Bearing Signature Analysis as a Medium for Fault Detection: A Review

Abstract

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.

The application of spectral kurtosis on Acoustic Emission and vibrations from a defective bearing

“…For an undamaged bearing the Kurtosis is typically 3 while greater values are normally associated with loss of integrity. However, the main drawback of using this method is that the Kurtosis begins to revert back to the undamaged value as the defect further develops [1,2]. Other statistical features such as the KolmogorovSmirnov statistic has been applied by several investigators [3,4] in which they reported success in diagnosing a damaged bearing.…”

“…Other statistical features such as the KolmogorovSmirnov statistic has been applied by several investigators [3,4] in which they reported success in diagnosing a damaged bearing. Frequency domain analysis for machine fault diagnosis is well established and the authors refer the readers to a review by Patil et al [2].…”

Precursor monitoring approach for reliability assessment of cooling fans

“…The time domain approach includes the investigation of time wave form indices, probability density form, and probability density moments. The kurtosis calculated from probability density moments is one precursor parameter that is more sensitive to the early stage of bearing defects than the other time domain approaches due to its mathematical nature (Patil et al 2008). The frequency domain approach permits the detection of faults and the diagnosis of the health of a fan by comparing the vibration spectrum from the measurement to the characteristic defect frequencies.…”

Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

“…Commonly used technique for fault detection is vibration-based signature analysis. Signal processing in vibration-based monitoring of rotating machinery offers very important information about anomalies formed internally in the structure of the machinery [6]. Hundreds of papers in this field, including theory and practical applications, appear every year in academic journals, conference proceedings and technical reports.…”