Fault diagnosis of planetary gearbox using a novel semi-supervised method of multiple association layers networks

The vibration signal can effectively represent the equipment fault information and be utilized for fault diagnosis by further extracting sensitive features. However, traditional supervised diagnosis requires a huge amount of labeled samples, and the features extraction and selection are mainly done manually, which are costly and time consuming. Deep Learning (DL) can automatically extract fault-sensitive features and effectively improve the recognition rate of fault diagnosis, while Semi-Supervised Learning (SSL) trains a model together with labeled and unlabeled data to increase recognition accuracy. In this paper, a novel deep Semi-Supervised method of Multiple Association Layers Networks (SS-MALN) framework of planetary gearbox vibration-based fault diagnosis is proposed. The SS-MALN model has the advantages of SSL and DL simultaneously, which can reduce the amount of labeled samples and improve the accuracy of recognition. The wavelet packet transform was employed to highlight various impulse components and present time-frequency features of the original vibration signal. Then, the transformed samples were divided into two parts, a major part of which deletes the category information, i.e. the labels. After that, the labeled and unlabeled samples were employed to train the improved SS-MALN model. Validation by Drivetrain Diagnostic Simulator testbed data on several planetary gearbox fault datasets manifests that our SS-MALN semi-supervised method can deliver enhanced performance over other traditional deep networks of planetary gearbox fault classification with less labeled data. (C) 2019 Elsevier Ltd. All rights reserved.