Gearbox fault diagnosis: A DL-based nonparametric filter approach

Abstract

Deep Learning (DL) models have become increasingly popular for gearbox fault diagnosis due to their ability to automatically extract features and deliver accurate results with time series data. Typically, these models are applied directly to raw data collected from industrial machines. However, such real-world data often contains significant noise and interference from other components, which can compromise signal quality and reduce the classification accuracy of DL models. This work investigates the effectiveness of signal preprocessing using the Energy Operator (EO) and its variants, non-parametric filters, to enhance the performance of convolutional neural networks (CNNs), a widely used deep learning technique, in fault diagnosis. By improving the signal-to-noise ratio (SNR), EO and its variants help the CNN classifier achieve better accuracy compared to when raw, unprocessed data is used. The findings highlight that the higher-order variant, EO123, significantly outperforms other EO variants, as well as the traditional approach of using raw data without preprocessing. This superior performance of EO123 is consistently demonstrated across three benchmark datasets, each representing different gearboxes under various fault conditions. The results highlight that EO123 is the most effective method for improving classification accuracy in DL-based gearbox fault diagnosis, making it the preferred choice over other preprocessing techniques and raw data.