Acoustic-Based Engine Fault Diagnosis Using WPT, PCA and Bayesian Optimization

Featured Application In this paper, the proposed method is validated using experimental studies based on sound signals for engine fault diagnosis, though ultimately the developed framework can be widely applicable to many other industrial fault diagnosis scenarios, e.g., in aeronautical, automotive, energy and manufacturing industry. Engine fault diagnosis aims to assist engineers in undertaking vehicle maintenance in an efficient manner. This paper presents an automatic model and hyperparameter selection scheme for engine combustion fault classification, using acoustic signals captured from cylinder heads of the engine. Wavelet Packet Transform (WPT) is utilized for time-frequency analysis, and statistical features are extracted from both high- and low-level WPT coefficients. Then, the extracted features are used to compare three models: (i) standard classification model; (ii) Bayesian optimization for automatic model and hyperparameters selection; and (iii) Principle Component Analysis (PCA) for feature space dimensionality reduction combined with Bayesian optimization. The latter two models both demonstrated improved accuracy and the other performance metrics compared to the standard model. Moreover, with similar accuracy level, PCA with Bayesian optimized model achieved around 20% less total evaluation time and 8-19% less testing time, compared to the second model, for all fault conditions, which thus shows a promising solution for further development in real-time engine fault diagnosis.