Abstract

The two-stage condition monitoring approach takes account of the fact that, due to the advent of improved lubricant formulations and more efficient filter designs, conventional laboratory oil testing has recently become less useful for extracting timely condition monitoring information. The new approach uses the latest technological advances in imaging hardware combined with ASTM D7684-11 compliant wear debris particle analysis software containing an extensively researched knowledge base that has captured the diagnostic skills and experience of a number of expert wear debris analysts, each with decades of hands-on experience. It offers a cost effective, on-site diagnostic capability to rival that of most specialist labs. First stage testing uses the latest computer vision technology to visualise fine debris. This innovative digital imaging hardware enables on-site maintenance professionals not only to reliably size and count but also to analyse wear debris particles as small as 5 microns, offering timely equipment health information that few laboratories can match. The second stage consists of on-site, in-service filter analysis triggered by the appearance of abnormalities in the fine debris particles during first stage analyses. The innovative, diagnostic wear debris particle analysis software then uses the five level severity rating advocated by the ASTM D7684-11 standard guide such that timely alerts do not allow wear to escalate to a critical level. The paper includes the results of a forensic case study illustrating the way in which a catastrophic bearing failure, costing millions of euros in critical equipment down-time could easily have been avoided had the two-stage condition monitoring methodology been applied. This new approach has the potential to avoid costly, unscheduled, equipment down-time due to the unpredicted failure of critical equipment or equally costly false alarms when equipment is unnecessarily removed from service. This is accomplished by extracting information concerning equipment health from fine wear debris at an early stage in the wear process, where such information has previously only been available by the analysis of large wear debris particles at a much later stage in the equipment wear cycle.

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