Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches

Fault Detection and Isolation (FDI) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDI framework is required to reduce the energy needs for buildings and improving indoor environment quality. The main goal of this paper is to merge the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers to improve the efficiency of the detection and isolation of Air Conditioning (AC) systems. First, the multivariate statistical features extraction and selection is achieved using the PCA method. Then, the multiscale representation is applied to separate feature from noise and approximately decorrelate autocorrelation between available measurements. Third, the extracted and selected features are introduced to several machine learning classifiers for fault classification purposes. The effectiveness and higher classification accuracy of the developed Multiscale PCA (MSPCA)-based ML technique is demonstrated using two examples: synthetic data and simulated data extracted from Air Conditioning systems.

[1]  Ludmila I. Kuncheva,et al.  PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Data , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[2]  L. Ren,et al.  Single-Sensor Incipient Fault Detection , 2011, IEEE Sensors Journal.

[3]  J. Ross Quinlan,et al.  Simplifying Decision Trees , 1987, Int. J. Man Mach. Stud..

[4]  Tapas K. Das,et al.  Wavelet-based multiscale statistical process monitoring: A literature review , 2004 .

[5]  Yidi Wang,et al.  A Training Data Set Cleaning Method by Classification Ability Ranking for the $k$ -Nearest Neighbor Classifier , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Hazem Nounou,et al.  Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems , 2020 .

[7]  Hazem Nounou,et al.  Machine learning-based statistical testing hypothesis for fault detection in photovoltaic systems , 2019, Solar Energy.

[8]  Gilles Mourot,et al.  An improved PCA scheme for sensor FDI: Application to an air quality monitoring network , 2006 .

[9]  Hazem Nounou,et al.  Multiscale Gaussian process regression-based generalized likelihood ratio test for fault detection in water distribution networks , 2019, Eng. Appl. Artif. Intell..

[10]  Hamid Reza Shaker,et al.  Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings , 2019, Energies.

[11]  Nader Meskin,et al.  Sensor data validation and fault diagnosis using Auto-Associative Neural Network for HVAC systems , 2020 .

[12]  Liangxiao Jiang,et al.  Class-specific attribute weighted naive Bayes , 2019, Pattern Recognit..

[13]  Weihua Li,et al.  Detection, identification, and reconstruction of faulty sensors with maximized sensitivity , 1999 .

[14]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[15]  Peng Zhang,et al.  Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA , 2019, Applied Sciences.

[16]  Dongping Du,et al.  Fault detection and diagnosis using empirical mode decomposition based principal component analysis , 2018, Comput. Chem. Eng..

[17]  S. Qin,et al.  Determining the number of principal components for best reconstruction , 2000 .

[18]  Stéphane Ploix,et al.  Advancing Building Fault Diagnosis Using the Concept of Contextual and Heterogeneous Test , 2019, Energies.

[19]  Houda Najeh,et al.  Diagnosis of sensor grids in a building context: Application to an office setting , 2018 .

[20]  Hazem Nounou,et al.  Online reduced gaussian process regression based generalized likelihood ratio test for fault detection , 2020 .

[21]  B. Bakshi.  Multiscale PCA with application to multivariate statistical process monitoring , 1998 .

[22]  Thomas F. Edgar,et al.  Identification of faulty sensors using principal component analysis , 1996 .

[23]  Brian Surgenor,et al.  An unsupervised artificial neural network versus a rule-based approach for fault detection and identification in an automated assembly machine , 2017 .

[24]  I. Jolliffe.  A Note on the Use of Principal Components in Regression , 1982 .

[25]  T. Agami Reddy,et al.  Evaluation of the Suitability of Different Chiller Performance Models for On-Line Training Applied to Automated Fault Detection and Diagnosis (RP-1139) , 2003 .

[26]  Mercedes Ibarra,et al.  Development of a failure detection tool using machine learning techniques for a large aperture concentrating collector at an industrial application in Chile , 2019 .