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Due to growth of mechanization and automation, today’s industrial systems are becoming more complex. A small breakdown of any non-redundant machine component affects the operation of the entire system. Compressors are utilized widely in the oil and chemical industry. Great attention has been paid to the condition monitoring and fault diagnosis of the Compressor by the field engineers and technicians. In this study, an effective and reliable method based on vibration analysis and with signal processing and classification techniques is presented for troubleshooting of a centrifugal Compressor. Among different time – frequency methods, wavelet transformation extracts information about the signal time scale through a series of convolution operation between the measured signals and the basis wavelet which was used as a preprocessing. The used mother wavelet is (db4) in which the original signal is switched to multiple details signals; then it features are taken from pre-processed signals and they were introduced to support vector machines as input. Kernel function used here in the support vector machine is RBF in which the parameters of support vector machine were optimized using Genetic Algorithm for better performance to increase the accuracy of classification. The highest accuracy was obtained as 100 %. The obtained accuracy clearly indicates high safety margin of the multistage centrifugal pump for fault detection.

There is a strong need to use different methods for maintaining high product quality and safety production line, given the rapid technological progress. Thus, the condition monitoring is widely used as an efficient method in various industries. A variety of methods have been used so far in order to implement condition monitoring system, the most common one has been seismic waves analysis in the time-frequency domain. The current paper proposes a Fuzzy Inference System for monitoring the status of the compressor based on Daubechies wavelet transform and decision trees. The J48 algorithm was used as a tool for classification design and selection of effective features on troubleshooting. The J48 algorithm output results showed that signal processing technique by Daubechies wavelet mother has the highest accuracy for the implementation of the FIS system. The J48 algorithm output is a decision tree used on production of if-then rules and fuzzy set membership functions of the system. Finally, the combination of WT-J48-FIS overall accuracy for classifying compressor defects of 93.33 % was obtained.

Due to the quick advancement of technology, application of different methods is highly required to maintain the high quality of production and health assessment of production lines. Hence, condition monitoring is widely used in the industry as an efficient approach. The purpose of the present study was to classify faults in centrifugal pumps using the vibration signal analysis and support vector machine (SVM) method. Vibration signals were decomposed in three levels by Daubechies wavelets, and a total of 44 descriptive statistical features were extracted from detail coefficients and approximation coefficients of the wavelets. In order to find the best model for fault classification of centrifugal pumps, parameters such as penalty, degree of polynomial, and width of the Gaussian radial basis function kernel (RBF kernel) were investigated. The classification results using the SVM method indicated that the maximum classification accuracy was 96.67 percent, which was obtained at an RBF kernel width of 0.1 and a penalty parameter value of 1.