Intelligent fault diagnosis of planetary gearbox based on adaptive normalized CNN under complex variable working conditions and data imbalance

Abstract

Abstract In real industrial application, the operating conditions of the planetary gearbox are always variable speed and variable load according to production. Meanwhile, the scarcity of fault data and different working conditions lead to serious data imbalance and distribution differences, which make a great challenge for planetary gearbox fault detection and diagnosis. To address these issues, a novel adaptive normalized convolutional neural network (ANCNN) is developed to accurately and automatically diagnose the different fault locations and severities of planetary gearbox considering the scenarios of complex variable working conditions and data imbalance. First, Teager calculated order (TCO) spectrum is applied to avoid the influences of large speed fluctuations and variable loads, which is an efficient pretreatment approach for the proposed ANCNN. Then, the batch normalization algorithm is adopted to eliminate the feature distribution differences caused by variable operating modes and data imbalance. Finally, in order to automatically adapt to different planetary gearbox diagnosis circumstances, the particle swarm optimization (PSO) strategy is used for optimizing and flexibly deciding the key hyperparameters of the designed model, thereby improving the overall performance of the model. The effectiveness of the presented method is confirmed through experiments on two different planetary gearbox datasets, which are from a generic planetary gearbox for industrial applications and a drivetrain dynamic simulator test rig. In addition, comparison with other mainstream intelligent diagnosis techniques validates the superiority of the presented method. The experimental results demonstrate that the presented method can achieve diagnostic accuracies of better than 99.8%, and also shows excellent stability for the unbalanced data classification.