Early Detection of Lubrication Anomalies in Oil-Lubricated Bearings

Condition monitoring of roller element bearings is of considerable interest to many industries since faulty roller element bearings are known to cause the majority of problems in rotating machinery. The class of failure modes in roller bearings that has received the most attention include spalling or localized defect in one or more of the bearing components. This refers to dislodgement of a portion of the contact surface in one or more of the bearing components in the presence of various kinds of stresses like rotor imbalance, speed, bad lubrication, heavy radial and axial loads. Whereas substantial work has been done in the detection of spalls in bearing components, studies show that one of the primary root causes of spalls in a bearing tends to be ineffective lubrication resulting either from lack of lubrication conditions or the presence of contaminants in the lubricant. Leading bearing companies have indicated that incorrect lubrication can account for more than 90% of bearing failures because of which lubrication can be a key influence that can make or break bearing service and life. This emphasizes the need to develop techniques to sense lubrication anomalies as well as provide information needed to act upon them. In other words, monitoring the health and effectiveness of the bearing lubricant should be at the forefront of a condition-monitoring program for bearings. In this paper, we describe experiments, analyses and results obtained for monitoring and detecting anomalies in bearing lubrication for oil-lubricated bearings. More specifically, we consider two kinds of lubrication anomalies: lack of lubrication and contamination of lubrication. Our work involves the use of techniques in sensing and analyses of acoustic emissions from the bearing housing for detection of anomalous lubrication conditions of the above classes. We explore these techniques by conducting controlled experiments where, conditions equivalent to the appropriate fault condition are simulated in a test rig and AE readings are recorded; as a baseline, we also record vibration readings using traditional accelerometers. Our analyses will consist of extracting features from the AE signal that can suitably distinguish between normal and abnormal lubrication conditions. Additional analyses that can potentially be used to understand the degree of severity of the abnormality will also be presented.