Bearing time-to-failure estimation using spectral analysis features. (March 2014)

• Record Type: Journal Article
• Authors: Reuben, Lim Chi Keong; Mba, David
• Is Part Of: Structural health monitoring. Volume 13:Number 2(2014)
• Page Start: 219
• Page End: 230
• Publication Date: 2014-03
• Subjects: Rolling element bearing -- vibration monitoring -- time to failure -- condition-based maintenance -- maintenance planning; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/1475921713520029
• Languages: English
• ISSNs: 1475-9217

Abstract

With the increasing use of health usage monitoring systems on helicopters, a lot of research has been undertaken for diagnosis of transmission components. However, most of these works are performed in laboratory environments and there are hardly any published works on in-service application. In this study, we present an experience in diagnosis of a helicopter gearbox bearing using actual service data gathered from AH64D helicopters belonging to the Republic of Singapore Air Force. A number of helicopters have been found with grease leak and radial play in the tail rotor gearbox output shaft during field maintenance. Subsequent tear-down inspections of the tail rotor gearboxes revealed that they had similar defects of bearing race spalling and widespread pitting of the rolling elements. Spectral analysis was carried out on the accelerometer data from these helicopters and correlated with the tear-down inspection findings. The fault patterns exhibited correspond well to progressing stages of bearing wear and are consistent across defective gearboxes from different helicopters. It is demonstrated that simple spectral analysis can be effective in tracking progressive stages of bearing damage using both low-frequency and high-frequency bandwidths. The observed fault patterns are extracted as features for diagnosis and used to determine the bearings' estimated time to failure for maintenance planning.