2. Research Background
2.1. Maintenance Approches
- Corrective maintenance: also known as reactive maintenance or run to failure maintenance, which consists of intervening after the failure. The equipment is allowed to operate until it fails.
- Preventive maintenance: this consists of carrying out inspection and maintenance actions while the equipment is still running to reduce the probability of breakdowns. Preventive maintenance can be either time-based via a schedule or usage-based (e.g., every 100 km). This approach helps to reduce the number of failures, but unnecessary inspections are performed and unplanned failures still occur, which increases the cost of maintenance.
- Predictive maintenance: this approach is based on using condition monitoring data to predict the future machine health state . This approach aims to predict when, where, and which components may have potential failures.
2.2. Deep Learning Overview
2.2.2. Long Short-Term Memory (LSTM)
3. Research Objective and Methodology
3.1. Aim of the Current Study
3.2. Quantitative Research
4. Framework Design
4.1. Defining Data Sources
- Building automation systems (BAS): BAS are largely used in modern buildings to control and monitor the different installations via real-time data . BAS contain both numerical and categorical data. Typical examples of numerical data are measurements such as temperature, energy consumption, and air and water flow rate, etc., whereas categorical data consist of time, alerts, and the binary state (ON/OFF), etc. 
- IoT devices and sensors: connected sensors and IoT devices have been introduced in buildings in recent years in order to collect information on the building and its surroundings [47,48,49]. These devices can be used to collect multiple types of information; they can be deployed on the installations (air handling unit (AHU), lift, chiller, etc.) to extract data such as temperature and vibration . They can be used to collect human behaviour data such as occupation or mobility [48,51]. They can also be used to collect indoor and outdoor environmental measurements such as CO2 levels .
- Computerised maintenance management systems (CMMS): CMMS are used to manage daily maintenance activities. Functionalities of CMMS include: receiving emergency work orders and users’ requests, scheduling preventive maintenance activities, recording the history of maintenance activities, and inventory control, etc. [53,54]. Thus, CMMS represents an important data source for predictive maintenance.
- Building information modelling (BIM): the BIM model provides architectural 3D visualisation and standardisation of building information exchange between the stockholders along the construction project life cycle . In recent years, several studies have been carried out to implement the BIM in the operation and maintenance phase [56,57,58]. BIM can be used to support FM teams while operating maintenance activities , to monitor energy efficiency in buildings  and to provide visual analytics for maintenance activities .
- Other data sources: building energy management system (BEMS), computer aided facility management (CAFM) and integrated workplace management system (IWMS) are examples of other data sources that can be found in the building environment. However, their use is limited to some specific facilities.
4.2. The Framework Architecture
4.3. Data Collection, Storage and Processing
- Data collection: the first step of the framework aims to collect data from the available sources in the building environment. It involves defining the data sources in the building then connecting them to extract the necessary data. The data sources were defined in this work in Section 4.1. However, data collection methods are not specified in this framework, since they depend on the user preference and the available ICT infrastructure.
- Data storage: this consists of storing the data after collecting them in a storage medium. There are different storage methods (cloud, local, etc.) which depend on the preference and the infrastructure of the user. in this framework, data storage is not discussed.
- Data pre-processing: the purpose of this step is to transform the raw data into a structured dataset ready for the training process. This step is comprised of two main parts:
- Data cleaning: this consists of cleaning the data entries by removing irrelevant entries, replacing Nan values, and treating outliers.
4.4. Model Development
4.4.1. The Autoencoder Model
4.4.2. Anomaly Score
4.5. Fault Notification
4.6. Feedback and Continuous Improvement
4.7. Model Implementation
5. Case Study: Predictive Maintenance for HVAC Installations in Sport Facility Buildings
5.1. The Facility Characteristics
5.2. Data and Model Characteristics
- The building automation system (BAS) was connected to a web server. Each installation is monitored via the BAS through one or multiple variables such as temperature, energy consumption, water consumption, and air or water flow rate, etc. A report from the BAS is uploaded every hour. The report contains the date and the time, the name of the variable and its value at that time.
- An extract from the CMMS that contains a part of the maintenance record.
- Vibration device: an IoT device was installed on the surface of the equipment; it is used to collect the vibration measurements on the installation. The data reported by the device are the acceleration measures on the three axis (x, y and z-axis), the frequency of vibration of the three axis, a binary variable (ON/OFF, which detects if the machine is enabled) and the temperature in the surface of the equipment. An example is presented in Figure 5 that shows a vibration device attached to a double pump.
- Electric meter device: an IoT meter installed to collect the electric energy data which includes the following measures: electric current intensity and voltage as well as the temperature on the surface of the equipment.
5.3. Results and Analysis
6. Discussion and Conclusions
- Data between diversity and scarcity: data in the building environment are diverse in terms of sources and in terms of nature. They are generated from the human activity indoors, from the diverse installations in the building (mechanical, electrical, electronic etc.), and from the building itself. However, the majority of data are not collected and not stored. Moreover, unlike other industries, there is a lack of open databases containing building data, except some databases mainly focusing on building energy consumption . As a result, building predictive maintenance has become a hard and a costly task.
- Return on investment: predictive maintenance strategy offers the facility manager the possibility to take early action to prevent failures, which improves the lifespan of the installations and improves the comfort of the inhabitants. However, the implementation of predictive maintenance may take a significant time to build an effective model. This presents an important barrier for the facility managers to invest in solutions that can take a significant time before it starts getting profitable.
- Each building is unique: unlike other industries, such as the manufacturing industry, where multiple installations are the same, each building has a different use, different architecture, and different occupancy. Thus, two same AHUs are not the same anymore once they are installed in different buildings. This unicity of buildings presents multiple opportunities and a wide market for predictive maintenance; moreover, it reveals several challenges to developing effective and affordable solutions.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
- Hong, T.; Koo, C.; Kim, J.; Lee, M.; Jeong, K. A review on sustainable construction management strategies for monitoring, diagnosing, and retrofitting the building’s dynamic energy performance: Focused on the operation and maintenance phase. Appl. Energy 2015, 155, 671–707. [Google Scholar] [CrossRef]
- Seyedzadeh, S.; Pour Rahimian, F.; Glesk, I.; Roper, M. Machine learning for estimation of building energy consumption and performance: A review. Vis. Eng. 2018, 6, 5. [Google Scholar] [CrossRef]
- Li, Y.; O’Neill, Z. A critical review of fault modeling of HVAC systems in buildings. Build. Simul. 2018, 11, 953–975. [Google Scholar] [CrossRef]
- Matarneh, S.T.; Danso-Amoako, M.; Al-Bizri, S.; Gaterell, M.; Matarneh, R.T. BIM for FM: Developing information requirements to support facilities management systems. Facilities 2019, 38, 378–394. [Google Scholar] [CrossRef]
- Zhan, J.; Ge, X.J.; Huang, S.; Zhao, L.; Wong, J.K.W.; He, S.X. Improvement of the inspection-repair process with building information modelling and image classification. Facilties 2019, 37, 395–414. [Google Scholar] [CrossRef]
- Lee, H.H.Y.; Scott, D. Overview of maintenance strategy, acceptable maintenance standard and resources from a building maintenance operation perspective. J. Build. Apprais. 2009, 4, 269–278. [Google Scholar] [CrossRef]
- Peter, W.T. Maintenance practices in Hong Kong and the use of the intelligent scheduler. J. Qual. Maint. Eng. 2002, 8, 369–380. [Google Scholar]
- Pitt, T.J. Data requirements for the prioritization of predictive building maintenance. Facilties 1997, 15, 97–104. [Google Scholar] [CrossRef]
- Gunay, B.; Shen, W.; Yang, C. Text-mining building maintenance work orders for component fault frequency. Build. Res. Inf. 2018, 47, 518–533. [Google Scholar] [CrossRef]
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436. [Google Scholar] [CrossRef]
- Boyes, H.; Hallaq, B.; Cunningham, J.; Watson, T. The industrial internet of things (IIoT): An analysis framework. Comput. Ind. 2018, 101, 1–12. [Google Scholar] [CrossRef]
- Mourtzis, D.; Vlachou, E.; Milas, N. Industrial Big Data as a Result of IoT Adoption in Manufacturing. Procedia CIRP 2016, 55, 290–295. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- BSI Standards Publication. Maintenance Terminology; BS EN 13306:2017; BSI Standards Publication: London, UK, 2017; ISBN 978-0-580-90370-0. [Google Scholar]
- Schmidt, B.; Wang, L. Cloud-enhanced predictive maintenance. Int. J. Adv. Manuf. Technol. 2016, 99, 5–13. [Google Scholar] [CrossRef]
- ACachada, A.; Barbosa, J.; Leitño, P.; Gcraldcs, C.A.S.; Deusdado, L.; Costa, J.; Teixeira, C.; Teixeira, J.; Moreira, A.H.J.; Moreira, P.M.; et al. Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture. In Proceedings of the 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), Turin, Italy, 4–7 September 2018; pp. 139–146. [Google Scholar] [CrossRef]
- Gholami, P.; Hafezalkotob, A. Maintenance scheduling using data mining techniques and time series models. Int. J. Manag. Sci. Eng. Manag. 2017, 13, 100–107. [Google Scholar] [CrossRef]
- Kwak, R.-Y.; Takakusagi, A.; Sohn, J.-Y.; Fujii, S.; Park, B.-Y. Development of an optimal preventive maintenance model based on the reliability assessment for air-conditioning facilities in office buildings. Build. Environ. 2004, 39, 1141–1156. [Google Scholar] [CrossRef]
- Halim, T.; Tang, L.-C. A graphical approach for confidence limits of optimal preventive maintenance cycles. Qual. Reliab. Eng. Int. 2009, 25, 199–213. [Google Scholar] [CrossRef]
- Sandeepan, C.; Dubey, K. Mechanical Vibration Analysis of HVAC system and Its Optimization Techniques. Adv. Res. Electr. Electron. Eng. 2015, 2, 77–82. [Google Scholar]
- Ning, M.; Zaheeruddin, M.; Chen, Z. Fuzzy-Set Based HVAC System Uncertainty Analysis. In Proceedings of the NAFIPS 2006—2006 Annual Meeting of the North American Fuzzy Information Processing Society, Montréal, QC, Canada, 3–6 June 2006; pp. 229–234. [Google Scholar]
- Wang, L.; Hong, T. Modeling and Simulation of HVAC Faulty Operation and Performance Degradation due to Maintenance Issues. In Proceedings of the ASIM 2012—1st Asia conference of International Building Performance Simulation Association, Hong Kong, China, 25–27 November 2014. [Google Scholar]
- Mattera, C.; Quevedo, J.; Escobet, T.; Shaker, H.R.; Jradi, M. A Method for Fault Detection and Diagnostics in Ventilation Units Using Virtual Sensors. Sensors 2018, 18, 3931. [Google Scholar] [CrossRef]
- Saxena, L.M.A., Jr.; Knapp, G.M. Statistical-based or condition-based preventive maintenance? J. Qual. Maint. Eng. 1995, 1, 46–59. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA, 3–6 December 2012; pp. 1097–1105. [Google Scholar]
- Otter, D.W.; Medina, J.R.; Kalita, J.K. A Survey of the Usages of Deep Learning for Natural Language Processing. IEEE Trans. Neural Netw. Learn. Syst. 2020, 1–21. [Google Scholar] [CrossRef] [PubMed]
- Nguyen, P.; Tran, T.; Wickramasinghe, N.; Venkatesh, S. Deepr: A Convolutional Net for Medical Records. IEEE J. Biomed. Health Inform. 2017, 21, 22–30. [Google Scholar] [CrossRef] [PubMed]
- Silver, D.; Huang, A.; Maddison, C.J.; Guez, A.; Sifre, L.; Driessche, G.V.D.; Schrittwieser, J.; Antonoglou, I.; Panneershelvam, V.; Lanctot, M.; et al. Mastering the game of Go with deep neural networks and tree search. Nature 2016, 529, 484–489. [Google Scholar] [CrossRef] [PubMed]
- Chalapathy, R.; Chawla, S. Deep learning for anomaly detection: A survey. arXiv 2019, arXiv:1901.03407v2. [Google Scholar]
- Tehrani, M.M.; Beauregard, Y.; Rioux, M.; Kenne, J.-P.; Ouellet, R. A Predictive Preference Model for Maintenance of a Heating Ventilating and Air Conditioning System. IFAC Pap. 2015, 48, 130–135. [Google Scholar] [CrossRef]
- Mahamad, A.K.; Saon, S.; Hiyama, T. Predicting remaining useful life of rotating machinery based artificial neural network. Comput. Math. Appl. 2010, 60, 1078–1087. [Google Scholar] [CrossRef]
- Pham, M.T.; Kim, J.-M.; Kim, C.H. Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems. Sensors 2020, 20, 6886. [Google Scholar] [CrossRef]
- Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning; MIT Press: Cambridge, UK, 2016.
- Gasparin, A.; Lukovic, S.; Alippi, C. Deep Learning for Time Series Forecasting: The Electric Load Case. arXiv 2019, arXiv:1907.09207. [Google Scholar]
- Hadsell, R.; Sermanet, P.; Ben, J.; Erkan, A.; Scoffier, M.; Kavukcuoglu, K.; Muller, U.; LeCun, Y. Learning long-range vision for autonomous off-road driving. J. Field Robot. 2009, 26, 120–144. [Google Scholar] [CrossRef]
- Fan, C.; Xiao, F.; Zhao, Y.; Wang, J. Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data. Appl. Energy 2018, 211, 1123–1135. [Google Scholar] [CrossRef]
- Morán, A.; Alonso, S.; Prada, M.A.; Fuertes, J.J.; Díaz, I.; Domínguez, M. Analysis of Parallel Process in HVAC Systems Using Deep Autoencoders. Progr. Ing. Nat. 2017, 744, 15–26. [Google Scholar] [CrossRef]
- Li, C.; Ding, Z.; Zhao, D.; Yi, J.; Zhang, G. Building Energy Consumption Prediction: An Extreme Deep Learning Approach. Energies 2017, 10, 1525. [Google Scholar] [CrossRef]
- Baldi, P. Autoencoders, Unsupervised Learning, and Deep Architectures. JMLR Workshop Conf. Proc. 2012, 27, 37–49. [Google Scholar]
- Wang, Y.; Yao, H.; Zhao, S. Auto-encoder based dimensionality reduction. Neurocomputing 2016, 184, 232–242. [Google Scholar] [CrossRef]
- Murphree, J. Machine learning anomaly detection in large systems. IEEE Autotestcon 2016, 1–9. [Google Scholar] [CrossRef]
- Araya, D.B.; Grolinger, K.; El Yamany, H.F.; Capretz, M.A.M.; Bitsuamlak, G. An ensemble learning framework for anomaly detection in building energy consumption. Energy Build. 2017, 144, 191–206. [Google Scholar] [CrossRef]
- Bouabdallaoui, Y.; Lafhaj, Z.; Yim, P.; Ducoulombier, L.; Bennadji, B. Natural Language Processing Model for Managing Maintenance Requests in Buildings. Buildings 2020, 10, 160. [Google Scholar] [CrossRef]
- Su, Y.; Zhao, Y.; Niu, C.; Liu, R.; Sun, W.; Pei, D. Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 4–8 August 2019; Volume 1485, pp. 2828–2837. [Google Scholar]
- Fan, C.; Xiao, F.; Yan, C. A framework for knowledge discovery in massive building automation data and its application in building diagnostics. Autom. Constr. 2015, 50, 81–90. [Google Scholar] [CrossRef]
- Moreno, M.V.; Dufour, L.; Skarmeta, A.F.; Jara, A.J.; Genoud, D.; Ladevie, B.; Bézian, J.-J.; Cano, M.V.M. Big data: The key to energy efficiency in smart buildings. Soft Comput. 2015, 20, 1749–1762. [Google Scholar] [CrossRef]
- Minoli, D.; Sohraby, K.; Occhiogrosso, B. IoT Considerations, Requirements, and Architectures for Smart Buildings—Energy Optimization and Next-Generation Building Management Systems. IEEE Internet Things J. 2017, 4, 269–283. [Google Scholar] [CrossRef]
- Schmidt, M.; Åhlund, C. Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency. Renew. Sustain. Energy Rev. 2018, 90, 742–756. [Google Scholar] [CrossRef]
- Carli, R.; Cavone, G.; Ben Othman, S.; Dotoli, M. IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings. Sensors 2020, 20, 781. [Google Scholar] [CrossRef]
- Akkaya, K.; Guvenc, I.; Aygun, R.; Pala, N.; Kadri, A. IoT-based occupancy monitoring techniques for energy-efficient smart buildings. In Proceedings of the 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), New Orleans, LA, USA, 9–12 March 2015; pp. 58–63. [Google Scholar] [CrossRef]
- Bourdeau, M.; Zhai, X.; Nefzaoui, E.; Guo, X.; Chatellier, P. Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustain. Cities Soc. 2019, 48. [Google Scholar] [CrossRef]
- Gunay, B.; Shen, W.; Newsham, G. Data analytics to improve building performance: A critical review. Autom. Constr. 2019, 97, 96–109. [Google Scholar] [CrossRef]
- Marmo, R.; Nicolella, M.; Polverino, F.; Tibaut, A.; Marmo, R. A Methodology for a Performance Information Model to Support Facility Management. Sustainability 2019, 11, 7007. [Google Scholar] [CrossRef]
- Zhang, J.; Seet, B.-C.; Lie, T.T. Building Information Modelling for Smart Built Environments. Buildings 2015, 5, 100–115. [Google Scholar] [CrossRef]
- Lu, Q.; Chen, L.; Lee, S.; Zhao, X. Activity theory-based analysis of BIM implementation in building O&M and first response. Autom. Constr. 2018, 85, 317–332. [Google Scholar] [CrossRef]
- Gerrish, T.; Ruikar, K.; Cook, M.J.; Johnson, M.; Phillip, M.; Lowry, C. BIM application to building energy performance visualisation and management: Challenges and potential. Energy Build. 2017, 144, 218–228. [Google Scholar] [CrossRef]
- Dong, B.; O’Neill, Z.; Li, Z. A BIM-enabled information infrastructure for building energy Fault Detection and Diagnostics. Autom. Constr. 2014, 44, 197–211. [Google Scholar] [CrossRef]
- Chen, W.; Chen, K.; Cheng, J.C.; Wang, Q.; Gan, V.J. BIM-based framework for automatic scheduling of facility maintenance work orders. Autom. Constr. 2018, 91, 15–30. [Google Scholar] [CrossRef]
- Motamedi, A.; Hammad, A.; Asen, Y. Knowledge-assisted BIM-based visual analytics for failure root cause detection in facilities management. Autom. Constr. 2014, 43, 73–83. [Google Scholar] [CrossRef]
- Nargesian, F.; Samulowitz, H.; Khurana, U.; Khalil, E.B.; Turaga, D. Learning Feature Engineering for Classification. In Proceedings of the Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia, 19–25 August 2017; pp. 2529–2535.
- Zheng, A.; Casari, A. Feature Engineering for Machine Learning; O’Reilly Media, Inc.: Sebastopol, CA, USA, 2018. [Google Scholar]
- Sagheer, A.; Kotb, M. Unsupervised Pre-training of a Deep LSTM-based Stacked Autoencoder for Multivariate Time Series Forecasting Problems. Sci. Rep. 2019, 9, 1–16. [Google Scholar] [CrossRef] [PubMed]
- Zhuang, F.; Qi, Z.; Duan, K.; Xi, D.; Zhu, Y.; Zhu, H.; Xiong, H.; He, Q. A Comprehensive Survey on Transfer Learning. Proc. IEEE 2021, 109, 43–76. [Google Scholar] [CrossRef]
- Mekki, K.; Bajic, E.; Chaxel, F.; Meyer, F. A comparative study of LPWAN technologies for large-scale IoT deployment. ICT Express 2019, 5, 1–7. [Google Scholar] [CrossRef]
- Miller, C.; Arjunan, P.; Kathirgamanathan, A.; Fu, C.; Roth, J.; Park, J.Y.; Balbach, C.; Gowri, K.; Nagy, Z.; Fontanini, A.D.; et al. The ASHRAE Great Energy Predictor III competition: Overview and results. Sci. Technol. Built Environ. 2020, 26, 1427–1447. [Google Scholar] [CrossRef]
|Quantity||Installation||Attached IoT Devices|
Electric meter device
|3||Double pump||Vibration device|
|Boiler 2||14 April 2020||Confirmed Failure|
|Boiler 2||15 April 2020||Confirmed Failure|
|AHU 2||25 April 020||Not Confirmed|
|Boiler 2||12 May 2020||Not Confirmed|
|AHU 1||Not detected||Failure not detected|
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).