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

Steve Koshy Mathew,Yu Zhang

doi:10.3390/app10196890

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.

Highlights

Internal Combustion Engines (ICEs) are the major power source for a variety of application including automobiles, aircraft, marine units, lighting plants, machine tools, power tool, etc
This can be achieved by the use of the condition-based maintenance scheme for emergent fault diagnosis or detecting any deviations from optimal conditions
The aim of this paper is to present a new fault diagnosis method for a gasoline ICE with various fault conditions by analyzing its acoustic signals

Summary

Introduction

Internal Combustion Engines (ICEs) are the major power source for a variety of application including automobiles, aircraft, marine units, lighting plants, machine tools, power tool, etc. In order for the vehicle manufactures to comply with increasingly stringent regulations, there is a need to maintain the overall efficiency, performance and emission level in an ICE. It is always crucial to run an engine in optimal conditions. This can be achieved by the use of the condition-based maintenance scheme for emergent fault diagnosis or detecting any deviations from optimal conditions. A spark ignition engine, which is a type of ICE, is subject to maintenance issues such as aged spark plug, damaged oxygen sensor or prolonged knocking, which can deteriorate the performance of the engine or even worse can cause total engine failure in the long run [2]. Fault detection systems can help identify these faults at an early stage and reduce further damage to the engine, thereby increasing safety and reliability of the vehicle

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Discussion

Conclusion