Detection and Isolation of Unbalanced and Bearing Faults in Rotary Machinery Using Artificial Intelligence

In this paper, the type and location of unbalanced and bearing faults in rotary machines are investigated. The data used in this work is provided by the rotary machine fault simulator. These data include 14 different states, including 13 modes with unbalanced and bearings faults at different locations and a healthy mode, recorded by six acceleration sensors. The empirical mode decomposition (EMD) method is used for signal decomposition and feature extraction. Nine different features of the signals are extracted and the PCA method is used to reduce the dimensions of the data. Also, in the classification section, the performance of three types of classifiers RBF, MLP and FCM are evaluated. The results show that the best performance pertains to the RBF method using two Kurtosis and RMS features with the number of intrinsic mode functions (IMF) equals 9 and dimensions reduced to 25. In this case, accuracy in the test phase was obtained at 96.42%.