Positive-Unlabelled Learning based Novelty Detection for Industrial Chillers
A Data-Driven Approach to Avoid Energy Wastage
Keywords:Chiller CBM , Machine Learning, Energy Efficiency
Chiller systems are used in many different applications in both the industrial and the commercial sector. They are considered major energy consumers and thus contribute a non-negligible factor to environmental pollution as well as to the overall operating cost. In addition, chillers, especially in industrial applications, are often associated with high reliability requirements, as unplanned system downtimes are usually costly. As many studies over the past decades have shown, the presence of faults can lead to significant performance degradation and thus higher energy consumption of these systems. Thus, data-driven fault detection plays an ever-increasing role in terms of energy efficient control strategies. However, labelled data to train associated algorithms are often only available to a limited extent, which consequently inhibits the broad application of such technologies. Therefore, this paper presents an approach that exploits only a small amount of labelled and large amounts of unlabelled data in the training phase in order to detect fault related anomalies. For this, the model utilizes the residual space of the data transformed through principal component analyses in conjunction with a biased support vector machine, which can be ascribed to the concept of semi-supervised learning, or more specifically, positive-unlabelled learning.
A. Afshari and L. Friedrich, “A proposal to introduce tradable energy savings certificates in the emirate of abu dhabi,” Renewable and Sustainable Energy Reviews, vol. 55, pp. 1342–1351, 2016.
ISO, Condition monitoring and diagnostics of machines–vocabulary, 2012.
G. J. Vachtsevanos, Intelligent fault diagnosis and prognosis for engineering systems. Wiley Hoboken, 2006, vol. 456.
S. Wang, Q. Zhou, and F. Xiao, “A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults,” Energy and Buildings, vol. 42, no. 4, pp. 477–490, 2010.
D.-W. Sun, Handbook of frozen food processing and packaging. CRC Press, 2016.  V. Venkatasubramanian, R. Rengaswamy, and S. N. Kavuri, “A review of process fault detection and diagnosis: Part ii: Qualitative models and search strategies,” Computers & chemical engineering, vol. 27, no. 3, pp. 313–326, 2003.
V. Venkatasubramanian, R. Rengaswamy, S. N. Kavuri, and K. Yin, “A review of process fault detection and diagnosis: Part iii: Process history based methods,” Computers & chemical engineering, vol. 27, no. 3, pp. 327–346, 2003.
V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri, “A review of process fault detection and diagnosis: Part i: Quantitative model-based methods,” Computers & chemical engineering, vol. 27, no. 3, pp. 293–311, 2003.
Y. Fan, X. Cui, H. Han, and H. Lu, “Chiller fault diagnosis with field sensors using the technology of imbalanced data,” Applied Thermal Engineering, vol. 159, p. 113 933, 2019.
C. Anger, “Hidden semi-markov models for predictive maintenance of rotating elements,”Ph.D. dissertation, Technische Universit ¨ at, 2018.
A. Beghi, R. Brignoli, L. Cecchinato, G. Menegazzo, M. Rampazzo, and F. Simmini, “Data-driven fault detection and diagnosis for HVAC water chillers,” Control Engineering Practice, vol. 53, pp. 79–91, 2016.
B. Liu, Y. Dai, X. Li, W. S. Lee, and P. S. Yu, “Building text classifiers using positive and unlabeled examples,” in Third IEEE International Conference on Data Mining, 2003, pp. 179–186.
H. Han, Z. Cao, B. Gu, and N. Ren, “PCA-SVM-based automated fault detection and diagnosis (AFDD) for vapor-compression refrigeration systems,” HVAC&R Research, vol. 16, no. 3, pp. 295–313, 2010.
A. Beghi, L. Cecchinato, C. Corazzol, M. Rampazzo, F. Simmini, and G. A. Susto, “A One-Class SVM based tool for machine learning novelty detection in HVAC chiller systems,” FAC Proceedings Volumes, vol. 47, no. 3, pp. 1953–1958, 2014.
G. Li, Y. Hu, H. Chen, L. Shen, H. Li, M. Hu, J. Liu, and K. Sun, “An improved fault detection method for incipient centrifugal chiller faults using the PCA-R-SVDD algorithm,” Energy and Buildings, vol. 116, pp. 104–113, 2016.
B. Sch¨ olkopf, A. J. Smola, F. Bach, et al., Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, 2002.  B. Sch¨ olkopf, R. C. Williamson, A. Smola, J. Shawe-Taylor, and J. Platt, “Support vector method for novelty detection,” Advances in neural information processing systems, vol. 12, pp. 582–588, 1999.
D. M. J. Tax and R. P. W. Duin, “Support vector data description,” Machine learning, vol. 54, no. 1, pp. 45–66, 2004.
H. Han, B. Gu, J. Kang, and Z. R. Li, “Study on a hybrid SVM model for chiller FDD applications,” Applied Thermal Engineering, vol. 31, no. 4, pp. 582–592, 2011.
K. Yan, Z. Ji, and W. Shen, “Online fault detection methods for chillers combining extended kalman filter and recursive One-Class SVM,” Neurocomputing, vol. 228, pp. 205–212, 2017.
D. Li, G. Hu, and C. J. Spanos, “A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis,” Energy and Buildings, vol. 128, pp. 519–529, 2016.
Z. Wang, Z. Wang, S. He, X. Gu, and Z. F. Yan, “Fault detection and diagnosis of chillers using bayesian network merged distance rejection and multi-source non-sensor information,” Applied Energy, vol. 188, pp. 200–214, 2017.
S. Wang and Y. Chen, “Sensor validation and reconstruction for building central chilling systems based on principal component analysis,” Energy Conversion and anagement, vol. 45, no. 5, pp. 673–695, 2004.
H. Han, X. Cui, Y. Fan, and H. Qing, “Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features,” Applied Thermal Engineering, vol. 154, pp. 540–547, 2019.
Y. Zhao, S. Wang, and F. Xiao, “Pattern recognition-based chillers fault detection method using support vector data description (SVDD),” Applied Energy, vol. 112, pp. 1041–1048, 2013.
W. S. Lee and B. Liu, “Learning with positive and unlabeled examples using weighted logistic regression,” in ICML, vol. 3, 2003, pp. 448–455.
M. C. Comstock, J. E. Braun, and R. Bernhard, Experimental data from fault detection and diagnostic studies on a centrifugal chiller. Purdue University, 1999.
R. van de Sand, C. Falk, S. Corasaniti, and J. Reiff-Stephan, “A data-driven fault diagnosis approach towards oil retention in vapour compression refrigeration systems,” in 2019 International IEEE Conference and Workshop in O´ buda on Electrical and Power Engineering (CANDO-EPE), 2019, pp. 197–202.
Copyright (c) 2021 Ron van de Sand, Sandra Corasaniti, Jörg Reiff-Stephan
This work is licensed under a Creative Commons Attribution 4.0 International License.