Rolling element bearing defects are among the main reasons for the breakdown of electrical machines, and therefore, early diagnosis of these is necessary to avoid more catastrophic failure consequences. This paper presents a novel approach for identifying rolling element bearing defects in brushless DC motors under non-stationary operating conditions. Stator current and lateral vibration measur...

Rolling element bearings are widely used in industry, especially where high load capacity is required. The diagnosis of their conditions is essential matter for downtime reduction and saving cost of maintenance. Therefore, an intensive analysis of frequency spectrum of their faults must be carried out in order to determine the main reason of the fault. This paper focus on a beating phenomena ob...

This paper mainly deals with the issue of incipient fault diagnosis for rolling element bearing. Firstly, an envelope demodulation technique based on wavelet packet transform and energy operator is applied to extract the fault feature of vibration signal. Secondly, the relative spectral entropy of envelope spectrum and the gravity frequency are combined to construct two-dimensional features vec...

This paper reviews the design and validation of a novel bearing test rig developed at the KU Leuven. The test rig allows multi-axial static and dynamic loading of a rolling element bearing. Different types and sizes of bearings can be mounted and loaded as if built into a real machine. The test rig will be used to analyse the effect of external dynamic loads on the lifetime of these bearings. T...

Maintenance and design engineers have great concern for the functioning of rotating machineries due to the vibration phenomenon. Improper functioning in rotating machinery originates from the damage to rolling element bearings. The status of rolling element bearings require advanced technologies to monitor their health status efficiently and effectively. Avoiding vibration during machine runnin...

A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditio...

The rolling element bearing is a key part in many mechanical facilities and the diagnosis of its faults is very important in the field of predictive maintenance. Till date, the resonant demodulation technique (envelope analysis) has been widely exploited in practice. In complex machines, the vibration generated by a component is easily affected by the vibration of other components or is corrupt...

The following paper will describe a methodology for automatically detecting and diagnosing rolling element bearing wear. The techniques involved have been proven in over 15 years of use in a huge variety of environments, machine types and applications. The techniques involved in diagnosing bearing wear will be described in detail and supported by a set of example graphs and an annotated diagnos...