Ocean waves can provide a renewable and secure energy supply to coastal residents around the world. Yet, to safely harness and convert the available energy, issues such as bearing reliability and maintainability need to be resolved. This paper presents the application of a PHM based research methodology to derive empirical models for estimating the wear of polymer bearings installed on wave energy converters. Forming the foundation of the approach is an applicable wave model, sample data set, and experimental test stand to impose loading conditions similar to that expected in real seas.

This paper reviews the application of oil debris monitoring as an effective PHM solution for wind turbine gearboxes. The paper explains the common surface fatigue damage mode of bearing and gear rolling elements and the characteristics of the destructive debris that result from this damage mode. The paper outlines a simple means of deriving accumulated debris count damage limits based upon basic gearbox component geometry and the use of moving averages for estimating rates of debris generation as a simple yet effective damage data-driven propagation model.

Aircraft engine bearing prognosis not only requires early detection of a bearing defect, but also the ability to predict bearing health conditions given certain operational scenarios. This paper summarizes a physics-based remaining useful life prediction method developed in the DARPA engine system prognosis (ESP) program. This investigation focuses on a typical roller bearing fault (or defect) on the outer raceway. Spall detection is based on the fusion of vibration and online oil debris sensors.

This paper deals with the development of a model based method for bearing fault diagnostics. This method effectively combines the information available in the data and the model for efficient classification of the bearing and the type of defect. A four degrees of freedom nonlinear rigid rotor model is used to simulate the rotor bearing system. Precession of the shaft is measured using proximity probes. The deviation of the measurement from the model is used to classify the system. Typically proximity probe data by itself does not contain enough information for accurate classification.

Rolling element bearings are key components in most rotating machinery. It is necessary to determine the condition of the bearing with reasonable degree of confidence. Many techniques have been developed for bearing fault detection. Each of these techniques have their own strengths and weaknesses. In this paper various features are compared for detecting inner race defects in rolling element bearings. Mutual information between the feature and defect is used as a quantitative measure of quality and the features are ranked appropriately.

In this paper, we present a new diagnosis and prognosis method using degree of randomness (DoR) measure and Laplace test procedure. The abnormal events are detected through the measure of change of randomness of vibration signals. The changes of randomness are resulted from the faulty components such as roller bearings. We aim at the early detection of semi-failure events through the use of Laplace test statistic which measures the rate changes of the abnormal event occurrence.

Bearing envelope analysis (BEA) is a powerful technique in the detection of faults in bearings. The improper selection of the envelope window frequency and window bandwidth can render the analysis ineffective. This can reduce the ability of a health and usage monitoring system (HUMS) to correctly identify a degraded bearing. This occurred recently: a teardown analysis (TDA) of a utility helicopter oil cooler fan housing found extensive bearing damage. The HUMS did not detect the fault. This paper is an analysis of why the BEA failed to detect the damage bearing.