Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis

Abstract

A new method is presented for predicting rolling element bearing condition from measurements of bearing housing vibration. This method is based on a statistical parameter Kurtosis, that remains constant for an undamaged bearing irrespective of load and speed, yet changes with damage. The extent of damage can be assessed from the distribution of this statistical parameter in selected frequency ranges. An assessment of bearing condition can thus be made with minimum recourse to historical information. Most other damage detection techniques rely heavily on the trend analysis of data and so this new method may prove to be a significant advance in bearing fault detection technology, at least when viewed within the original objective to provide a simple and cheap technique. As with most other simple detection techniques, the precise nature of the fault cannot be defined and for such information it is necessary to use the more sophisticated diagnostic methods.

A Study on Damage Evaluation of Bearings for Rotating Machinery in Power Plant Using Ultrasonic Wave

“…) 그러나 대부분 의 연구들이 구름베어링에 대한 것들이 많은 편 이며 (Dyer, 1978;Mcfadden, 1984) 볼 베어링에 대 한 진단기술은 상대적으로 저조하며 (Sato, 1983 참고문헌…”

Fault Diagnosis of Shaft- Ball Bearing System Using One-Way Analysis of Variance

“…Statistical movements are descriptors of the shape of the amplitude distribution of vibration data collected from a bearing, and have some advantages over traditional time and frequency analysis [16]. Dyer and Stewart [17] first proposed the use of the fourth normalized central statistical moment kurtosis for bearing defect detection. White [9] studied the effectiveness of this method under a simulated condition.…”

Intelligent Vibration Signal Diagnostic System Using Artificial Neural Network

“…Furthermore, each trending index, which may be treated as a different aspect of the signal, carries different sensitivities for different machine fault types. In the case of vibration, several trending monitoring techniques have been developed and studied (Mathew, 1989;Dyer & Stewart, 1978;Mathew & Alfredson, 1984;Spoerre, 1993). In this instance, EWMA (Exponential Weighted Moving Average), RMS (Root Mean Square).…”