Vibration-based gearbox fault diagnosis using a multi-scale convolutional neural network with depth-wise feature concatenation

Abstract

This article proposes a novel approach for vibration-based gearbox fault diagnosis using a multi-scale convolutional neural network with depth-wise feature concatenation named MixNet. In industrial environments where equipment reliability directly impacts productivity, safety, and operational efficiency, timely and accurate fault detection in gearboxes is of paramount importance. As critical components in manufacturing, energy production, transportation, and heavy machinery, gearboxes constitute major potential failure points, with malfunctions leading to costly downtime and, in severe cases, catastrophic incidents. The proposed method addresses these industrial challenges by integrating advanced signal processing techniques with deep learning architectures to enhance diagnostic accuracy and robustness. Specifically, MixNet utilizes multi-scale convolutional layers combined with depth-wise feature concatenation to extract discriminative features from spectrogram representations of vibration signals, generated via the Short-time Fourier transform (STFT). This approach offers several practical advantages for engineering applications, including non-invasive monitoring that eliminates the need for disassembly, early fault detection that facilitates condition-based maintenance strategies, automated diagnosis that minimizes reliance on domain-specific expertise, and robust performance under noisy and variable operating conditions. Experimental results on the Gearbox fault diagnosis dataset demonstrate that MixNet outperforms existing deep learning models, achieving a significantly higher accuracy of 99.32% with a relatively fast training time of only 4 minutes and 29 seconds. The combination of high accuracy and computational efficiency renders the proposed method well-suited for deployment in real-time monitoring systems across manufacturing plants, power generation facilities, and automotive applications, where it has the potential to reduce maintenance costs by up to 30% and improve equipment availability by enabling the detection of incipient faults before catastrophic failures.