Fusion of Audio and Vibration Signals for Bearing Fault Diagnosis Based on a Quadratic Convolution Neural Network

Abstract

In this paper, a quadratic convolution neural network (QCNN) using both audio and vibration signals is utilized for bearing fault diagnosis. Specifically, to make use of multi-modal information for bearing fault diagnosis, the audio and vibration signals are first fused together using a 1 × 1 convolution. Then, a quadratic convolution neural network is applied for the fusion feature extraction. Finally, a decision module is designed for fault classification. The proposed method utilizes the complementary information of audio and vibration signals, and is insensitive to noise. The experimental results show that the accuracy of the proposed method can achieve high accuracies for both single and multiple bearing fault diagnosis in the noisy situations. Moreover, the combination of two-modal data helps improve the performance under all conditions.

Marine Diesel Engine Fault Detection Based on Xilinx ZYNQ SoC

“…Additionally, Cihan Ates, Tobias Höfchen, and others utilized Convolutional Autoencoders for predictive maintenance of rolling bearings, exhibiting impressive performance [6]. Jin Yan, Jian-bin Liao, and their colleagues combined second-order convolutional neural networks (QCNN) with audio and vibration signals from bearings, indicating the improved CNNs' ability to diagnose complex parameters such as vibration signals [7]. Jan Monieta, Lech Kasyk, and others achieved a diagnostic accuracy of over 90% for fuel injection systems using neural network (NN) machine learning methods for amplitude and frequency analysis [8].…”