Outlier detection via multiclass deep autoencoding Gaussian mixture model for building chiller diagnosis
Tài liệu tham khảo
Mirnaghi, 2020, Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review, Energy Build., 110492, 10.1016/j.enbuild.2020.110492
Zhao, 2019, Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future, Renew. Sustain. Energy Rev., 109, 85, 10.1016/j.rser.2019.04.021
Li, 2021, A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data, Appl. Energy, 285, 10.1016/j.apenergy.2021.116459
Yan, 2020, Generative adversarial network for fault detection diagnosis of chillers, Build. Environ., 172, 10.1016/j.buildenv.2020.106698
Comstock, 2002, A survey of common faults for chillers/Discussion, Ashrae Transactions, 108, 819
Zhu, 2021, Transfer learning based methodology for migration and application of fault detection and diagnosis between building chillers for improving energy efficiency, Build. Environ., 10.1016/j.buildenv.2021.107957
Zhao, 2013, An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network, Energy Build., 57, 278, 10.1016/j.enbuild.2012.11.007
Luo, 2020, Novel pattern recognition-enhanced sensor fault detection and diagnosis for chiller plant, Energy Build., 228, 10.1016/j.enbuild.2020.110443
Wang, 2021, Fault diagnosis using fused reference model and Bayesian network for building energy systems, J. Build. Eng., 34
Liu, 2020, Data-driven and association rule mining-based fault diagnosis and action mechanism analysis for building chillers, Energy Build., 216, 10.1016/j.enbuild.2020.109957
Xiao, 2011, A fault detection and diagnosis strategy with enhanced sensitivity for centrifugal chillers, Appl. Therm. Eng., 31, 3963, 10.1016/j.applthermaleng.2011.07.047
Zhao, 2012, A virtual condenser fouling sensor for chillers, Energy Build., 52, 68, 10.1016/j.enbuild.2012.05.018
Li, 2014, A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform, Energy Build., 68, 63, 10.1016/j.enbuild.2013.08.044
Wang, 2018, Enhanced chiller fault detection using Bayesian network and principal component analysis, Appl. Therm. Eng., 141, 898, 10.1016/j.applthermaleng.2018.06.037
Li, 2019, An enhanced PCA-based chiller sensor fault detection method using ensemble empirical mode decomposition based denoising, Energy Build., 183, 311, 10.1016/j.enbuild.2018.10.013
Li, 2018, Improved sensor fault detection, diagnosis and estimation for screw chillers using density-based clustering and principal component analysis, Energy Build., 173, 502, 10.1016/j.enbuild.2018.05.025
Wang, 2017, Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information, Appl. Energy, 188, 200, 10.1016/j.apenergy.2016.11.130
Wang, 2018, Feature selection based on Bayesian network for chiller fault diagnosis from the perspective of field applications, Appl. Therm. Eng., 129, 674, 10.1016/j.applthermaleng.2017.10.079
Zhang, 2019, An improved association rule mining-based method for revealing operational problems of building heating, ventilation and air conditioning (HVAC) systems, Appl. Energy, 253, 10.1016/j.apenergy.2019.113492
Han, 2019, Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features, Appl. Therm. Eng., 154, 540, 10.1016/j.applthermaleng.2019.03.111
Sun, 2016, A novel efficient SVM-based fault diagnosis method for multi-split air conditioning system’s refrigerant charge fault amount, Appl. Therm. Eng., 108, 989, 10.1016/j.applthermaleng.2016.07.109
Li, 2016, Fault detection and diagnosis for building cooling system with a tree-structured learning method, Energy Build., 127, 540, 10.1016/j.enbuild.2016.06.017
Han, 2020, Ensemble learning with member optimization for fault diagnosis of a building energy system, Energy Build., 226, 10.1016/j.enbuild.2020.110351
Du, 2014, Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis, Build. Environ., 73, 1, 10.1016/j.buildenv.2013.11.021
Yan, 2020, Chiller Fault Diagnosis Based on VAE-Enabled Generative Adversarial Networks, IEEE Trans. Autom. Sci. Eng.
Wang, 2020, A novel fault diagnosis approach for chillers based on 1-D convolutional neural network and gated recurrent unit, Sensors, 20, 2458, 10.3390/s20092458
Gao, 2021, Fault diagnosis for building chillers based on data self-production and deep convolutional neural network, Journal of Building Engineering, 34, 10.1016/j.jobe.2020.102043
Jin, 2019, Detecting and diagnosing incipient building faults using uncertainty information from deep neural networks, 1
Han, 2021, Novel chiller fault diagnosis using deep neural network (DNN) with simulated annealing (SA), Int. J. Refrig, 121, 269, 10.1016/j.ijrefrig.2020.10.023
E. Klevak, S. Lin, A. Martin, O. Linda, and E. Ringger, “Out-Of-Bag Anomaly Detection,” arXiv preprint arXiv:2009.09358, 2020.
Pang, 2018, Learning representations of ultrahigh-dimensional data for random distance-based outlier detection, 2041
Bhaduri, 2011, Algorithms for speeding up distance-based outlier detection, 859
Hautamaki, 2004, Outlier detection using k-nearest neighbour graph, 430
Zhang, 2009, A new local distance-based outlier detection approach for scattered real-world data, 813
Sadooghi, 2018, Improving one class support vector machine novelty detection scheme using nonlinear features, Pattern Recogn., 83, 14, 10.1016/j.patcog.2018.05.002
Liu, 2012, Isolation-based anomaly detection, ACM Trans. Knowledge Discov. Data (TKDD), 6, 1, 10.1145/2133360.2133363
Serneels, 2008, Principal component analysis for data containing outliers and missing elements, Comput. Stat. Data Anal., 52, 1712, 10.1016/j.csda.2007.05.024
Lee, 2012, Anomaly detection via online oversampling principal component analysis, IEEE Trans. Knowl. Data Eng., 25, 1460, 10.1109/TKDE.2012.99
Chen, 2017, Outlier detection with autoencoder ensembles, 90
Kieu, 2019, Outlier detection for time series with recurrent autoencoder ensembles, IJCAI, 2725
Yamanishi, 2004, On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms, Data Min. Knowl. Disc., 8, 275, 10.1023/B:DAMI.0000023676.72185.7c
M. Goldstein A. Dengel “Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm,” KI-2012: Poster and Demo Track 2012 59 63
Candès, 2011, Robust principal component analysis?, J. ACM (JACM), 58, 1, 10.1145/1970392.1970395
Zong, 2018, Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Tra, 2017, Bearing fault diagnosis under variable speed using convolutional neural networks and the stochastic diagonal levenberg-marquardt algorithm, Sensors, 17, 2834, 10.3390/s17122834
Tra, 2021, Health indicators construction and remaining useful life estimation for concrete structures using deep neural networks, Appl. Sci., 11, 4113, 10.3390/app11094113
Tra, 2019, Improving diagnostic performance of a power transformer using an adaptive over-sampling method for imbalanced data, IEEE Trans. Dielectr. Electr. Insul., 26, 1325, 10.1109/TDEI.2019.008034
Fan, 2019, Chiller fault diagnosis with field sensors using the technology of imbalanced data, Appl. Therm. Eng., 159, 10.1016/j.applthermaleng.2019.113933
Zhou, 2021, Data-driven fault diagnosis for residential variable refrigerant flow system on imbalanced data environments, Int. J. Refrig, 125, 34, 10.1016/j.ijrefrig.2021.01.009
Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput., 18, 1527, 10.1162/neco.2006.18.7.1527
Comstock, 2002