Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems
Abstract
:1. Introduction
1.1. Microclimate Control as a Global Problem
1.2. Importance of Microclimate Control for People
1.3. Importance of Fault Detection Methods
1.4. Paper Structure
2. Types of Microclimate Control Systems
2.1. White and Black Box Models
2.2. Microclimate Control in Greenhouses
2.3. Microclimate Control in Hospitals
2.4. Microclimate Studies in Monuments and Art Galleries
2.5. Automatic Microclimate Control Methods
- Qceil—Heat loss through the ceiling
- Qd—Heat loss through door;
- QEo—Heat loss through exhaust openings;
- Qh—Human heat dissipation;
- Qwin—Heat loss through windows;
- Qw—Heat loss through walls;
- tR—Radiation temperature;
- +t—Room temperature;
- −t—Outside temperature;
- +fi—Room humidity;
- −fi—Outside humidity;
- −v—Outside air velocity;
- +v—Room air velocity;
- Qfl—Heat loss through the floor;
- 1—Heating system;
- g—Building structure thickness;
2.6. Summary
3. Fault Detection
3.1. Methods of Fault Detection
- Initial fault detection;
- Detection of multi-faults;
- Uncertainty control;
- Compatibility;
- Requirements for real-time computing;
- Justification and output response
3.2. Quantitative Methods
3.3. Rule Based Methods
- Economy and relative accuracy of results;
- The output is stable and non-random, as it depends on the rules;
- The coverage is fair under different scenarios and circumstances as the accuracy of the results is high. The error rate of the results is low due to the predefined rules, in the case if the rules are well defined;
- Optimizing system speed is easy because all parts of the system are well-known;
- Too much data and deep knowledge of the subject area, as well as too much manual work;
- Difficulty in writing and maintaining rules, time-consuming;
- Minimal capacity for self-learning;
- Difficulty in identifying complex patterns since the coding rules requires a lot of time and analysis.
3.4. Process History Based Model
4. Fault Prediction
4.1. Prediction Methods
4.2. Quantitative Methods
4.3. Machine Learning Approach
4.4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Farmani, F.; Parvizimosaed, M.; Monsef, H.; Rahimi-Kian, A. A conceptual model of a smart energy management system for a residential building equipped with CCHP system. Electr. Power Energy Syst. 2021, 2018, 523–536. [Google Scholar] [CrossRef]
- Hyvärinen, J.; Kärki, S. IEA Annex 25. Real Time Simulation of HVAC Systems for Building Optimization, Fault Detection and Diagnosis. Building Optimization and Fault Diagnosis Source Book; Technical Report; VTT Building Technology: Espoo, Finland, 1996. [Google Scholar]
- Nacer, A.; Marhic, B.; Delahoche, L.; Masson, J.B. ALOS: Automatic learning of an occupancy schedule based on a new prediction model for a smart heating management system. Build. Environ. 2018, 142, 484–501. [Google Scholar] [CrossRef]
- Nurlanuly, A.; Daurenbayeva, N. The study of human behavior in the house and its role in the overall life of the building in the field of energy consumption. World Sci. Eng. Sci. 2019, 1, 24–28. [Google Scholar] [CrossRef]
- Zhitov, V.G. Investigation and Provision of Microclimate Parameters of Residential and Public Buildings by Methods of Optimal Experiment Planning. Ph.D. Thesis, Irkutsk State Technical University, Irkutsk, Russia, 2007. [Google Scholar]
- Miljković, D. Fault detection methods: A literature survey. In Proceedings of the 2011 Proceedings of the 34th international convention MIPRO, Opatija, Croatia, 23–27 May 2011; IEEE: Piscataway, NJ, USA, 2011; pp. 750–755. [Google Scholar]
- Mateus, B.C.; Mendes, M.; Farinha, J.T.; Assis, R.; Cardoso, A.M. Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press. Energies 2021, 14, 6958. [Google Scholar] [CrossRef]
- Denizopoulou, Z.A.; Andreopoulou, A.M. Monitoring pollution level and microclimate conditions in a naturally ventilated livestock building using open-source device. J. Environ. Prot. Ecol. 2019, 20, 562–570. [Google Scholar]
- Zhao, Q.; Lian, Z.; Lai, D. Thermal comfort models and their developments: A review. Energy Built Environ. 2021, 2, 21–33. [Google Scholar] [CrossRef]
- Ashrae, A. Standard 55-Thermal Environmental Conditions for Human Occupancy. 2017. Available online: https://www.ashrae.org/technical-resources/bookstore/standard-55-thermal-environmental-conditions-for-human-occupancy (accessed on 10 December 2022).
- Tartarini, F.; Schiavon, S.; Cheung, T.; Hoyt, T. CBE Thermal Comfort Tool: Online tool for thermal comfort calculations and visualizations. SoftwareX 2020, 12, 100563. [Google Scholar] [CrossRef]
- Choab, N.; Allouhi, A.; El Maakoul, A.; Kousksou, T.; Saadeddine, S.; Jamil, A. Review on greenhouse microclimate and application: Design parameters, thermal modeling and simulation, climate controlling technologies. Sol. Energy 2019, 191, 109–137. [Google Scholar] [CrossRef]
- Mukazhanov, Y.; Kamshat, Z.; Orazbayeva, A.; Shayhmetov, N.; Alimbaev, C. Microclimate Control in Greenhouses. In Proceedings of the 17th International Multidisciplinary Scientific GeoConference SGEM 2017, Vienna, Austria, 27–29 November 2017; pp. 699–704. [Google Scholar] [CrossRef]
- Ganzhur, M.; Ganzhur, A.; Kobylko, A.; Fathi, D. Automation of microclimate in greenhouses. E3S Web Conf. 2020, 210, 05004. [Google Scholar] [CrossRef]
- Cannistraro, G.; Bernardo, E. Monitoring of the indoor microclimate in hospital environments a case study the Papardo hospital in Messina. Int. J. Heat Technol. 2017, 35, S456–S465. [Google Scholar] [CrossRef]
- Fabbri, K.; Gaspari, J.; Vandi, L. Indoor Thermal Comfort of Pregnant Women in Hospital: A Case Study Evidence. Sustainability 2019, 11, 6664. [Google Scholar] [CrossRef]
- Ferrante, M.; Oliveri Conti, G.; Blandini, G.L.; Cacia, G.; Distefano, C.; Distefano, G.; Mantione, V.; Ursino, A.; Milletari, G.; Coniglio, M.A.; et al. Microclimatic and Environmental Surveillance of Operating Theaters: Trend and Future Perspectives. Atmosphere 2021, 12, 1273. [Google Scholar] [CrossRef]
- Hoxha, A.; Dervishi, M.G.; Bici, M.E. Evaluation of microclimate in regional hospital in Berat. IOSR J. Dent. Med. Sci. 2014, 13, 96–101. [Google Scholar] [CrossRef]
- Czarniecki, W.; Kopacz, M.; Okołowicz, W.; Gajewski, J.; Grzedziński, E. Investigations of the microclimate in hospital wards. Energy Build. 1991, 16, 727–733. [Google Scholar] [CrossRef]
- García-Diego, F.J.; Zarzo, M. Microclimate monitoring by multivariate statistical control: The renaissance frescoes of the Cathedral of Valencia (Spain). J. Cult. Herit. 2010, 11, 3. [Google Scholar] [CrossRef]
- Camuffo, D.; Bernardi, A.; Sturaro, G.; Valentino, A. The microclimate inside the Pollaiolo and Botticelli rooms in the Uffizi Gallery, Florence. J. Cult. Herit. 2002, 3, 155–161. [Google Scholar] [CrossRef]
- Kostarev, S.N.; Sereda, T.G. Microclimate Control System Development. IOP Conf. Ser. 2018, 450, 62013. [Google Scholar] [CrossRef]
- Radojevic, N.; Kostadinovic, D.; Vlajkovic, H.; Veg, E. Microclimate Control in Greenhouses. FME Trans. 2014, 42, 699–703. [Google Scholar] [CrossRef]
- Rezvani, S.M.E.D.; Shamshiri, R.R.; Hameed, I.A.; Abyane, H.Z.; Godarzi, M.; Momeni, D.; Balasundram, S.K. Greenhouse Crop Simulation Models and Microclimate Control Systems, A Review. In Next-Generation Greenhouses for Food Security; IntechOpen: London, UK, 2021. [Google Scholar] [CrossRef]
- Deiana, G.; Arghittu, A.; Dettori, M.; Masia, M.D.; Deriu, M.G.; Piana, A.; Muroni, M.R.; Castiglia, P.; Azara, A. Environmental Surveillance of Legionella spp. in an Italian University Hospital Results of 10 Years of Analysis. Water 2021, 13, 2304. [Google Scholar] [CrossRef]
- Warriach, E.; Tei, K.; Nguyen, T.A.; Aiello, M. Poster abstract: Fault detection in wireless sensor networks: A hybrid approach. In Proceedings of the 11th international conference on Information Processing in Sensor Networks, Beijing, China, 16–20 April 2012; pp. 87–88. [Google Scholar] [CrossRef]
- Panda, R.R.; Gouda, B.S.; Panigrahi, T. Efficient fault node detection algorithm for wireless sensor networks. In Proceedings of the 2014 International Conference on High Performance Computing and Applications (ICHPCA), Bhubaneswar, India, 22–24 December 2014; pp. 1–5. [Google Scholar] [CrossRef]
- Park, Y.J.; Fan, S.K.; Hsu, C.Y. A Review on Fault Detection and Process Diagnostics in Industrial Processes. Processes 2020, 8, 1123. [Google Scholar] [CrossRef]
- Lau, M.; Liu, Y.; Yu, Y. On Detection Conditions of Double FaultsRelated to Terms in Boolean Expressions. Comput. Softw. Appl. Conf. Annu. Int. 2006, 1, 403–410. [Google Scholar] [CrossRef]
- Chen, L.; Li, S.; Wang, X. Quickest Fault Detection in Photovoltaic Systems. IEEE Trans. Smart Grid 2018, 9, 1835–1847. [Google Scholar] [CrossRef]
- Ning, Y.; Xu, X.L.; Jiang, Z.; Ning, B.Y. Research on Fault Detection and Diagnosis for Small Unmanned Aerial Vehicle. In Proceedings of the International Conference on Environmental Science and Sustainable Energy, Suzhou, China, 23–25 June 2017. [Google Scholar] [CrossRef]
- Panda, M.; Khilar, P.M. Distributed Byzantine fault detection technique in wireless sensor networks based on hypothesis testing. Comput. Electr. Eng. 2015, 48, 270–285. [Google Scholar] [CrossRef]
- Yu, T.; Akhtar, A.M.; Wang, X.; Shami, A. Temporal and spatial correlation based distributed fault detection in wireless sensor networks. In Proceedings of the 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE), Halifax, NS, Canada, 3–6 May 2015; pp. 1351–1355. [Google Scholar] [CrossRef]
- Lazarova-Molnar, S.; Shaker, H.R.; Mohamed, N.; Jorgensen, B.N. Fault detection and diagnosis for smart buildings: State of the art, trends and challenges. In Proceedings of the 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC); IEEE: Piscataway, NJ, USA, 2016; pp. 1–7. [Google Scholar] [CrossRef]
- Li, Y. A Fault Prediction and Cause Identification Approach in Complex Industrial Processes Based on Deep Learning. Comput. Intell. Neurosci. 2021, 2021, 6612342. [Google Scholar] [CrossRef] [PubMed]
- Dey, M.; Rana, S.P.; Dudley, S. A case study based approach for remote fault detection using multi-level machine learning in a smart building. Smart Cities 2020, 3, 401–419. [Google Scholar] [CrossRef]
- Xiangjun, Z.; Yuanyuan, W.; Yao, X. Faults Detection for Power Systems. In Fault Detection; Zhang, W., Ed.; IntechOpen: Rijeka, Croatia, 2010; Chapter 4. [Google Scholar] [CrossRef]
- Yan, K.; Ma, L.; Dai, Y.; Shen, W.; Ji, Z.; Xie, D. Cost-sensitive and sequential feature selection for chiller fault detection and diagnosis. Int. J. Refrig. 2018, 86, 401–409. [Google Scholar] [CrossRef]
- Rafati, A.; Shaker, H.R.; Ghahghahzadeh, S. Fault Detection and Efficiency Assessment for HVAC Systems Using Non-Intrusive Load Monitoring: A Review. Energies 2022, 15, 341. [Google Scholar] [CrossRef]
- Bang, M.; Engelsgaard, S.; Alexandersen, E.; Skydt, M.; Shaker, H.R.; Jradi, M. Novel Real-Time Model-Based Fault Detection Method for Automatic Identification of Abnormal Energy Performance in Building Ventilation Units. Energy Build. 2018, 183, 238–251. [Google Scholar] [CrossRef]
- Rodrigues, J.A.; Farinha, J.T.; Mendes, M.; Mateus, R.J.; Cardoso, A.J.M. Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition. Energies 2022, 15, 6308. [Google Scholar] [CrossRef]
- Pandey, S.K.; Mishra, R.B.; Tripathi, A.K. Machine learning based methods for software fault prediction: A survey. Expert Syst. Appl. 2021, 172, 114595. [Google Scholar]
- Mateus, B.; Mendes, M.; Farinha, J.T.; Martins, A.B.; Cardoso, A.M. Data Analysis for Predictive Maintenance Using Time Series and Deep Learning Models—A Case Study in a Pulp Paper Industry. In Proceedings of IncoME-VI and TEPEN 2021; Springer: Berlin/Heidelberg, Germany, 2023; pp. 11–25. [Google Scholar]
- Makridakis, S.; Hibon, M. ARMA models and the Box–Jenkins methodology. J. Forecast. 1997, 16, 147–163. [Google Scholar] [CrossRef]
- Martins, A.; Fonseca, I.; Farinha, J.T.; Reis, J.; Cardoso, A.J.M. Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study. Appl. Sci. 2021, 11, 7685. [Google Scholar] [CrossRef]
Author/Year | Variables Monitored | Method/Approach | Main Contribution |
---|---|---|---|
Nemanja Radojević et al., 2014 [23] | heating, ventilation, fogging, lighting and shading, fertigation, CO2 injection | practical approach to the real-time control system in a greenhouse | An effective mechatronic system for large greenhouses. The monitoring concept is based on a centralized main block and distributed local blocks and censorship in neighboring assets. |
Moin Rezvani et al., 2020 [24] | temperature, humidity, light, concentration of carbon dioxide | Crop growth model, Functional-structural plant model | Greenhouse process (KASPRO) model is constructed from modules describing the physics of mass and energy transport in the greenhouse enclosure and the modules that simulate the greenhouse climate controllers. |
Giovanna Deiana et al., 2021 [25] | air temperature, relative humidity, mean radiant temperature, airspeed | Evaluation of thermal comfort using Fanger indices, standards required by current legislation and specific guidelines | Non-compliant values for at least one parameter were found in 98.8% of the examinations. A condition of thermal discomfort was calculated for 3.6% of healthcare professionals and 98.3% of patients. |
Adrian Hoxha et al., 2014 [18] | temperature, relative humidity, lighting, and air movements | Monitor air temperature, humidity, lighting (lux), air speed | Method to control internal temperature and relative humidity compared to the norms |
Mauro Cannistraro et al., 2017 [15] | air temperature, absolute humidity or moisture, a minimum flow of outdoor air | Use the model suggested by the ISO 7730 standard | Full control of thermohygrometric parameters (temperature, humidity, and airspeed), as well as the ability to control and regulate large areas of flow and air exchange in individual places. |
Reference | Classification | Approach | Detection Approach | Topology Dependent | Threshold Based |
---|---|---|---|---|---|
Panda et al., 2014 [27] | Centralized | Statistic | Active | Yes | No |
Warriach et al., 2012 [26] | Centralized | ML | Active | No | No |
Park et al., 2020 [28] | Centralized | Statistic | Passive | No | No |
Lau et al., 2006 [29] | Centralized | ML | Active | No | Yes |
Chen et al., 2018 [30] | Distributed | Hybrid | Active | No | Yes |
Ning et al., 2017 [31] | Distributed | WV | Active | Yes | Yes |
Panda et al., 2015 [32] | Distributed | Self detection | Active | Yes | Yes |
Yu et al., 2015 [33] | Distributed | Statistic | Active | Yes | Yes |
Lazarova-Molnar et al., 2017 [34] | Centralized | ML | Active | No | No |
Methods | Advantages | Disadvantages |
---|---|---|
Regression models | Speed of obtaining results; Availability of intermediate calculations; Simplicity of models; The heterogeneity of the tasks being solved. | Complexity of determining parameters; Possibility of modeling only linear processes; Complexity of determining the type of functional dependence. |
Autoregressive models | Speed of obtaining results; Availability of intermediate calculations; Simplicity of models; Heterogeneity of the tasks being solved. | Complexity of determining parameters; Possibility of modeling only linear processes. |
Exponential smoothing models | Simplicity of models; Speed of getting results; | Lack of flexibility. |
Neural network models | Solving long-term prediction problems; Possibility of modeling nonlinear processes; Adaptability; Scalability; Heterogeneity of the tasks being solved. | Complexity of software implementation; Lack of intermediate calculations; High requirements for consistency of the training sample. |
Models based on Markov chains | Simplicity of models; | Narrow applicability of models. Impossibility of solving prediction problems with a long memory. |
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Daurenbayeva, N.; Nurlanuly, A.; Atymtayeva, L.; Mendes, M. Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems. Energies 2023, 16, 3508. https://doi.org/10.3390/en16083508
Daurenbayeva N, Nurlanuly A, Atymtayeva L, Mendes M. Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems. Energies. 2023; 16(8):3508. https://doi.org/10.3390/en16083508
Chicago/Turabian StyleDaurenbayeva, Nurkamilya, Almas Nurlanuly, Lyazzat Atymtayeva, and Mateus Mendes. 2023. "Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems" Energies 16, no. 8: 3508. https://doi.org/10.3390/en16083508