| Summary: | The Thesis can be broadly classified into 2 sections. Initially it attempts to solve an existing long-term problem in the spark plug manufacturing industry, namely reliability and criteria for spark plug production testing. The second section of the Thesis develops the preliminary work and explores the fundamental theory of ignition sparks in preparation for the main study of ignition system and ignition spark mathematical modelling. Section 1 of the Thesis begins by introducing spark plug faults and existing production faults detection equipment and methods. An evaluation of potential new fault detection and classification systems follows, in two phases: detection and analysis. A suitable electrical test and neural network based production test system is then derived from the preceding work. This first section of the Thesis culminates in a factory evaluation of the prototype system at the sponsoring company. Section 2 of the Thesis uses experience gained from Section 1 to effect an investigation into the fundamentals of ignition spark development, leading to the mathematical modelling of Kettering-type ignition systems and associated spark profiles. Finally, the practical benefits of the modelling are discussed with respect to potential real automotive applications. There are two main novel aspects of the work. (1) The use of a neural network to analyse and classify ignition spark waveforms is believed to be a novel idea which can be further extended in many areas beyond the spark plug testing application discussed here. (2) Ignition system and spark modelling is a relatively unexplored research area. This work has resulted in an increase in the level of complexity of model and therefore a potential increase in precision.
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