Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox

TLDR

Traditional fault diagnosis of wind turbine gearboxes separates feature extraction and classification, requiring additional signal processing and expertise. This study proposes an end‑to‑end learning framework that automatically learns fault features from raw vibration signals and classifies gearbox faults without extra processing. The authors design a multiscale convolutional neural network that simultaneously extracts multiscale features and classifies faults using a hierarchical CNN with multiple convolutional and pooling layers, and evaluate it on a WT gearbox test rig. Experimental results show that the MSCNN outperforms traditional CNNs and multiscale feature extractors, achieving superior diagnosis performance.

Abstract

This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches, where feature extraction and classification are separately designed and performed, this paper aims to automatically learn effective fault features directly from raw vibration signals while classify the type of faults in a single framework, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise. Considering the multiscale characteristics inherent in vibration signals of a gearbox, a new multiscale convolutional neural network (MSCNN) architecture is proposed to perform multiscale feature extraction and classification simultaneously. The proposed MSCNN incorporates multiscale learning into the traditional CNN architecture, which has two merits: 1) high-level fault features can be effectively learned by the hierarchical learning structure with multiple pairs of convolutional and pooling layers; and 2) multiscale learning scheme can capture complementary and rich diagnosis information at different scales. This greatly improves the feature learning ability and enables better diagnosis performance. The proposed MSCNN approach is evaluated through experiments on a WT gearbox test rig. Experimental results and comprehensive comparison analysis with respect to the traditional CNN and traditional multiscale feature extractors have demonstrated the superiority of the proposed method.