Automobile engine condition monitoring using sound emission

A wavelet packet transform (WPT) is a well-known technique used for data and signal-processing that has proven to be successful in condition monitoring and fault diagnosis. In this study, using feature extraction based on wavelet transformation, sound signals emitted from automobile engines under both faulty and healthy conditions are analyzed. The intention is to categorize sound signals into both healthy and faulty classes. Sound signals are generated from 4 different automobile engines in both healthy and faulty conditions. The investigated fault is within the ignition system of the engines. In addition, there are other possible problems that may also affect the generated sound signals. In the reported study, a set of features is initially extracted from the recorded signals. The more informative features are later selected using a correlation-based feature selection (CFS) algorithm. Results prove the efficiency of wavelet-based feature extraction for the case study of the reported work.

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