Research on Turbocharger Surge Identification and Prediction Based on Acoustic Signal

This study presents a non-intrusive method for detecting surge in diesel engine turbochargers using acoustic signal analysis. We introduce an innovative surge identification approach that leverages multi-domain composite features derived from these acoustic signals. The research involved conducting controlled turbocharger surge experiments, which provided the data necessary to develop a comprehensive surge acoustic signal database. From this database, eighteen acoustic features were meticulously selected and extracted across both time and frequency domains. Their relevance to surge detection was evaluated using a random forest algorithm, enabling the identification of key features critical to accurate detection. These essential features were then integrated into a multi-domain composite feature set, which served as the sensitive indicator for turbocharger surge detection. During the model development phase, we systematically compared the performance of various machine learning algorithms, including random forest, support vector machines (SVM), decision trees, ensemble learning, and gradient boosting. The random forest classifier emerged as the most effective model due to its superior accuracy in surge identification. To further enhance the model’s performance, optimization algorithms were employed to fine-tune the hyperparameters of the random forest model, particularly focusing on the coefficients of the kernel function. The final model was rigorously validated using acoustic signals from turbocharger bench tests, demonstrating a surge identification accuracy exceeding 98%. This high level of accuracy underscores the robustness and effectiveness of the proposed method, offering a reliable solution for early and accurate surge detection in diesel engine turbochargers.