Tracking the severity of naturally developed spalls in rolling element bearings

Condition monitoring of rolling element bearing is vital for condition-based maintenance (CBM) in many industries. A key obstacle at present is the ability to accurately quantify the severity of the bearing faults, which is commonly measured in terms of the bearing defect size. Limitations of previous studies in the area include: (i) most accelerometer-based approaches were developed for artificial bearing faults instead of naturally developed spalls, and (ii) a systematic comparison between accelerometers and alternative measurements is not available. Therefore, this thesis aims at obtaining effective methods to estimate and track the growth of bearing spalls. This has been achieved by both advancing the processing of accelerometer signals and exploiting the capabilities of alternative measurements. Firstly, a novel approach based on accelerometers is proposed, which utilises natural frequency perturbations to estimate spall size. By comparing it with the well-established existing methods, it was found that all methods are effective for artificial spalls, but only the newly proposed approach is successful for naturally developed faults. Then, three alternative measurements (acoustic emission, instantaneous angular speed, and radial load) are investigated and benchmarked against acceleration on UNSW’s bearing test rig. It was found that radial load was far superior in fault-size estimation comparing to all other sensors, and achieved more precise results than accelerometers with less complex processing. This was justified considering radial load as a proxy for radial displacement, whose potential was recently suggested by theoretical studies. To confirm this, in the last part of this work, actual displacement sensors (proximity probes) were installed on the bearing test rig and a larger gearbox facility. Both experiments demonstrated that the proposed displacement approach can effectively estimate the size of natural spalls, with very limited signal processing required. This thesis has therefore provided three significant novel contributions to the field of bearing fault severity assessment: (i) the development of a new acceleration-based approach, effective on natural spalls for the first time, (ii) the collection and analysis of a new and comprehensive database of alternative measurements, obtained on naturally developed spalls, (iii) the discovery of the superior effectiveness of direct displacement measurements

Volume 2 – Conference: Wednesday, March 9

10. Internationales Fluidtechnisches Kolloquium:Group 1 | 2: Novel System Structures Group 3 | 5: Pumps Group 4: Thermal Behaviour Group 6: Industrial Hydraulic

Model Referenced Condition Monitoring of High Performance CNC Machine Tools

Generally, machine tool monitoring is the prediction of the system’s health based on signal acquisition and processing and classification in order to identify the causes of the problem. The producers of machine tools need to pay more attention to their products life cycle because their customers increasingly focus on machine tool reliability and costs. The present study is concerned with the development of a condition monitoring system for high speed Computer Numerical Control (CNC) milling machine tools. A model is a simplification of a real machine to visualize the dynamics of a mechatronic system. This thesis applies recent modelling techniques to represent all parameters which affect the accuracy of a component produced automatically. The control can achieve an accuracy approaching the tolerance restrictions imposed by the machine tool axis repeatability and its operating environment. The motion control system of the CNC machine tool is described and the elements, which compose the axis drives including both the electrical components and the mechanical ones, are analysed and modelled. SIMULINK models have been developed to represent the majority of the dynamic behaviour of the feed drives from the actual CNC machine tool. Various values for the position controller and the load torque have been applied to the motor to show their behaviour. Development of a mechatronic hybrid model for five-axis CNC machine tool using Multi-Body-System (MBS) simulation approach is described. Analysis of CNC machine tool performance under non-cutting conditions is developed. ServoTrace data have been used to validate the Multi-body simulation of tool-to-workpiece position. This thesis aspects the application of state of art sensing methods in the field of condition monitoring of electromechanical systems. The ballscrew-with-nut is perhaps the most prevalent CNC machine subsystem and the condition of each element is crucial to the success of a machining operation. It’s essential to know of the health status of ballscrew, bearings and nut. Acoustic emission analysis of machines has been carried out to determine the deterioration of the ballscrew. Standard practices such as use of a Laser Interferometer have been used to determine the position of the machine tool. A novel machine feed drive condition monitoring system using acoustic emission (AE) signals has been proposed. The AE monitoring techniques investigated can be categorised into traditional AE parameters of energy, event duration and peak amplitude. These events are selected and normalised to estimate remaining life of the machine. This method is shown to be successfully applied for the ballscrew subsystem of an industrial high-speed milling machine. Finally, the successful outcome of the project will contribute to machine tool industry making possible manufacturing of more accurate products with lower costs in shorter time