School of Computing and Engineering Diagnostic Engineering Research Group Diagnosis and Prognosis of Machinery Health based on Advanced Intelligent Computations Shukri Ali Abdusslam, 1 st Year PhD supervised by Prof. A. Ball and Dr. F.Gu The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK Abstract 5. Interim Conclusion and future work Analysis in the time domain, frequency domain and spectrum domain show good detection and diagnosis results. However, the amplitude of the features is not sufficiently high for reliable diagnosis. Joint time-frequency analysis will be used to enhance the detection and diagnosis features. Advanced data analysis approaches will be reviewed, In addition, condition monitoring data for evaluating the processing methods reviewed will be collected. Furthermore, non-linear time-series will be investigated. Vibration Based Diagnosis An undamaged bearing under load is subjected to complex forces and moments. These include static forces such as shaft loads and preloads, dynamic forces due to centrifugal loads, fluid pressure, traction and friction. For a good bearing operating at a constant shaft speed and load, all forces are in quasi-equilibrium. A primary mode of bearing failure is due to localized fatigue spalling of bearing elements: outer race, inner race, rolling elements and carriage. When such a defect exists, a rapid localised change in the elastic deformation of the elements takes place and a corresponding transient force imbalance occurs. The transient forces will then cause high impact vibration on the bearing components and bearing housing. Therefore, bearing faults can be detected by vibration measurement and analysis. More advanced data processing methods are required to achieve the detection and diagnosis of faults as early a stage as possible. The data from machinery health monitoring contains high noise components and low information content. The research is concentrated on developing more advanced methods to analysis the data for more accurate diagnosis and prognosis of machinery health. Rolling bearings are the most common components used in different machines and the data from them are representative in terms of wide frequency bands, short impulses and random noise components. The method development is based on bearing systems at the beginning. Vibration signal is employed as the data sources for the analysis. Advanced intelligent computations which include non-linear time-series, various evolutionary algorithms, adaptive pattern algorithms, various neural networks and their ensembles, non-linear system based data conditioning will be applied to the data and their diagnosis performance will be investigated based on different degrees and types of faults from bearings. This research will produce a set of tools for accurate diagnosis of machines based on the advanced the intelligent methods. Test Facilities Aim To develop advanced approaches to the diagnosis and prognosis based on advanced intelligent computations which includes nonlinear time-series, various evolutionary algorithms, adaptive pattern algorithms, various neural networks and their ensembles, non-linear system based data conditioning, etc. Bearing Test Rig & Vibration Measurement