machines. 2018;6:23. doi: 10.3390/machines6020023. DOI
Frank A.G., Dalenogare L.S., Ayala N.F. Industry 4.0 technologies: Implementation patterns in manufacturing companies. Int. J. Prod. Econ. 2019;210:15–26. doi: 10.1016/j.ijpe.2019.01.004. DOI
Rojko A. Industry 4.0 concept: Background and overview. Int. J. Interact. Mob. Technol. IJIM. 2017;11:77–90. doi: 10.3991/ijim.v11i5.7072. DOI
Pai M., McCulloch M., Gorman J.D., Pai N., Enanoria W., Kennedy G., Tharyan P., Colford J.M. Systematic reviews and meta-analyses: An illustrated, step-by-step guide. Natl. Med. J. India. 2004;17:86–95. PubMed
Aromataris E., Riitano D. Systematic reviews. AJN Am. J. Nurs. 2014;114:49–56. doi: 10.1097/01.NAJ.0000446779.99522.f6. PubMed DOI
Van Eck N.J., Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84:523–538. doi: 10.1007/s11192-009-0146-3. PubMed DOI PMC
Kleinberg J. Bursty and hierarchical structure in streams. Data Min. Knowl. Discov. 2003;7:373–397. doi: 10.1023/A:1024940629314. DOI
Sci2 Team Science of Science (Sci2) Tool. [(accessed on 1 September 2020)]; Available online: https://sci2.cns.iu.edu.
Moher D., Liberati A., Tetzlaff J., Altman D.G. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Int. J. Surg. 2010;8:336–341. doi: 10.1016/j.ijsu.2010.02.007. PubMed DOI
Hooper L., Bartlett C., Davey Smith G., Ebrahim S. Reduced dietary salt for prevention of cardiovascular disease (Cochrane Review) Cochrane Database Syst. Rev. 2003 doi: 10.1002/14651858.CD003656.pub2. PubMed DOI
Aheleroff S., Xu X., Lu Y., Aristizabal M., Velásquez J.P., Joa B., Valencia Y. IoT-enabled smart appliances under industry 4.0: A case study. Adv. Eng. Inform. 2020;43:101043. doi: 10.1016/j.aei.2020.101043. DOI
Hopkins J., Hawking P. Big dataanalytics and IoT in logistics: A case study. Int. J. Logist. Manag. 2018;29:575–591. doi: 10.1108/IJLM-05-2017-0109. DOI
Varsha P.V., Sankar S.R., Mathew R.J. Predictive analysis using big data analytics for sensors used in fleet truck monitoring system. Int. J. Eng. Technol. 2016;8:714–719.
Taie M.A., Moawad E.M., Diab M., Elhelw M. Remote diagnosis, maintenance and prognosis for advanced driver assistance systems using machine learning algorithms. SAE Int. J. Passeng. Cars Electron. Electr. Syst. 2016;9:114–122. doi: 10.4271/2016-01-0076. DOI
Djurdjanovic D., Lee J., Ni J. Watchdog Agent—An infotronics-based prognostics approach for product performance degradation assessment and prediction. Adv. Eng. Inform. 2003;17:109–125. doi: 10.1016/j.aei.2004.07.005. DOI
Hsu H.-Y., Srivastava G., Wu H.-T., Chen M.-Y. Remaining useful life prediction based on state assessment using edge computing on deep learning. Comput. Commun. 2020;160:91–100. doi: 10.1016/j.comcom.2020.05.035. DOI
Lu Y.-W., Hsu C.-Y., Huang K.-C. An autoencoder gated recurrent unit for remaining useful life prediction. Processes. 2020;8:1155. doi: 10.3390/pr8091155. DOI
Alonso Á., Pozo A., Cantera J.M., De La Vega F., Hierro J.J. Industrial data space architecture implementation using FIWARE. Sensors. 2018;18:2226. doi: 10.3390/s18072226. PubMed DOI PMC
Karimanzira D., Rauschenbach T. Enhancing aquaponics management with IoT-based predictive analytics for efficient information utilization. Inf. Process. Agric. 2019;6:375–385. doi: 10.1016/j.inpa.2018.12.003. DOI
Hwang J.-Y., Myung S.-T., Sun Y.-K. Sodium-ion batteries: Present and future. Chem. Soc. Rev. 2017;46:3529–3614. doi: 10.1039/C6CS00776G. PubMed DOI
Palomares V., Serras P., Villaluenga I., Hueso K.B., Carretero-González J., Rojo T. Na-ion batteries, recent advances and present challenges to become low cost energy storage systems. Energy Environ. Sci. 2012;5:5884–5901. doi: 10.1039/c2ee02781j. DOI
Buzea C., Pacheco I.I., Robbie K. Nanomaterials and nanoparticles: Sources and toxicity. Biointerphases. 2007;2:MR17–MR71. doi: 10.1116/1.2815690. PubMed DOI
Leitão P. Agent-based distributed manufacturing control: A state-of-the-art survey. Eng. Appl. Artif. Intell. 2009;22:979–991. doi: 10.1016/j.engappai.2008.09.005. DOI
McFarlane D., Sarma S., Chirn J.L., Wong C., Ashton K. Auto ID systems and intelligent manufacturing control. Eng. Appl. Artif. Intell. 2003;16:365–376. doi: 10.1016/S0952-1976(03)00077-0. DOI
Wang S., Wan J., Li D., Zhang C. Implementing smart factory of industrie 4.0: An outlook. Int. J. Distrib. Sens. Netw. 2016;12 doi: 10.1155/2016/3159805. DOI
Kanchev H., Lu D., Colas F., Lazarov V., Francois B. Energy management and operational planning of a microgrid with a PV-based active generator for smart grid applications. IEEE Trans. Ind. Electron. 2011;58:4583–4592. doi: 10.1109/TIE.2011.2119451. DOI
Kamilaris A., Prenafeta-Boldú F.X. Deep learning in agriculture: A survey. Comput. Electron. Agric. 2018;147:70–90. doi: 10.1016/j.compag.2018.02.016. DOI
Wang S., Wan J., Zhang D., Li D., Zhang C. Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Comput. Netw. 2016;101:158–168. doi: 10.1016/j.comnet.2015.12.017. DOI
Gubbi J., Buyya R., Marusic S., Palaniswami M. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Gener. Comput. Syst. 2013;29:1645–1660. doi: 10.1016/j.future.2013.01.010. DOI
Yick J., Mukherjee B., Ghosal D. Wireless sensor network survey. Comput. Netw. 2008;52:2292–2330. doi: 10.1016/j.comnet.2008.04.002. DOI
Peppas N.A., Hilt J.Z., Khademhosseini A., Langer R. Hydrogels in biology and medicine: From molecular principles to bionanotechnology. Adv. Mater. 2006;18:1345–1360. doi: 10.1002/adma.200501612. DOI
Housner G.W., Bergman L.A., Caughey T.K., Chassiakos A.G., Claus R.O., Masri S.F., Skelton R.E., Soong T.T., Spencer B.F., Yao J.T.P. Structural control: Past, present, and future. J. Eng. Mech. 1997;123:897–971. doi: 10.1061/(ASCE)0733-9399(1997)123:9(897). DOI
Ou J., Li H. Structural health monitoring in mainland China: Review and future trends. Struct. Health Monit. 2010;9:219–231. doi: 10.1177/1475921710365269. DOI
Halim D., Moheimani S. Spatial resonant control of flexible structures-application to a piezoelectric laminate beam. IEEE Trans. Control Syst. Technol. 2001;9:37–53. doi: 10.1109/87.896744. DOI
Annigeri A.R., Ganesan N., Swarnamani S. Free vibration behaviour of multiphase and layered magneto-electro-elastic beam. J. Sound Vib. 2007;299:44–63. doi: 10.1016/j.jsv.2006.06.044. DOI
Atzori L., Lera A., Morabito G. The internet of things: A survey. Tạp Chí Nghiên Cứu Dân Tộc. 2018;54 doi: 10.25073/0866-773X/64. DOI
Pantelopoulos A., Bourbakis N.G. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. Man Cybern. C. 2010;40:1–12. doi: 10.1109/TSMCC.2009.2032660. DOI
Zhang Q., Yang L.T., Chen Z., Li P. A survey on deep learning for big data. Inf. Fusion. 2018;42:146–157. doi: 10.1016/j.inffus.2017.10.006. DOI
Gupta H., Dastjerdi A.V., Ghosh S.K., Buyya R. iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, edge and fog computing environments. Softw. Pract. Exp. 2017;47:1275–1296. doi: 10.1002/spe.2509. DOI
Ang K.H., Chong G., Li Y. PID control system analysis, design, and technology. IEEE Trans. Control. Syst. Technol. 2005;13:559–576. doi: 10.1109/tcst.2005.847331. DOI
Widodo A., Yang B.-S. Support vector machine in machine condition monitoring and fault diagnosis. Mech. Syst. Signal Process. 2007;21:2560–2574. doi: 10.1016/j.ymssp.2006.12.007. DOI
Peng Y., Dong M., Zuo M.J. Current status of machine prognostics in condition-based maintenance: A review. Int. J. Adv. Manuf. Technol. 2010;50:297–313. doi: 10.1007/s00170-009-2482-0. DOI
Van Noortwijk J. A survey of the application of gamma processes in maintenance. Reliab. Eng. Syst. Saf. 2009;94:2–21. doi: 10.1016/j.ress.2007.03.019. DOI
Xu X., Newman S. Making CNC machine tools more open, interoperable and intelligent—A review of the technologies. Comput. Ind. 2006;57:141–152. doi: 10.1016/j.compind.2005.06.002. DOI
McArthur S., Strachan S., Jahn G. The design of a multi-agent transformer condition monitoring system. IEEE Trans. Power Syst. 2004;19:1845–1852. doi: 10.1109/TPWRS.2004.835667. DOI
Wang G., Gunasekaran A., Ngai E.W., Papadopoulos T. Big data analytics in logistics and supply chain management: Certain investigations for research and applications. Int. J. Prod. Econ. 2016;176:98–110. doi: 10.1016/j.ijpe.2016.03.014. DOI
Bevilacqua M., Braglia M. The analytic hierarchy process applied to maintenance strategy selection. Reliab. Eng. Syst. Saf. 2000;70:71–83. doi: 10.1016/S0951-8320(00)00047-8. DOI
Gao R., Wang L., Teti R., Dornfeld D., Kumara S., Mori M., Helu M. Cloud-enabled prognosis for manufacturing. CIRP Ann. 2015;64:749–772. doi: 10.1016/j.cirp.2015.05.011. DOI
International Federation of Robotics Robot Density in the Manufacturing Industry 2019. [(accessed on 5 December 2020)]; Available online: https://ifr.org/downloads/press2018/Robot_density_by_country_2019_-_chart.png.
Manikandan G., Perumal R. Symmetric cryptography for secure communication in IoT. Mater. Today Proc. 2020 doi: 10.1016/j.matpr.2020.09.737. DOI
Xu W., Cai Y., Gao S., Hou S., Yang Y., Duan Y., Fu Q., Chen F., Wu J. New understanding of miniaturized VOCs monitoring device: PID-type sensors performance evaluations in ambient air. Sens. Actuators B Chem. 2021;330:129285. doi: 10.1016/j.snb.2020.129285. DOI
Antons O., Arlinghaus J.C. Designing decision-making authorities for smart factories. Procedia CIRP. 2020;93:316–322. doi: 10.1016/j.procir.2020.04.047. DOI
Goodall P., Sharpe R., West A. A data-driven simulation to support remanufacturing operations. Comput. Ind. 2019;105:48–60. doi: 10.1016/j.compind.2018.11.001. DOI
Yan J., Lee J., Pan Y.-C. Introduction of watchdog prognostics agent and its application to elevator hoistway performance assessment. J. Chin. Inst. Ind. Eng. 2005;22:56–63. doi: 10.1080/10170660509509277. DOI
Kiangala K.S., Wang Z. Initiating predictive maintenance for a conveyor motor in a bottling plant using industry 4.0 concepts. Int. J. Adv. Manuf. Technol. 2018;97:3251–3271. doi: 10.1007/s00170-018-2093-8. DOI
Starr A., Willetts R., Hannah P., Hu W., Banjevic D., Jardine A.K.S. Data fusion applications in intelligent condition monitoring; Proceedings of the 6th WSEAS International Multiconference on Circuits, Systems, Communications and Computers (CSCC 2002); Rethymno, Greece. 7–14 July 2002; pp. 110–115.
Li J., Tao H., Shuhong L., Salih S.Q., Zain J.M., Yankun L., Vivekananda G.N., Thanjaivadel M. Internet of things assisted condition-based support for smart manufacturing industry using learning technique. Comput. Intell. 2020;36:1737–1754. doi: 10.1111/coin.12319. DOI
Suh J.H., Kumara S.R., Mysore S.P. Machinery fault diagnosis and prognosis: Application of advanced signal processing techniques. CIRP Ann. 1999;48:317–320. doi: 10.1016/S0007-8506(07)63192-8. DOI
Peng C.-C., Tsan L.-G. IEPE accelerometer fault diagnosis for maintenance management system information integration in a heavy industry. J. Ind. Inf. Integr. 2020;17:100120. doi: 10.1016/j.jii.2019.100120. DOI
Barbieri M., Nguyen K.T.P., Diversi R., Medjaher K., Tilli A. RUL prediction for automatic machines: A mixed edge-cloud solution based on model-of-signals and particle filtering techniques. J. Intell. Manuf. 2020:1–20. doi: 10.1007/s10845-020-01696-6. PubMed DOI
Çınar Z.M., Nuhu A.A., Zeeshan Q., Korhan O., Asmael M., Safaei B. Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability. 2020;12:8211. doi: 10.3390/su12198211. DOI
Tinga T. Principles of Loads and Failure Mechanisms. Springer International Publishing; London, UK: 2013.
Kumar A., Chinnam R.B., Tseng F. An HMM and polynomial regression based approach for remaining useful life and health state estimation of cutting tools. Comput. Ind. Eng. 2019;128:1008–1014. doi: 10.1016/j.cie.2018.05.017. DOI
Lin C.-C., Deng D.-J., Kuo C.-H., Chen L. Concept drift detection and adaption in big imbalance industrial iot data using an ensemble learning method of offline classifiers. IEEE Access. 2019;7:56198–56207. doi: 10.1109/ACCESS.2019.2912631. DOI
Musselman M., Djurdjanovic D. Tension monitoring in a belt-driven automated material handling system. CIRP J. Manuf. Sci. Technol. 2012;5:67–76. doi: 10.1016/j.cirpj.2011.08.002. DOI
Sadiki S., Ramadany M., Faccio M., Amegouz D., Boutahari S. Running smart monitoring maintenance application using cooja simulator. Int. J. Eng. Res. Afr. 2019;42:149–159. doi: 10.4028/www.scientific.net/JERA.42.149. DOI
Doostparast M., Kolahan F., Doostparast M. A reliability-based approach to optimize preventive maintenance scheduling for coherent systems. Reliab. Eng. Syst. Saf. 2014;126:98–106. doi: 10.1016/j.ress.2014.01.010. DOI
Al-Jlibawi A., Othman I.M.L., Al-Huseiny M.S., Bin Aris I., Noor S.B.M. Efficient soft sensor modelling for advanced manufacturing systems by applying hybrid intelligent soft computing techniques. Int. J. Simul. Syst. Sci. Technol. 2019 doi: 10.5013/IJSSST.a.19.03.15. DOI
Bekar E.T., Nyqvist P., Skoogh A. An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study. Adv. Mech. Eng. 2020;12:168781402091920. doi: 10.1177/1687814020919207. DOI
Chien C.-F., Chen C.-C. Data-driven framework for tool health monitoring and maintenance strategy for smart manufacturing. IEEE Trans. Semicond. Manuf. 2020;33:1. doi: 10.1109/TSM.2020.3024284. DOI
Kozłowski E., Mazurkiewicz D., Żabiński T., Prucnal S., Sęp J. Machining sensor data management for operation-level predictive model. Expert Syst. Appl. 2020;159:113600. doi: 10.1016/j.eswa.2020.113600. DOI
Lao L., Ellis M., Durand H., Christofides P.D. Real-time preventive sensor maintenance using robust moving horizon estimation and economic model predictive control. AIChE J. 2015;61:3374–3389. doi: 10.1002/aic.14960. DOI
Park B., Jeong H., Huh H., Kim M., Lee S. Experimental study on the life prediction of servo motors through model-based system degradation assessment and accelerated degradation testing. J. Mech. Sci. Technol. 2018;32:5105–5110. doi: 10.1007/s12206-018-1007-x. DOI
Peng C.C., Kuo C.H., Wu C.Y. Graphical histogram algorithm for integrated-circuit-piezoelectric-type accelerometer for health condition diagnosis and monitoring. Sens. Mater. 2017;29:1645–1656. doi: 10.18494/SAM.2017.1738. DOI
Shan L., Wang Z.R., Jiang C. Key technologies of real-time visualization system for intelligent manufacturing equipment operating state under hot environment. J. Internet Technol. 2020;21:1479–1489. doi: 10.3966/160792642020092105021. DOI
Tarashioon S., Baiano A., Van Zeijl H., Guo C., Koh S., Van Driel W., Zhang G. An approach to “Design for Reliability” in solid state lighting systems at high temperatures. Microelectron. Reliab. 2012;52:783–793. doi: 10.1016/j.microrel.2011.06.029. DOI
Tsao Y.-C., Lee P.-L., Liao L.-W., Zhang Q., Vu T.-L., Tsai J. Imperfect economic production quantity models under predictive maintenance and reworking. Int. J. Syst. Sci. Oper. Logist. 2020;7:347–360. doi: 10.1080/23302674.2019.1590663. DOI
Uhlmann E., Laghmouchi A., Geisert C., Hohwieler E. Smart wireless sensor network and configuration of algorithms for condition monitoring applications. J. Mach. Eng. 2017;17:45–55.
Villalobos K., Suykens J., Illarramendi A. A flexible alarm prediction system for smart manufacturing scenarios following a forecaster–analyzer approach. J. Intell. Manuf. 2020:1–22. doi: 10.1007/s10845-020-01614-w. PubMed DOI
Vlasov A.I., Grigoriev P.V., Krivoshein A.I., Shakhnov V.A., Filin S.S., Migalin V.S. Smart management of technologies: Predictive maintenance of industrial equipment using wireless sensor networks. Entrep. Sustain. Issues. 2018;6:489–502. doi: 10.9770/jesi.2018.6.2(2). DOI
Yan J., Meng Y., Lu L., Li L. Industrial big data in an industry 4.0 environment: Challenges, schemes, and applications for predictive maintenance. IEEE Access. 2017;5:23484–23491. doi: 10.1109/ACCESS.2017.2765544. DOI
Zhang H., Zhang Q., Shao S., Niu T., Yang X. Attention-based LSTM network for rotatory machine remaining useful life prediction. IEEE Access. 2020;8:132188–132199. doi: 10.1109/ACCESS.2020.3010066. DOI
Zhang Y., Cheng Y., Wang X.V., Zhong R.Y., Zhang Y., Tao F. Data-driven smart production line and its common factors. Int. J. Adv. Manuf. Technol. 2019;103:1211–1223. doi: 10.1007/s00170-019-03469-9. DOI
Luo Z., Hu X., Borisenko V.E., Chu J., Tian X., Luo C., Xu H., Li Q., Li Q., Zhang J., et al. Structure-property relationships in graphene-based strain and pressure sensors for potential artificial intelligence applications. Sensors. 2019;19:1250. doi: 10.3390/s19051250. PubMed DOI PMC
Cottone P., Re G.L., Maida G., Morana M. Motion sensors for activity recognition in an ambient-intelligence scenario; Proceedings of the 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops); San Diego, CA, USA. 18–22 March 2013; pp. 646–651.
Shoaib M., Bosch S., Incel O.D., Scholten J., Havinga P.J.M. Fusion of smartphone motion sensors for physical activity recognition. Sensors. 2014;14:10146–10176. doi: 10.3390/s140610146. PubMed DOI PMC
Kaptan C., Kantarci B., Soyata T., Boukerche A. Emulating smart city sensors using soft sensing and machine intelligence: A case study in public transportation; Proceedings of the 2018 IEEE International Conference on Communications (ICC); Kansas City, MO, USA. 20–24 May 2018; pp. 1–7.
Ryu S., Kim S.-C. Impact sound-based surface identification using smart audio sensors with deep neural networks. IEEE Sens. J. 2020;20:10936–10944. doi: 10.1109/JSEN.2020.2993321. DOI
Mennel L., Symonowicz J., Wachter S., Polyushkin D.K., Molina-Mendoza A.J., Mueller T. Ultrafast machine vision with 2D material neural network image sensors. Nat. Cell Biol. 2020;579:62–66. doi: 10.1038/s41586-020-2038-x. PubMed DOI
Sergiyenko O., Tyrsa V., Flores-Fuentes W., Rodriguez-Quiñonez J., Mercorelli P. Machine vision sensors. J. Sens. 2018;2018:3202761. doi: 10.1155/2018/3202761. DOI
Rodriguez-Vazquez A., Fernandez-Berni J., Lenero-Bardallo J.A., Vornicu I., Carmona-Galan R. CMOS vision sensors: Embedding computer vision at imaging front-ends. IEEE Circuits Syst. Mag. 2018;18:90–107. doi: 10.1109/MCAS.2018.2821772. DOI
Ping J., Wang Y., Wu J., Ying Y. Development of an electrochemically reduced graphene oxide modified disposable bismuth film electrode and its application for stripping analysis of heavy metals in milk. Food Chem. 2014;151:65–71. doi: 10.1016/j.foodchem.2013.11.026. PubMed DOI
Schroeder V., Savagatrup S., He M., Lin S., Swager T.M. Carbon nanotube chemical sensors. Chem. Rev. 2018;119:599–663. doi: 10.1021/acs.chemrev.8b00340. PubMed DOI PMC
Salvatore G.A., Sülzle J., Kirchgessner N., Hopf R., Magno M., Tröster G., Valle F.D., Cantarella G., Robotti F., Jokic P., et al. Biodegradable and highly deformable temperature sensors for the internet of things. Adv. Funct. Mater. 2017;27 doi: 10.1002/adfm.201702390. DOI
Farahani H., Wagiran R., Hamidon M.N. Humidity sensors principle, mechanism, and fabrication technologies: A comprehensive review. Sensors. 2014;14:7881–7939. doi: 10.3390/s140507881. PubMed DOI PMC
Alberto N., Domingues M.F., Marques C., André P., Antunes P. Optical fiber magnetic field sensors based on magnetic fluid: A review. Sensors. 2018;18:4325. doi: 10.3390/s18124325. PubMed DOI PMC
Jureschi C.-M., Linares J., Boulmaali A., Dahoo P.R., Rotaru A., Garcia Y. Pressure and temperature sensors using two spin crossover materials. Sensors. 2016;16:187. doi: 10.3390/s16020187. PubMed DOI PMC
Wang T., Guo Y., Wan P., Zhang H., Chen X., Sun X. Flexible transparent electronic gas sensors. Small. 2016;12:3748–3756. doi: 10.1002/smll.201601049. PubMed DOI
Indri M., Lachello L., Lazzero I., Sibona F., Trapani S. Smart sensors applications for a new paradigm of a production line. Sensors. 2019;19:650. doi: 10.3390/s19030650. PubMed DOI PMC
Jia L., Chen R., Xu J., Zhang L., Chen X., Bi N., Gou J., Zhao T. A stick-like intelligent multicolor nano-sensor for the detection of tetracycline: The integration of nano-clay and carbon dots. J. Hazard. Mater. 2021;125296:125296. doi: 10.1016/j.jhazmat.2021.125296. PubMed DOI
Thakkar S., Dumée L.F., Gupta M., Singh B.R., Yang W. Nano–enabled sensors for detection of arsenic in water. Water Res. 2021;188:116538. doi: 10.1016/j.watres.2020.116538. PubMed DOI
Singh K., Sharma S., Shriwastava S., Singla P., Gupta M., Tripathi C. Significance of nano-materials, designs consideration and fabrication techniques on performances of strain sensors-A review. Mater. Sci. Semicond. Process. 2021;123:105581. doi: 10.1016/j.mssp.2020.105581. DOI
Büchi G., Cugno M., Castagnoli R. Smart factory performance and Industry 4.0. Technol. Forecast. Soc. Chang. 2020;150:119790. doi: 10.1016/j.techfore.2019.119790. DOI
Kalsoom T., Ramzan N., Ahmed S., Ur-Rehman M. Advances in sensor technologies in the era of smart factory and industry 4.0. Sensors. 2020;20:6783. doi: 10.3390/s20236783. PubMed DOI PMC
Zunino C., Valenzano A., Obermaisser R., Petersen S. Factory communications at the dawn of the fourth industrial revolution. Comput. Stand. Interfaces. 2020;71:103433. doi: 10.1016/j.csi.2020.103433. DOI
Vrchota J., Řehoř P., Maříková M., Pech M. Critical success factors of the project management in relation to industry 4.0 for sustainability of projects. Sustainability. 2020;13:281. doi: 10.3390/su13010281. DOI
Lujak M., Fernández A., Onaindia E. Spillover Algorithm: A decentralised coordination approach for multi-robot production planning in open shared factories. Robot. Comput. Manuf. 2021;70:102110. doi: 10.1016/j.rcim.2020.102110. DOI
Shpilevoy V., Shishov A., Skobelev P., Kolbova E., Kazanskaia D., Shepilov Y., Tsarev A., Shepilov Y. Multi-agent system “Smart Factory” for real-time workshop management in aircraft jet engines production. IFAC Proc. Vol. 2013;46:204–209. doi: 10.3182/20130522-3-BR-4036.00025. DOI
Leusin M.E., Kück M., Frazzon E.M., Maldonado M.U., Freitag M. Potential of a multi-agent system approach for production control in smart factories. IFAC PapersOnLine. 2018;51:1459–1464. doi: 10.1016/j.ifacol.2018.08.309. DOI
Nunes M.L., Pereira A., Alves A. Smart products development approaches for Industry 4.0. Procedia Manuf. 2017;13:1215–1222. doi: 10.1016/j.promfg.2017.09.035. DOI
Wang Y., Ma H.-S., Yang J.-H., Wang K.-S. Industry 4.0: A way from mass customization to mass personalization production. Adv. Manuf. 2017;5:311–320. doi: 10.1007/s40436-017-0204-7. DOI
Yang J., Xie H., Yu G., Liu M. Achieving a just–in–time supply chain: The role of supply chain intelligence. Int. J. Prod. Econ. 2021;231:107878. doi: 10.1016/j.ijpe.2020.107878. DOI
Kumar M., Shenbagaraman V.M., Shaw R.N., Ghosh A. Predictive data analysis for energy management of a smart factory leading to sustainability. In: Favorskaya M.N., Mekhilef S., Pandey R.K., Singh N., editors. Innovations in Electrical and Electronic Engineering. Volume 661. Springer Singapore; Singapore: 2021. pp. 765–773. (Lecture Notes in Electrical Engineering).
Cavalieri S., Salafia M.G. A model for predictive maintenance based on asset administration shell. Sensors. 2020;20:6028. doi: 10.3390/s20216028. PubMed DOI PMC
Jimenez-Cortadi A., Irigoien I., Boto F., Sierra B., Rodriguez G. Predictive maintenance on the machining process and machine tool. Appl. Sci. 2019;10:224. doi: 10.3390/app10010224. DOI
Carlson A., Sakao T. Environmental assessment of consequences from predictive maintenance with artificial intelligence techniques: Importance of the system boundary. Procedia CIRP. 2020;90:171–175. doi: 10.1016/j.procir.2020.01.093. DOI
Xu Y., Nascimento N.M.M., De Sousa P.H.F., Nogueira F.G., Torrico B.C., Han T., Jia C., Filho P.P.R. Multi-sensor edge computing architecture for identification of failures short-circuits in wind turbine generators. Appl. Soft Comput. 2021;101:107053. doi: 10.1016/j.asoc.2020.107053. DOI
Xiang W., Tian Y., Liu X. Theoretical analysis of detection sensitivity in nano-resonator-based sensors for elasticity and density measurement. Int. J. Mech. Sci. 2021;197:106309. doi: 10.1016/j.ijmecsci.2021.106309. DOI
Fernandes S., Antunes M., Santiago A.R., Barraca J.P., Gomes D., Aguiar R.L. Forecasting appliances failures: A machine-learning approach to predictive maintenance. Information. 2020;11:208. doi: 10.3390/info11040208. DOI
Verhagen W.J., De Boer L.W. Predictive maintenance for aircraft components using proportional hazard models. J. Ind. Inf. Integr. 2018;12:23–30. doi: 10.1016/j.jii.2018.04.004. DOI
Ruhi S., Karim M.R. Selecting statistical model and optimum maintenance policy: A case study of hydraulic pump. SpringerPlus. 2016;5:969. doi: 10.1186/s40064-016-2619-1. PubMed DOI PMC
Stodola P., Stodola J. Stodola Model of predictive maintenance of machines and equipment. Appl. Sci. 2019;10:213. doi: 10.3390/app10010213. DOI
Warneke B.A., Pister K.S.J. MEMS for distributed wireless sensor networks; Proceedings of the 9th International Conference on Electronics, Circuits and Systems; Doubrovnik, Croatia. 15–18 September 2002; pp. 291–294.
Ovsthus A.A.K., Kristensen L.M. An industrial perspective on wireless sensor networks—a survey of requirements, protocols, and challenges. IEEE Commun. Surv. Tutor. 2014;16:1391–1412. doi: 10.1109/surv.2014.012114.00058. DOI
Flammini A., Ferrari P., Marioli D., Sisinni E., Taroni A. Wired and wireless sensor networks for industrial applications. Microelectron. J. 2009;40:1322–1336. doi: 10.1016/j.mejo.2008.08.012. DOI
Li X., Li D., Wan J., Vasilakos A.V., Lai C.-F., Wang S. A review of industrial wireless networks in the context of Industry 4.0. Wirel. Netw. 2017;23:23–41. doi: 10.1007/s11276-015-1133-7. DOI
Usamentiaga R., Venegas P., Guerediaga J., Vega L., Molleda J., Bulnes F.G. Infrared thermography for temperature measurement and non-destructive testing. Sensors. 2014;14:12305–12348. doi: 10.3390/s140712305. PubMed DOI PMC
Akerberg J., Gidlund M., Bjorkman M. Future research challenges in wireless sensor and actuator networks targeting industrial automation; Proceedings of the 2011 9th IEEE International Conference on Industrial Informatics; Lisbon, Portugal. 26–29 July 2011; pp. 410–415.
Park H., Kim H., Joo H., Song J. Recent advancements in the Internet of Things related standards: A oneM2M perspective. ICT Express. 2016;2:126–129. doi: 10.1016/j.icte.2016.08.009. DOI
Cavalieri S., Salafia M.G., Scroppo M.S. Towards interoperability between OPC UA and OCF. J. Ind. Inf. Integr. 2019;15:122–137. doi: 10.1016/j.jii.2019.01.002. DOI
Turk Ž. Interoperability in construction—Mission impossible? Dev. Built Environ. 2020;4:100018. doi: 10.1016/j.dibe.2020.100018. DOI
Salarian H., Chin K.-W., Naghdy F. Coordination in wireless sensor–actuator networks: A survey. J. Parallel Distrib. Comput. 2012;72:856–867. doi: 10.1016/j.jpdc.2012.02.013. DOI
Kullaa J. Robust damage detection using Bayesian virtual sensors. Mech. Syst. Signal Process. 2020;135:106384. doi: 10.1016/j.ymssp.2019.106384. DOI
Villagrossi E., Simoni L., Beschi M., Pedrocchi N., Marini A., Tosatti L.M., Visioli A. A virtual force sensor for interaction tasks with conventional industrial robots. Mechatronics. 2018;50:78–86. doi: 10.1016/j.mechatronics.2018.01.016. DOI
Landolfi G., Barni A., Izzo G., Fontana A., Bettoni A. A MaaS platform architecture supporting data sovereignty in sustainability assessment of manufacturing systems. Procedia Manuf. 2019;38:548–555. doi: 10.1016/j.promfg.2020.01.069. DOI
Toublanc T., Guillet S., De Lamotte F., Berruet P., Lapotre V. Using a virtual plant to support the development of intelligent gateway for sensors/actuators security. IFAC PapersOnLine. 2017;50:5837–5842. doi: 10.1016/j.ifacol.2017.08.541. DOI
Alcaraz C., Roman R., Najera P., Lopez J., Tello M.C.A. Security of industrial sensor network-based remote substations in the context of the Internet of Things. Ad Hoc Netw. 2013;11:1091–1104. doi: 10.1016/j.adhoc.2012.12.001. DOI
Gungor V.C., Hancke G.P. Industrial wireless sensor networks: Challenges, design principles, and technical approaches. IEEE Trans. Ind. Electron. 2009;56:4258–4265. doi: 10.1109/TIE.2009.2015754. DOI
Application Perspective on Cybersecurity Testbed for Industrial Control Systems