Engine spark ignition diagnosis with wavelet packet transform and case-based reasoning

With the development of modern technology, modern vehicles adopt electronic control system for injection and ignition. In traditional way, whenever there is any fault in an automotive engine, an automotive mechanic usually performs a diagnosis in the ignition system of the engine to check any exceptional symptoms. In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem based on the signals captured using a computer-linked automotive scope meter. The advantage of CBR model is that multiple possible diagnoses can be provided to the user, instead of a single most probable diagnosis provided by traditional network-based models such as multi-layer perceptrons (MLP). So, CBR paradigm fits better the requirements of an expert system. CBR is effective but mostly inefficient in time because every instance in a case library must be compared during reasoning. To overcome this inefficiency, case base maintenance (CBM) technique is employed. Considering the ignition patterns captured by scope meter are very similar. Their difference can only be compared with extracted important signal features. Hence, wavelet packet transform (WPT) was proposed for feature extraction.