# Acoustic-Based Engine Fault Diagnosis Using WPT, PCA and Bayesian Optimization
###### Abstract  
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. eng 20202010 10 6890 29 September 20202010 October 202015 September 202029 September 20202010 October 202015 September 2020  
Fault diagnosis methods have been categorized into two main types: signal-based and model-based methods [3]. In this study, we used a signal-based method which includes extracting useful features and comparing them against those of the nominal operational conditions using acoustic information from ICEs. Signal-based methods are less complex, easier to implement and more flexible to operation changes compared to model-based fault diagnosis methods. Information from ICEs can be gathered in various formats such as acoustic signals, vibration [4], oil quality [5] and thermal images [6], which are widely studied for engine fault diagnosis. Other analysis techniques include in-cylinder pressure mapping [7] and instantaneous angular speed measurement [8], which can be tedious and expensive. Recently, acoustic signals have gained increased attention for engine fault diagnosis because of the capacity of taking measurements from a distance, avoiding safety risks and removing the need of temperature sensitive sensors in the case of vibration [9]. Acoustic analysis has not been used widely in the past due to the fact that these signals are susceptible to high noise content and are easily influenced by local environment. However, this could be mitigated through the use of sophisticated signal processing, denoising and feature mining techniques to extract useful information from a contaminated signal. The signal-based fault diagnosis system consists of mainly four phases: data collection, signal decomposition, feature extraction and fault condition classification [10]. The traditional signal processing approach uses time-domain and frequency-domain analysis to distinguish different fault conditions. For example, the authors of [11] used frequency-domain analysis, including Fast Fourier Transform (FFT), envelope analysis and order tracking, for the recognition of different operating conditions and fault types. The major disadvantage of FFT-based approaches is that it is not adequate to analyze the time-dependent elements in the frequency domain only. This issue can be solved through the use of time-frequency analysis methods, such as Short-time Fourier Transform (STFT) [12] and wavelet transform. Wavelet transform is particularly effective compared to STFT because of its capability of analyzing signals in multi-resolution time-frequency windows. Therefore, it is been used widely for fault diagnosis of engines [13; 14; 15; 16; 17]. Although many variations of wavelet transform exist, in this paper, Wavelet Packet Transform (WPT) is used because WPT can transform the signal into both high- and low-level wavelet coefficients. This is significant, since various fault types embedded in different frequency ranges are analyzed here [18; 19].