Deep Transfer Learning-Based Fault Diagnosis for Gearbox under Complex Working Conditions

Abstract

In the large amount of available data, information insensitive to faults in historical data interferes in gear fault feature extraction. Furthermore, as most of the fault diagnosis models are learned from offline data collected under single/fixed working condition only, this may cause unsatisfactory performance for complex working conditions (including multiple and unknown working conditions) if not properly dealt with. This paper proposes a transfer learning-based fault diagnosis method of gear faults to reduce the negative effects of the abovementioned problems. In the proposed method, a cohesion evaluation method is applied to select sensitive features to the task with a transfer learning-based sparse autoencoder to transfer the knowledge learnt under single working condition to complex working conditions. The experimental results on wind turbine drivetrain diagnostics simulator show that the proposed method is effective in complex working conditions and the achieved results are better than those of traditional algorithms.

Research on fault diagnosis method of planetary gearbox based on dynamic simulation and deep transfer learning

“…Zhu et al 23 calculated the domain loss between the source domain and the target domain through the linear combination of multiple Gaussian kernels, which enhanced the adaptive ability of the diagnostic model. Wan et al 24 put forward a TL means combining sensitive feature selection and sparse automatic encoder, which reduces the interference of insensitive information in the original signal and raises the diagnostic precision of the model under complex operating conditions. Han et al 25 extended the marginal distribution adaptation to joint distribution adaptation, allowing the suggested network to employ the identification structure linked to the labeled data in the source domain to adapt to the conditional distribution of the unlabeled target data and ensure higher precise distribution matching.…”

Semi-Supervised Transfer Learning Method for Bearing Fault Diagnosis with Imbalanced Data

“…Therefore, transfer learning is a promising technique for overcoming the challenge outlined above, as it is based on transferring knowledge across domains [15,16]. Transfer learning aims to increase model accuracy or reduce the number of labeled samples in the target domain by leveraging knowledge from the source domain [17,18]. In the area of transfer learning-based fault diagnosis, the feature spaces of the source and target domains are usually adopted by the maximum mean discrepancy (MMD) distance [19,20].…”

Rotating Machinery Fault Identification via Adaptive Convolutional Neural Network

“…As a key component of mechanical transmission system, rotating machineries have been widely used in the transmission system of automobiles [1], ships [2], wind turbine [3], machine tools, etc. However, in the actual industrial scene, they are easy to be broken down due to the harsh service environment and variable speed and load [4,5]. So, it is vulnerable to catastrophic accidents if health state of equipment is not considered in a timely manner.…”