Using wavelet transform analysis and the support vector machine to detect angular misalignment of a rubber coupling

Shaft misalignment is a common problem for many types of rotating systems. It can cause machine breakdowns due to the premature failure of bearings or other components. Common diagnostic approaches rely on detecting increasing vibration response spectra at multiples of the shaft speed. However, in many time-variant systems, such as wind turbines, the speed and load vary considerably, which can make spectrum analysis insufficient. In this paper, a method for detecting shaft misalignment by using wavelet analysis is proposed. The method was experimentally evaluated in a laboratory test rig for four different operating conditions by varying the rotational speed and load. An angular misalignment was introduced between a hydraulic pump (load) and a medium-sized industrial gearbox connected with a rubber coupling. Vibration data were collected by using two accelerometers mounted in an axial and a radial direction directly on the gearbox casing. The features extracted from wavelet representation were classified by using a support vector machine algorithm. The detection of misalignment and the sensitivity of the proposed method are presented using validation data and confusion matrices. The final results of the confusion matrices clearly indicate that this method can detect misalignment even when the speed and load vary. The proposed method can be used for systems which are connected with shafts and there are many similar systems (comprising an electric motor, a gearbox and a centrifugal pump) working under the same circumstances.