Fault Diagnosis of Monoblock Centrifugal Pump Using Stationary Wavelet Features and Bayes Algorithm
DOI:
https://doi.org/10.51983/ajsat-2014.3.2.797Keywords:
Stationary wavelets transform, fault diagnosis, wavelet feature, BayesnetAbstract
Fault diagnosis of monoblock centrifugal pump is conceived as a pattern recognition problem. There are three important steps to be performed in pattern recognition namely feature extraction, feature selection and classification. In this study, Stationary wavelet transform (SWT) is used for feature extraction from the input signals and Bayes net classifier is used for classification. A WEKA implementation of Bayes net algorithm is used. The different fault conditions considered for the present study are Cavitation (CAV), Impeller fault (FI), Bearing Fault (BF) and both Impeller and Bearing Fault (FBI). The representative signal is acquired for all faulty conditions,
Features are extracted, classified and the results are presented. The experimental setup and the procedure for conducting the experiments are discussed in detail.
References
V. Muralidharan and V. Sugumaran, "Selection of Discrete Wavelets for Fault Diagnosis of Monoblock Centrifugal Pump using the J48 Algorithm," Applied Artificial Intelligence, vol. 27, pp. 1-19, 2013.
V. Muralidharan, V. Sugumaran, and N. R. Sakthivel, "Wavelet decomposition and support vector machine for fault diagnosis of monoblock centrifugal pump," International Journal of Data Analysis and Strategies, vol. 3, pp. 159–177, 2011.
J. Wang and H. Hu, "Vibration based fault diagnosis of pump using fuzzy technique," Measurement, vol. 39, pp. 176-185, 2006.
F. Kong and R. Chen, "A combined method for triplex pump fault diagnosis based on wavelet transforms, fuzzy logic and neuro-networks," Mechanical System and Signal Processing, vol. 18, pp. 161-168, 2004.
V. Muralidharan, V. Sugumaran, and H. Kumar, "Fault Diagnosis of Monoblock Centrifugal Pump Using Discrete Wavelet Features and J48 Algorithm," International Journal of Mechanical Engineering and Technology, vol. 3, pp. 120–126, 2012.
V. Muralidharan and V. Sugumaran, "Feature Extraction using Wavelets and classification through Decision Tree Algorithm for Fault Diagnosis of Mono-Block Centrifugal Pump," Measurement, vol. 46, pp. 353–359, 2013.
D. Zogg, E. Shafai, and H. P. Geering, "Fault diagnosis of heat pumps with parameter identification and clustering," Control Engineering Practice, vol. 12, pp. 1435-1444, 2006.
V. Muralidharan and V. Sugumaran, "Rough Set Based Rule Learning and Fuzzy Classification of Wavelet Features for Fault Diagnosis of Monoblock Centrifugal Pump," Measurement, vol. 46, no. 9, pp. 3057-3063, Nov. 2013.
V. Muralidharan, S. Ravikumar, and H. Kanagasabapathy, "Condition monitoring of Self aligning carrying idler (SAI) in belt conveyor system using statistical features and decision tree algorithm," Measurement, vol. 58, pp. 274–279, Dec. 2014.
V. Muralidharan, V. Sugumaran, and M. Indira, "Fault Diagnosis of Monoblock Centrifugal pump using SVM," Engineering Science and Technology, an International Journal, vol. 17, pp. 152-157, Sep 2014.
V. Muralidharan, V. Sugumaran, and G. Pandey, "SVM Based Fault Diagnosis of Monoblock Centrifugal pump using Stationary Wavelet Features," International Journal of Design and Manufacturing Technology, vol. 2, no. 1, pp. 1-6, 2011.
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