THE MAINTENANCE SYSTEM FOR A SMART BUILDING IN INDUSTRY 4.0 ENVIRONMENT
THE MAINTENANCE SYSTEM FOR A SMART BUILDING IN INDUSTRY 4.0 ENVIRONMENT
DOI:
https://doi.org/10.55766/sujst-2023-01-e01881Keywords:
Maintenance, Energy, Smart building, 4.0 IOT (IoT)Abstract
Buildings, where we spend the majority of our time, have been quickly altering in recent years. Building systems powering integrate consumer-grade Internet of things (IoT) sensors such as cameras with operational technology controllers and actuators to accommodate the increased complexity of building operations. This study discussed the idea of “smart buildings” and the comparison along with the technologies being developed for these structures and the implications for libraries and specialized smart building technology using various IoT approaches used in smart building categories. The research goal is to improve approaches which active in recent years and the gaps researchers have found in their investigations. As a result, smart buildings provide physical value by generating data intelligently to enhance operations and customer skills. Recent breakthroughs in IoT have piqued experts and engineers across the world. However, improvements are only possible if we consider the numerous flaws and mistakes in the present technology. IoT developers might consider many challenges and hurdles while designing a better model in this survey article. Significant IoT technology sectors in which IoT developers and academics are interested are frequently addressed. In addition to providing services, IoT captures a vast amount of data. Therefore, big data analytics is essential, trustworthy judgments can be made, and a more robust IoT framework can be created. This study compares the previous studies that focused on characteristics that a controlling and advanced approach to making buildings smarter with Maintenance to reduce greenhouse gas emissions and enhance the world's environment at a lower cost.
References
Abbasi, M.A., A.R. Husain., N.R, Nik Idris., and S.M, Fasih ur Rehman. (2020). Computationally efficient predictive torque control for induction motor drives based on flux positional errors and extended Kalman filter. IET Electr. Power Appl., doi: 10.1049/elp2.12035.
Ahammad, I., M.A.R, Khan., and Z.U, Salehin. (2021). QoS Performance Enhancement Policy through Combining Fog and SDN. Simul. Model. Pract. Theory., 109:102,292. doi: 10.1016/j.simpat.2021.102292.
Ahmadipour, M., Murtadha, Othman M., Alrifaey, M., Bo, R., and Kit Ang, C. (2022a). Classification of faults in grid-connected photovoltaic system based on wavelet packet transform and an equilibrium optimization algorithm-extreme learning machine. Measurement., 197:111,338. doi:10.1016/j.measurement.2022.111338.
Ahmadipour, M., Murtadha, Othman M., Salam, Z., Alrifaey, M., Mohammed, Ridha H., and Veerasamy, V. (2022b). Optimal load shedding scheme using grasshopper optimization algorithm for islanded power system with distributed energy resources. Ain Shams Engineering J., p. 101,835. doi:10.1016/j.asej.2022.101835.
Ahmed, Syed Hassan., and Shalli, Rani. (2018). A Hybrid Approach, Smart Street Use Case and Future Aspects for Internet of Things in Smart Cities. Future Generation Computer Systems., 79:941-51. https://doi.org/10.1016/j.future.2017.08.054.
Alrifaey, M., Lim, W.H., and Ang, C.K. (2021). A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator. IEEE Access., 9:21,433-21,442. doi:10.1109/ACCESS.2021.3055427.
Alrifaey, M., Lim, W.H., Ang, C.K., Natarajan, E., Solihin, M.I., Juhari, M.R.M., and Tiang, S.S. (2022). Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System. IEEE Access., 1-1, doi:10.1109/ACCESS.2022.3140287.
Alrifaey, M., Sai Hong, T., As’arry, A., Elianddy, Supeni E., and Ang, C.K. (2020). Optimization and Selection of Maintenance Policies in an Electrical Gas Turbine Generator Based on the Hybrid Reliability-Centered Maintenance (RCM) Model. Processes., 8:670, doi:10.3390/pr8060670.
Alshutbi, M., Li, Z., Alrifaey, M., Ahmadipour, M., and Othman, M.M. (2022). A hybrid classifier based on support vector machine and Jaya algorithm for breast cancer classification. Neural Computing and Applications. doi:10.1007/s00521-022-07290-6
Andoni, M. et al., (2018). Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renew. Sustain. Energy Rev., 100:143-174, doi: 10.1016/j.rser.2018.10.014.
Bag, S., G, Yadav., L.C, Wood., P, Dhamija., and S, Joshi. (2020). Industry 4.0 and the circular economy: Resource melioration in logistics. Resour. Policy., 68:101776. doi: 10.1016/j.resourpol.2020.101776.
Bauen, A., I. Gomez., D, Oude Nijeweme., and M, Paraschiv. (2018). Alternative Fuels Expert group report. Cybersecurity concerns for total productive maintenance in smart manufacturing systems. Procedia Manuf., 38:532-539. doi: 10.1016/j.promfg.2020.01.067.
Compare, M., P, Baraldi., and E, Zio. (2019). Challenges to IoT-Enabled Predictive Maintenance for Industry 4.0. IEEE Internet Things Journal., 7(5):4,585-4,597. doi: 10.1109/JIOT.2019.2957029.
Dalzochio, J. (2020). Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Comput. Ind., 123:103,298. doi: 10.1016/j.compind.2020.103298.
de la Peña Zarzuelo, I., M.J, Freire Soeane., and B, López Bermúdez. (2020). Industry 4.0 in the port and maritime industry: A literature review. J. Ind. Inf. Integr., 20:100,173. doi: 10.1016/j.jii.2020.100173.
Deng, Tianhu., Keren, Zhang., and Zuo-jun, Max Shen. (2021). A Systematic Review of a Digital Twin City: A New Pattern of Urban Governance toward Smart Cities. Journal of Management Science and Engineering., https://doi.org/10.1016/j.jmse.2021.03.003.
Dogan, Alican., and Derya, Birant. (2021). Machine Learning and Data Mining in Manufacturing. Expert Systems with Applications., 166:114,060. https://doi.org/10.1016/j.eswa.2020.114060.
Duan, L., and Da, Xu L. (2018). Big data for cyber-physical systems in industry 4.0: a survey. Enterp. Inf. Syst., 13(2):148-169. doi: 10.1080/17517575.1442934.
Dutta, Pramanik Pijush., Mukherjee, Bulbul., Pal, Saurabh., Pal, Tanmoy., and Singh, Simar Preet. (2019). Green Smart Building: Requisites, Architecture, Challenges, and Use Cases. 10.4018/978-1-5225-9754-4.ch001.
Elsisi, M., Tran, M.Q., Mahmoud, K., Lehtonen, M., and Darwish, M.M.F. (2021a). Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings. Sensors., 21(4). doi:10.3390/s21041038
Elsisi, M., Tran, M.Q., Mahmoud, K., Mansour, D.E.A., Lehtonen, M., and Darwish, M.M.F. (2021b). Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning. IEEE Access., 9:78,415-78,427. doi:10.1109/ACCESS.2021.3083499.
Faheem, M., S.B.H, Shah., R.A, Butt., B, Raza., M, Anwar., M.W, Ashraf., Md.A, Ngadi., and V.C, Gungor. (2018). Smart Grid Communication and Information Technologies in the Perspective of Industry 4.0: Opportunities and Challenges. Computer Science Review., 30:1-30. https://doi.org/10.1016/j.cosrev.2018.08.001.
Farhan, Syahida., Mohammad, Shahadat., Suzylawati, Ismail., and Syed, Wazed. (2021). Prospect of Clay-Based Fl Exible Adsorbent Coatings as Cleaner Production Technique in Wastewater Treatment, Challenges, and Issues : A Review. Journal of the Taiwan Institute of Chemical Engineers. https://doi.org/10.1016/j.jtice.2021.03.018.
Fatimah, Yun Arifatul., Kannan, Govindan., Rochiyati, Murniningsih., and Agus, Setiawan. (2020). Industry 4.0 Based Sustainable Circular Economy Approach for Smart Waste Management System to Achieve Sustainable Development Goals: A Case Study of Indonesia. Journal of Cleaner Production., 269:122,263. https://doi.org/10.1016/j.jclepro.2020.122263.
Foresti, Ruben., Stefano, Rossi., Matteo, Magnani., Corrado, Guarino Lo Bianco., and Nicola, Delmonte. (2020). Smart Society and Artificial Intelligence: Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance. Engineering., 6(7):835-46. https://doi.org/10.1016/j.eng.2019.11.014.
Froufe, Mariangela Monteiro., Christine, Kowal Chinelli., André, Luis Azevedo., Guedes, Assed Naked Haddad., Ahmed, W.A. Hammad., and Carlos, Alberto Pereira Soares. (2020). Smart Buildings: Systems and Drivers. Buildings., 10(9):153. https://doi.org/10.3390/buildings10090153.
Gao, X., P, Pishdad-Bozorgi., D.R, Shelden., and S, Tang. (2021). Internet of Things Enabled Data Acquisition Framework for Smart Building Applications. Journal Constr. Eng. Manag., 147(2):04020169. doi: 10.1061/(asce)co.1943-7862.0001983.
Gupta, H., A, Kumar., and P, Wasan. (2021). Industry 4.0, cleaner production and circular economy: An integrative framework for evaluating ethical and sustainable business performance of manufacturing organisations. J. Clean. Prod., 295:126,253. doi: 10.1016/j.jclepro.2021.126253.
Hoffmann, M.L., Souza, C.A. da Costa., G. de Oliveira, Ramos., and R. da Rosa, Righi. (2020). A survey on decision-making based on system reliability in the context of Industry 4.0. J. Manuf. Syst., 56(June):133-156. doi: 10.1016/j.jmsy.2020.05.016.
Javed, M.S., D. Zhong, T. Ma, A. Song., and S, Ahmed. (2019). Hybrid pumped hydro and battery storage for renewable energy based power supply system. Appl. Energy., 257:114,026. doi: 10.1016/j.apenergy.2019.114026.
Kamal, M.M., U. Sivarajah, A.Z., Bigdeli, F. Missi., and Y, Koliousis. (2020). Servitization implementation in the manufacturing organisations: Classification of strategies, definitions, benefits and challenges. Int. J. Inf. Manage., 55:102,206. doi: 10.1016/j.ijinfomgt.2020.102206.
Kamble, Sachin S., Angappa, Gunasekaran., Abhijeet, Ghadge., and Rakesh, Raut. (2020). A Performance Measurement System for Industry 4.0 Enabled Smart Manufacturing System in SMMEs- A Review and Empirical Investigation. International Journal of Production Economics., 229 (August 2019):107853. https://doi.org/10.1016/j.ijpe.2020.107853.
Kassab, Wafa’a., and Khalid A, Darabkh. (2020). A-Z Survey of Internet of Things: Architectures, Protocols, Applications, Recent Advances, Future Directions and Recommendations. Journal of Network and Computer Applications., 163:102,663. https://doi.org/10.1016/j.jnca.2020.102663.
Khan, I.S., M.O, Ahmad., and J, Majava. (2021). Industry 4.0 and sustainable development: A systematic mapping of triple bottom line, Circular Economy and Sustainable Business Models perspectives. J. Clean. Prod., 297:126,655. doi: 10.1016/j.jclepro.2021.126655.
Kim, Hakpyeong., Heeju, Choi., Hyuna, Kang., Jongbaek, An., Seungkeun, Yeom., and Taehoon, Hong. (2021). A Systematic Review of the Smart Energy Conservation System: From Smart Homes to Sustainable Smart Cities. Renewable and Sustainable Energy Reviews., 140:110,755. https://doi.org/10.1016/j.rser.2021.110755.
Kotsiopoulos, Thanasis., Panagiotis, Sarigiannidis., Dimosthenis, Ioannidis., and Dimitrios, Tzovaras. (2021). Machine Learning and Deep Learning in Smart Manufacturing: The Smart Grid Paradigm. Computer Science Review., 40:100,341. https://doi.org/10.1016/j.cosrev.2020.100341.
Kumar, Sachin., Tiwari, Prayag., and Zymbler, Mikhail. (2019). Internet of Things is a revolutionary approach for future technology enhancement: a review. Journal of Big Data., 6. 10.1186/s40537-019-0268-2.
Lim, Chun Hsion., Steven, Lim., Bing, Shen How., Wendy, Pei., Qin, Ng., Sue, Lin Ngan., Wei, Dong Leong., and Hon, Loong Lam. (2021).A Review of Industry 4.0 Revolution Potential in a Sustainable and Renewable Palm Oil Industry: HAZOP Approach. Renewable and Sustainable Energy Reviews., 135:110,223. https://doi.org/10.1016/j.rser.2020.110223.
Lu, Y., (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal Ind. Inf. Integr., 6:1-10. doi: 10.1016/j.jii.2017.04.005.
Luthra, S., and S.K, Mangla. (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Saf. Environ. Prot., 117:168-179. doi: 10.1016/j.psep.2018.04.018.
Majeed, Umer., Latif U, Khan., Ibrar, Yaqoob., S, M. Ahsan Kazmi., Khaled, Salah., and Choong, Seon Hong. (2021). Blockchain for IoT-Based Smart Cities: Recent Advances, Requirements, and Future Challenges. Journal of Network and Computer Applications., 181:103,007. https://doi.org/10.1016/j.jnca.2021.103007.
Manavalan, E., and K, Jayakrishna. (2018). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Comput. Ind. Eng., 127:925-953. doi: 10.1016/j.cie.2018.11.030.
Minoli, D., (2019). Positioning of blockchain mechanisms in IOT-powered smart home systems: A gateway-based approach. Internet of Things., 10:100,147. doi: 10.1016/j.iot.2019.100147.
Misra, S., Roy, C., and Mukherjee, A. (2021). Overview of Industry 4.0 and Industrial Internet of Things. Introduction to Industrial Internet of Things and Industry 4.0., 27-50. doi:10.1201/9781003020905-2.
Moayedi, H. and A, Mosavi. (2021). Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings. Energies., 14(5):1,331. doi: 10.3390/en14051331.
Moura, Jose., and David, Hutchison. (2020). Fog Computing Systems: State of the Art, Research Issues and Future Trends, with a Focus on Resilience. Journal of Network and Computer Applications., 169:102,784. https://doi.org/10.1016/j.jnca.2020.102784.
Onu, P., and C, Mbohwa. (2020). Industry 4.0 opportunities in manufacturing SMEs: Sustainability outlook. Mater. Today Proc., doi: 10.1016/j.matpr.2020.12.095.
Pan, Yue., and Limao, Zhang. (2021). A BIM-Data Mining Integrated Digital Twin Framework for Advanced Project Management. Automation in Construction., 124:103,564. https://doi.org/10.1016/j.autcon.2021.103564.
Pivoto, Diego G.S., Luiz, F.F. de Almeida, Rodrigo, da Rosa Righi., Joel, J.P.C. Rodrigues., Alexandre, Baratella Lugli., and Antonio, M. Alberti. (2021). Cyber-Physical Systems Architectures for Industrial Internet of Things Applications in Industry 4.0: A Literature Review. Journal of Manufacturing Systems., 58(PA):176-92. https://doi.org/10.1016/j.jmsy.2020.11.017.
Rahim, M. A., M. A, Rahman., M.M, Rahman., A.T, Asyhari., M.Z.A, Bhuiyan., and D, Ramasamy. (2020). Evolution of IoT-enabled connectivity and applications in automotive industry: A review. Veh. Commun., 27:100,285. doi: 10.1016/j.vehcom.2020.100285.
Ran, Y., X, Zhou., P, Lin., Y, Wen., and R, Deng. (2019). A survey of predictive maintenance: Systems, purposes and approaches. arXiv.
Ray, P.P. (2018). A Survey on Internet of Things Architectures. Journal of King Saud University - Computer and Information Sciences., 30(3):291-319. https://doi.org/10.1016/j.jksuci.2016.10.003.
Reka, S.S., and T, Dragicevic. (2018). Future effectual role of
energy delivery: A comprehensive review of Internet of Things and smart grid. Renew. Sustain. Energy Rev., 91:90-108. doi: 10.1016/j.rser.2018.03.089.
Robert, J., S, Kubler., and S, Ghatpande. (2020). Enhanced Lightning Network (off-chain)-based micropayment in IoT ecosystems. Futur. Gener. Comput. Syst., 112:283-296. doi: 10.1016/j.future.2020.05.033.
Salimi, Shide., and Amin, Hammad. (2019). Critical Review and Research Roadmap of Office Building Energy Management Based on Occupancy Monitoring. Energy and Buildings., 182:214-41. https://doi.org/10.1016/j.enbuild.2018.10.007.
Santoli, L.de., R, Paiolo., and G, Lo Basso. (2019). Energy-environmental experimental campaign on a commercial CHP fueled with H2NG blends and oxygen-enriched air hailing from on-site electrolysis,. Energy., 195:116,820. doi: 10.1016/j.energy.2019.116820.
SAP. (2021). What Is the Internet of Things (IoT)?. UK: AVSystem Company. Available from: https://insights.sap.com/what-is-iot-internet-of-things/.
Sarc, R., Curtis, A., Kandlbauer, L., Khodier, K., Lorber, K. E., and Pomberger, R. (2019). Digitalisation and intelligent robotics in value chain of circular economy oriented waste management - A review. Waste Management., 95:476-492. doi:10.1016/j.wasman.2019.06.035
Sengupta, Jayasree., Sushmita, Ruj., and Sipra, Das, Bit. (2020). A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT. Journal of Network and Computer Applications., 149:102,481. https://doi.org/10.1016/j.jnca.2019.102481.
Shao, X.F., W, Liu., Y, Li., H.R, Chaudhry., and X, G. Yue. (2020). Multistage implementation framework for smart supply chain management under industry 4.0. Technol. Forecast. Soc. Change., 162. doi: 10.1016/j.techfore.2020.120354.
Sunhare, P., R.R, Chowdhary., and M.K, Chattopadhyay. (2020). Internet of things and data mining: An application-oriented survey. J. King Saud Univ. - Comput. Inf. Sci., doi: 10.1016/j.jksuci.2020.07.002.
Tao, F., Q, Qi., L, Wang., and A.Y.C, Nee. (2019). Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison. Engineering., 5(4):653-661. doi: 10.1016/j.eng.2019.01.014.
Thum, G.W., Tang, S.H., Ahmad, S.A., and Alrifaey, M. (2020) Toward a Highly Accurate Classification of Underwater Cable Images via Deep Convolutional Neural Network. Journal of Marine Science and Engineering., 8. doi:10.3390/jmse8110924.
Todolí-Ferrandis, D., J, Silvestre-Blanes., S, Santonja-Climent., V, Sempere-Paya., and J, Vera-Pérez. (2017). Deploy&Forget wireless sensor networks for itinerant applications. Comput. Stand. Interfaces., 56:27-40. doi: 10.1016/j.csi.2017.09.002.
Torky, M., and A.E, Hassanein. (2020). Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges. Comput. Electron. Agric., 178:105,476. doi: 10.1016/j.compag.2020.105476.
Tran, M. Q., Elsisi, M., Mahmoud, K., Liu, M. K., Lehtonen, M., and Darwish, M.M.F. (2021b). Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment. IEEE Access., 9:115,429-115,441. doi:10.1109/ACCESS.2021.3105297
Tran, M.-Q., Elsisi, M., and Liu, M.-K. (2021a). Effective feature selection with fuzzy entropy and similarity classifier for chatter vibration diagnosis. Measurement., 184:109,962. doi: https://doi.org/10.1016/j.measurement.2021.109962
Villa-Henriksen, Andrés., Gareth, T.C. Edwards., Liisa, A. Pesonen., Ole, Green., and Claus, Aage Grøn Sørensen. (2020). Internet of Things in Arable Farming: Implementation, Applications, Challenges and Potential. Biosystems Engineering., 191:60-84. https://doi.org/10.1016/j.biosystemseng.12.013.
Yang, C.H., K.C, Lee., and S.E, Li. (2020). A mixed activity-based costing and resource constraint optimal decision model for IoT-oriented intelligent building management system portfolios. Sustain. Cities Soc., 60:102,142. doi: 10.1016/j.scs.2020.102142.
Zarreh, A., H. Da Wan, Y. Lee, C. Saygin, and R. Al Janahi. (2019). Cybersecurity concerns for total productive maintenance in smart manufacturing systems. Procedia Manuf., 38:532-539. doi: 10.1016/j.promfg.2020.01.067.
Zunino, C., A, Valenzano., R, Obermaisser., and S, Petersen. (2020). Factory Communications at the Dawn of the Fourth Industrial Revolution. Comput. Stand. Interfaces., 71:103,433. doi: 10.1016/j.csi.2020.103433.
