Rotor Fault Diagnosis Method Based on 1D-CNN LSTM
Published in 2023 O2O Integrated Conference of the Institute of Internet, Broadcasting and Communication, 2023
Abstract
The manufacturing industry is rapidly evolving in the era of the Fourth Industrial Revolution, emphasizing large-scale data, high speed, and low power consumption. Consequently, the importance of rotating machinery has significantly increased. This paper proposes a fault diagnosis method for rotating machines based on 1D-CNN LSTM (One-Dimensional Convolutional Neural Network Long Short-Term Memory). The proposed deep learning model is capable of recognizing complex patterns and making accurate predictions from large datasets. Experimental results demonstrate fault diagnosis accuracies of 95.6%, 71.4%, and 97.4% for different fault types. The proposed model enables early detection of faults, preventing production line downtime and enhancing manufacturing efficiency and competitiveness.
Keywords: Anomaly Detection, 1D-CNN LSTM, Rotating Machine, Deep Learning