Acoustic Emission Intelligent Identification for Initial Damage of the Engine based on Single Sensor

Abstract

Detecting the initial damage of the engine precisely is conducive to finding out the failure symptom timely and ensuring the reliable operation of the engine for avoiding malignant accidents. This article proposes a single-sensor acoustic emission (AE) diagnosis method based on deep learning (DL) for structural health monitoring (SHM) and initial damage intelligent identification of the engine. Firstly, the time-frequency domain features of AE signals collected from the engine test bench under different initial damages are extracted by continuous wavelet transform (CWT), and two-dimensional time-frequency images are constructed using the obtained results. After that, the proposed deep convolutional neural networks (DCNN) is employed to directly extract damage features from the converted two-dimensional time-frequency images via a multi-layer fusion and perform initial damage intelligent identification. In addition, the white Gaussian noise with different signal-to-noise ratio (SNR) is added to the raw AE signal, and the robustness of the proposed DCNN method is verified. Finally, to evaluate the performance of the proposed DCNN, the constructed test accuracy Euclidean distance and other indexes including Kappa, microF1 and macroF1 are introduced to compare with other commonly machine learning methods. The results demonstrated the effectiveness of the proposed DCNN model, which lays the foundation for the application of the single-sensor AE diagnosis method based on DCNN in SHM and initial damage intelligent identification of the engine.