Condition based monitoring is widely used for the determination of the health of machines. The Nottingham Trent University Computing Department has developed a new system, the Incremental Motion Encoder (!ME), which is based on the time interpolation of the digital signals produced by an optical encoder. Experiments have shown that the !ME can be used as a condition based maintenance sensor as it is possible to detect rolling element defects, an unbalanced shaft and oil contamination of a bearing. The system uses a geometrically configured optical device to scan a precision encoder disc and Digital Signal Processing technology is used to interpret the signals. Previous work has demonstrated the qualitative usefulness of the 1ME. However, further work was needed to assess the accuracy of the measurements, to analyse the principles of the 1ME, to validate the performance of the existing device and to develop methods for error definition and error compensation. Testing and experimentation on the existing experimental system have been carried out by the Candidate and an understanding gained of the device. The sources of error of the 1ME have been identified, which had not been quantified previously. Measuring and compensating for the three main sources of error, read head position, eccentricity of the encoder disc and encoder abnormalities are the three major tasks of the project. Modifications to the experimental rig have been developed in order to allow these tasks to be addressed. The Candidate has developed three different types of techniques to measure the position error of the read heads. A pattern recognition method was developed and is successful for 1ME systems that use an encoder disc with significant grating line errors. A second method using Fast Fourier Transform (FFT) has been developed to exploit the fact that the difference in the phase angles, obtained using a FFT, gives the angle between the read head positions. The new experimental system is now able to obtain the angular position of the read heads by using the index grating line. The third method relies on the presence of the index grating line on the encoder disc which may not be present in all systems. Eccentricity of disc centre relative to the centre of rotation affects the correct calculation of the angular position of the encoder disc. Algorithms have been developed by the Candidate in order to compensate for this type of error. Experimental results have shown that angular position error can be corrected successfully. The Candidate has developed methods for detection of small abnormalities of the encoder disc by using a multiple averaging technique. Computational algorithms have been developed to correct the encoder disc abnormalities by using individual information from each read head, promising results have been obtained from the experimental 1ME. An 1ME device can be tailored to fulfil the desired requirements of resolution, bandwidth and accuracy. A self calibration instrument can be developed by using the previously mentioned techniques in order to self calibrate and increase the accuracy and reliability of an IME's results.

published_or_final_version / Computer Science / Master / Master of Philosophy

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A number of researchers have investigated the application of neural networks to visual recognition, with much of the emphasis placed on exploiting the network's ability to generalise. However, despite the benefits of such an approach it is not at all obvious how networks can be developed which are capable of recognising objects subject to changes in rotation, translation and viewpoint. In this study, we suggest that a possible solution to this problem can be found by studying aspects of visual psychology and in particular, perceptual organisation. For example, it appears that grouping together lines based upon perceptually significant features can facilitate viewpoint independent recognition. The work presented here identifies simple grouping measures based on parallelism and connectivity and shows how it is possible to train multi-layer perceptrons (MLPs) to detect and determine the perceptual significance of any group presented. In this way, it is shown how MLPs which are trained via backpropagation to perform individual grouping tasks, can be brought together into a novel, large scale network capable of determining the perceptual significance of the whole input pattern. Finally the applicability of such significance values for recognition is investigated and results indicate that both the NILP and the Kohonen Feature Map can be trained to recognise simple shapes described in terms of perceptual significances. This study has also provided an opportunity to investigate aspects of the backpropagation algorithm, particularly the ability to generalise. In this study we report the results of various generalisation tests. In applying the backpropagation algorithm to certain problems, we found that there was a deficiency in performance with the standard learning algorithm. An improvement in performance could however, be obtained when suitable modifications were made to the algorithm. The modifications and consequent results are reported here.

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

by Leung Kin Lap. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 70-72). / ACKNOWLEDGEMENTS --- p.I / ABSTRACT --- p.II / TABLE OF CONTENTS --- p.III / TABLE OF FIGURES --- p.IV / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Contributions of the thesis --- p.2 / Chapter 1.2 --- Organization of the thesis --- p.4 / Chapter Chapter 2 --- Previous Work --- p.5 / Chapter 2.1 --- An overview of 3-D interpretation --- p.5 / Chapter 2.1.1 --- Multiple-View Clues --- p.5 / Chapter 2.1.2 --- Single-View Clues --- p.6 / Chapter 2.2 --- Line Drawing Interpretation --- p.7 / Chapter 2.2.1 --- Qualitative Interpretation --- p.7 / Chapter 2.2.2 --- Quantitative Interpretation --- p.10 / Chapter 2.3 --- Previous Methods of Quantitative Interpretation by Optimization --- p.12 / Chapter 2.3.1 --- Extremum Principle for Shape from Contour --- p.12 / Chapter 2.3.2 --- MSDA Algorithm --- p.14 / Chapter 2.4 --- Comments on Previous Work on Line Drawing Interpretation --- p.17 / Chapter Chapter 3 --- An Iterative Clustering Procedure for Imperfect Line Drawings --- p.18 / Chapter 3.1 --- Shape Constraints --- p.19 / Chapter 3.2 --- Problem Formulation --- p.20 / Chapter 3.3 --- Solution Steps --- p.25 / Chapter 3.4 --- Nearest-Neighbor Clustering Algorithm --- p.37 / Chapter 3.5 --- Discussion --- p.38 / Chapter Chapter 4 --- Experimental Results --- p.40 / Chapter 4.1 --- Synthetic Line Drawings --- p.40 / Chapter 4.2 --- Real Line Drawing --- p.42 / Chapter 4.2.1 --- Recovery of real images --- p.42 / Chapter Chapter 5 --- Conclusion and Future Work --- p.65 / Appendix A --- p.67 / Chapter A. 1 --- Gradient Space Concept --- p.67 / Chapter A. 2 --- Shading of images --- p.69 / Appendix B --- p.70