Partial cloud-based evolving method for fault detection of HVAC system

In this paper we present an initial investigation of the evolving cloud-based method using partial density calculation for fault detection of HVAC system (Heating, Ventilation and Air Conditioning). Moreover, we investigate different approaches of choosing the most influential components when calculating the partial local density which is further used for evolving the model structure. The method is based on the simplified fuzzy rule- based-system AnYa where the antecedent part is presented by data clouds. The effectiveness of the proposed method is tested on a model of HVAC system and furthermore, different types of faults are investigated. The results were compared with the well established fault detection method DPCA (Dynamic Principal Component Analysis).

[1]  Tiago J. Rato,et al.  Defining the structure of DPCA models and its impact on process monitoring and prediction activities , 2013 .

[2]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[3]  Alessandro Beghi,et al.  A data-driven approach for fault diagnosis in HVAC chiller systems , 2015, 2015 IEEE Conference on Control Applications (CCA).

[4]  Bo Fan,et al.  A hybrid FDD strategy for local system of AHU based on artificial neural network and wavelet analysis , 2010 .

[5]  Richard D. Braatz,et al.  Perspectives on process monitoring of industrial systems , 2016, Annu. Rev. Control..

[6]  Igor Škrjanc,et al.  Comparison of approaches for identification of all-data cloud-based evolving systems , 2015 .

[7]  Paul Raftery,et al.  Comparative analysis of the AHU InFO fault detection and diagnostic expert tool for AHUs with APAR , 2015 .

[8]  Plamen P. Angelov,et al.  Simplified fuzzy rule-based systems using non-parametric antecedents and relative data density , 2011, 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS).

[9]  Marios M. Polycarpou,et al.  A Distributed Architecture for HVAC Sensor Fault Detection and Isolation , 2015, IEEE Transactions on Control Systems Technology.

[10]  Žiga Stržinar.  Modeliranje in zaznavanje napak v klimatskih sistemih , 2017 .

[11]  Guang-Hong Yang,et al.  Observer-Based Output Feedback Control for Discrete-Time T-S Fuzzy Systems With Partly Immeasurable Premise Variables , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[12]  Igor Skrjanc,et al.  Evolving model identification for process monitoring and prediction of non-linear systems , 2018, Eng. Appl. Artif. Intell..

[13]  Ahmed Rubaai,et al.  Fuzzy Neural Network-Based Health Monitoring for HVAC System Variable-Air-Volume Unit , 2016, IEEE Transactions on Industry Applications.

[14]  Mia Hubert,et al.  Overview of PCA-Based Statistical Process-Monitoring Methods for Time-Dependent, High-Dimensional Data , 2015 .

[15]  P. Wang,et al.  On-line fault detection and diagnosis for chiller system , 2016, 2016 IEEE International Conference on Automation Science and Engineering (CASE).

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

[17]  Igor Skrjanc,et al.  A practical implementation of Robust Evolving Cloud-based Controller with normalized data space for heat-exchanger plant , 2016, Appl. Soft Comput..

[18]  Xinhua Xu,et al.  Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods , 2008 .

[19]  Plamen Angelov,et al.  Real-Time Fault Detection Using Recursive Density Estimation , 2014, Journal of Control, Automation and Electrical Systems.

[20]  Fu Xiao,et al.  AHU sensor fault diagnosis using principal component analysis method , 2004 .

[21]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[22]  Guang-Hong Yang,et al.  A New Sensor Fault Isolation Method for T–S Fuzzy Systems , 2017, IEEE Transactions on Cybernetics.

[23]  Weihua Li,et al.  Recursive PCA for Adaptive Process Monitoring , 1999 .

[24]  Plamen P. Angelov,et al.  Fully unsupervised fault detection and identification based on recursive density estimation and self-evolving cloud-based classifier , 2015, Neurocomputing.

[25]  Ping Zhang,et al.  A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .

[26]  Krishna R. Pattipati,et al.  Data-Driven Modeling, Fault Diagnosis and Optimal Sensor Selection for HVAC Chillers , 2007, IEEE Transactions on Automation Science and Engineering.