Fault Detection and Diagnosis in HVAC System Based on Soft Computing Approach
A.Parvaresh1, A. Hasanzade2, S. M. A. Mohammadi3, A. Gharaveisi4
1Ahmad Parvaresh, Department of electrical engineering, Shahid Bahonar university of Kerman, Kerman, Iran,
2Ali Hasanzade, Department of electrical engineering, Shahid Bahonar university of Kerman, Kerman, Iran
3S. M. Mohammadi, Department of electrical engineering, Shahid Bahonar university of Kerman, Kerman, Iran,
4A. Gharaveisi, Department of electrical engineering, Shahid Bahonar university of Kerman, Kerman, Iran,
Manuscript received on July 01, 2012. | Revised Manuscript received on July 04, 2012. | Manuscript published on July 05, 2012. | PP: 114-120 | Volume-2, Issue-3, July 2012. | Retrieval Number: C0712052312 /2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The fault detection and diagnosis (FDD) play an important role in the monitoring, repairing and maintaining of technical systems. In this paper, we presented a new method based on soft computing approach for FDD in a special type of HVAC system namely unitary system. In the proposed method, the feature vectors are extracted by applying wavelet transform to output signals of model. Then, a Takagi-Sugeno (T-S) fuzzy classifier detects and diagnoses the faults by use of extracted feature vectors, if the faults exist. The T-S fuzzy classifier needs to be trained. With inspiration from training formulation of support vector machine (SVM), the training process has been stated as an optimization problem. For solving the mentioned optimization problem, a reliable evolutionary algorithm namely differential evolution (DE) is used. One of the important types of faults in the unitary HVAC system is refrigerant leakage. FDD of refrigerant leakage is highlighted in the presented paper. The simulation has been done in MATLAB-Simulink and the efficacy of the proposed method is demonstrated based on the experimental results.
Keywords: Differential Evolution Algorithm, Fault Detection and Diagnosis, Takagi-Sugeno Fuzzy Classifier, Unitary HVAC System, Wavelet Transform