Rotatory Machine Fault Detection Using CNNs on Spectrogram Signal Data
Abstract
Rotary machines are vital in industrial and electrical systems, and prompt defect detection is crucial to prevent operational failures and financial losses. This article presents a framework using a Convolutional Neural Network (CNN) for defect detection via spectrogram images derived from simulated voltage, current, and load signals of rotary machines. The dataset, generated using MATLAB simulations and accessible on Kaggle, comprises spectrograms depicting normal operation and three fault conditions: $10 \Omega, 30 \Omega$, and $60 \Omega$. The CNN model proficiently extracts time-frequency characteristics from the spectrograms, attaining an overall classification accuracy of 96.3%, with precision, recall, and F1-scores continuously above 95% across all fault categories. The findings illustrate the model’s capacity to identify nuanced differences in machine behavior resulting from varying fault resistances. In contrast to traditional vibration- and signal-based techniques, the proposed method offers a resilient, non-invasive, and automated alternative for monitoring the state of rotary machines, facilitating predictive maintenance and mitigating the risk of unforeseen breakdowns. This research highlights the efficacy of integrating deep learning with spectrogram analysis for precise industrial problem identification.