Comparison between empirical mode decomposition and wavelet transform for unbalance detection on rotating machinery using optimized support vector machine

In this study, fair comparisons between the empirical mode decomposition, ensemble empirical mode decomposition and discrete wavelet transform with the mother wavelet function of Meyer and Daubechies, were performed for detecting unbalance faults in a rotating machinery. In order to classify the healthy class from the unbalance classes, a support vector machines that was optimized by particle swarm optimization algorithm, was used. A comparison between the performances of optimized and non-optimized of support vector machines were also carried out. In order to obtained the required data, a rotating machinery fault simulator was developed and vibrational signals were acquired at healthy and unbalance fault conditions by accelerometer sensors. By processing the recorded signals and analysing signal to their frequency components, several statistical features were extracted from each frequency component as input support vector machine for the separation of classes. The obtained results indicated that the discrete wavelet transform with the Meyer mother wavelet, higher success rate than other methods for diagnosing unbalance faults.