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Rolling element bearings find widespread domestic and industrial application. Defects in bearing unless detected in time may lead to malfunctioning of the machinery. Different methods are used for detection and diagnosis of the bearing defects. This paper is intended as a tutorial overview of bearing vibration signature analysis as a medium for fault detection. An explanation for the causes for the defects is discussed. Vibration measurement in both time domain and frequency domain is presented. Recent trends in research on the detection of the defects in bearings have been included.

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The presence of defect in the bearing (outer race, inner race, or ball) results in increased vibrations. Time domain indices such as rms, crest factor, and kurtosis are some of the important parameters used to monitor the condition of the bearing. Radial load and operating speed also have an important role in bearing vibrations. The interaction between the defect size, load, and speed helps to study their effect on vibrations more effectively. Response surface methodology (RSM) is a combination of statistical and mathematical techniques to represent the relationship between the inputs and the outputs of a physical system. But so far, the literature related to its application in bearing damage identification is scarce. The proposed study uses RSM to study the influence of defect size, load, and speed on the bearing vibrations. Kurtosis is used as response factor. Experiments are planned using Box Behnken design procedure. Experiments are performed using 6305 ball bearings and the results have been presented. MINITAB statistical software is used for analysis. It is seen from the analysis of the experimental results that the defect size, interaction effect of defect size and load, and interaction effect of defect size and speed are significant. Response surface method using Box Behnken design and analysis of variance has proved to be a successful technique to assess the significant factors related to bearing vibrations.

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