A convolutional neural network (cnn)-based classification of rotary machine vibration and acoustic signals

Abstract

Accurate identification of operating conditions in rotating machinery is essential for performance monitoring and early detection of abnormal behavior. Rotating systems generate both structural vibration and airborne acoustic emissions that reflect changes in speed and mechanical imbalance. In this study, synchronized vibration and acoustic measurements were collected from a laboratory table fan using one triaxial accelerometer and two microphones. The fan was evaluated under four operating conditions: low speed, high speed, low speed with added mass on one blade, and high speed with added mass on one blade. Time-domain signals were segmented into overlapping 2-second windows and transformed into the frequency domain using Fast Fourier Transform (FFT). Short-Time Fourier Transform (STFT) was applied to generate spectrograms representing the time-frequency structure of both vibration and acoustic signals. A supervised Convolutional Neural Network (CNN) was trained on these multi-channel spectrograms to classify the operating condition. The dataset was divided into training, validation, and testing subsets to evaluate generalization performance. The trained model successfully distinguished the four operating states, achieving high classification accuracy across multiple sensor configurations, including single-sensor and fused measurements. Multi-sensor configurations incorporating acoustic data demonstrated faster convergence while maintaining perfect classification performance.