AbstractThis study was concerned with investigating the wear characteristics of small direct current electric motors which are widely used in commercial and consumer machinery. This was aimed at developing the enabling techniques and methodologies to allow the establishment of effective and economical asset recovery processes for small electro-mechanical assemblies by manufacturers of commercial machinery. The wear process in small dc motors that are used in photocopying machines was investigated and physical wear parameters that can be used to estimate the usage level of these motors were identified. Accelerated life test laboratory experiments were conducted to investigate the changes in the characteristics of the wear indicating parameters with motor usage level. The experimental results have identified the reverse current waveforms as the single most reliable wear indicating parameter for the investigated motors. Detailed investigations were conducted into signal processing methods and data analysis techniques to interpret the experimental data. A novel transform for feature extraction (TFE) has been investigated and enhancements in its application were proposed. It was demonstrated that the TFE has data reduction and feature extraction capabilities which are superior to other existing techniques. Artificial neural networks (ANNs) were investigated to determine their suitability in estimating the motor usage level from experimental data of the wear indicating
parameters. Optimum ANN architectures were developed and utilised to classify the motor usage level in discrete bands. Finally, a production motor test system for asset recovery screening was developed using the findings of the experimental results.This study has demonstrated the significance of reliable asset recovery screening for small electro-mechanical assemblies and developed the test and analysis techniques to achieve it.
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