Monitoring hydrodynamic bearings with acoustic emission and vibration analysis

Abstract

Acoustic emission (AE) is one of many available technologies for condition health monitoring and diagnosis of rotating machines such as bearings. In recent years there have been many developments in the use of Acoustic Emission technology (AET) and its analysis for monitoring the condition of rotating machinery whilst in operation, particularly on high speed machinery. Unlike conventional technologies such as oil analysis, motor current signature analysis (MCSA) and vibration analysis, AET has been introduced due to its increased sensitivity in detecting the earliest stages of loss of mechanical integrity. This research presents an experimental investigation that is aimed at developing a mathematical model and experimentally validating the influence of operational variables such as film thickness, rotational speed, load, power loss, and shear stress for variations of load and speed conditions, on generation of acoustic emission in a hydrodynamic bearing. It is concluded that the power losses of the bearing are directly correlated with acoustic emission levels. With exponential law, an equation is proposed to predict power losses with reasonable accuracy from an AE signal. This experimental investigation conducted a comparative study between AE and Vibration to diagnose the rubbing at high rotational speeds in the hydrodynamic bearing. As it is the first known attempt in rotating machines. It has been concluded, that AE parameters such as amplitude, can perform as a reliable and sensitive tool for the early detection of rubbing between surfaces of a hydrodynamic bearing and high speed shaft. The application of vibration (PeakVue) analysis was introduced and compared with demodulation. The results observed from the demodulation and PeakVue techniques were similar in the rubbing simulation test. In fact, some defects on hydrodynamic bearings would not have been seen in a timely manner without the PeakVue analysis.In addition, the application of advanced signal processing and statistical methods was established to extract useful diagnostic features from the acquired AE signals in both time and frequency domain. It was also concluded that the use of different signal processing methods is often necessary to achieve meaningful diagnostic information from the signals. The outcome would largely contribute to the development of effective intelligent condition monitoring systems which can significantly reduce the cost of plant maintenance. To implement these main objectives, the Sutton test rig was modified to assess the capability of AET and vibration analysis as an effective tool for the detection of incipient defects within high speed machine components (e.g. shafts and hydrodynamic bearings). The first chapter of this thesis is an introduction to this research and briefly explains motivation and the theoretical background supporting this research. The second and third chapters, summarise the relevant literature to establish the current level of knowledge of hydrodynamic bearings and acoustic emission, respectively. Chapter 4 describes methodologies and the experimental arrangements utilized for this investigation. Chapter 5 discusses different NDT diagnosis. Chapter 6 reports on an experimental investigation applied to validate the relationship between AET on operational rotating machines, such as film thickness, speed, load, power loss, and shear stress. Chapter 7 details an investigation which compares the applicability of AE and vibration technologies in monitoring a rubbing simulation on a hydrodynamic bearing.