Hybrid feature selection method for SVM classification and its application for fault diagnosis of wear and peeling in journal bearing with a little muddy water using long-term real data

Abstract

Rotating machines are widely used as components of various industries around the world, and its normal operation of rotating machines is important. Thus, condition monitoring and fault diagnosis of rotating machines have considerable attention in recent years. Industrial statistics illustrate that 40% of total large machine breakdowns happened due to broken bearings, while for small machines, the analogous number reaches up to 90%. This study aimed at researching fault diagnosis of journal bearings using the support vector machine (SVM) method. The experimental systems of vertical and horizontal rotating shafts were developed. There was no adding any initial artificial failure in the bearing, and a little muddy water was used, and long-term vibration data in both systems were obtained in the normal operation of the machines until bearing damages occurred in the journal bearing (3-hour tests were conducted repeatedly and total 128 datasets for vertical shaft and 24 datasets for horizontal shaft were obtained). A feature selection method is focused, and a hybrid feature selection method by combining Fisher score (FS) and a sequential forward selection (SFS) method was proposed. Its accuracy and efficiency was proved experimentally with 97.14% and 100% for vertical shaft and 100% for horizontal shaft. Furthermore, as a result of the SVM model and hybrid feature selection method, the most important feature for journal bearing of horizontal rotor system was clarified as mean value of RMS, and only this feature can give good diagnosis result. It is useful suggestion in selecting the features for the fault diagnosis of horizontal rotating machines.