Acoustic emission (AE) is one of many available technologies for conditionhealth monitoring and diagnosis of rotating machines such as bearings. Inrecent years there have been many developments in the use of AcousticEmission technology (AET) and its analysis for monitoring the condition ofrotating machinery whilst in operation, particularly on high speed machinery.Unlike conventional technologies such as oil analysis, motor current signatureanalysis (MCSA) and vibration analysis, AET has been introduced due to itsincreased sensitivity in detecting the earliest stages of loss of mechanicalintegrity.This research presents an experimental investigation that is aimed atdeveloping a mathematical model and experimentally validating the influence ofoperational variables such as film thickness, rotational speed, load, power loss,and shear stress for variations of load and speed conditions, on generation ofacoustic emission in a hydrodynamic bearing. It is concluded that the powerlosses of the bearing are directly correlated with acoustic emission levels. Withexponential law, an equation is proposed to predict power losses withreasonable accuracy from an AE signal.This experimental investigation conducted a comparative study between AEand Vibration to diagnose the rubbing at high rotational speeds in thehydrodynamic bearing. As it is the first known attempt in rotating machines. Ithas been concluded, that AE parameters such as amplitude, can perform as areliable and sensitive tool for the early detection of rubbing between surfaces ofa hydrodynamic bearing and high speed shaft.The application of vibration (PeakVue) analysis was introduced and comparedwith demodulation. The results observed from the demodulation and PeakVuetechniques were similar in the rubbing simulation test. In fact, some defects onhydrodynamic bearings would not have been seen in a timely manner withoutthe PeakVue analysis.In addition, the application of advanced signal processing and statisticalmethods was established to extract useful diagnostic features from the acquiredAE signals in both time and frequency domain. It was also concluded that theuse of different signal processing methods is often necessary to achievemeaningful diagnostic information from the signals. The outcome would largelycontribute to the development of effective intelligent condition monitoringsystems which can significantly reduce the cost of plant maintenance.To implement these main objectives, the Sutton test rig was modified to assessthe capability of AET and vibration analysis as an effective tool for the detectionof incipient defects within high speed machine components (e.g. shafts andhydrodynamic bearings).The first chapter of this thesis is an introduction to this research and brieflyexplains motivation and the theoretical background supporting this research.The second and third chapters, summarise the relevant literature to establishthe current level of knowledge of hydrodynamic bearings and acoustic emission,respectively. Chapter 4 describes methodologies and the experimentalarrangements utilized for this investigation. Chapter 5 discusses different NDTdiagnosis. Chapter 6 reports on an experimental investigation applied tovalidate the relationship between AET on operational rotating machines, suchas film thickness, speed, load, power loss, and shear stress. Chapter 7 detailsan investigation which compares the applicability of AE and vibrationtechnologies in monitoring a rubbing simulation on a hydrodynamic bearing.
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