Domain Adaptation-Based Transfer Learning for Gear Fault Diagnosis Under Varying Working Conditions

2021 ◽  
Vol 70 ◽  
pp. 1-10
Author(s):  
Chao Chen ◽  
Fei Shen ◽  
Jiawen Xu ◽  
Ruqiang Yan
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Exploring Sample/Feature Hybrid Transfer for Gear Fault Diagnosis Under Varying Working Conditions

2020 ◽  
Vol 20 (4) ◽  
Author(s):  
Fei Shen ◽  
Reza Langari ◽  
Ruqiang Yan
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Transfer Model ◽  
Chi Square ◽  
Operation Conditions ◽  
Similarity Estimation ◽  
Gear Fault ◽  
Chi Square Test ◽  
Gear Fault Diagnosis

Abstract Unknown environmental noise and varying operation conditions negatively affect gear fault diagnosis (GFD) performance. In this paper, the sample/feature hybrid transfer learning (TL) strategies are adopted for GFD under varying working conditions, where source working conditions are considered to help the learning of target working conditions. Here, a multiple domains-feature vector is extracted where certain insensitive features offset the adverse effects of varying working conditions on sensitive features, including time domain, frequency domain, noise domain, and torque domain. Before TL, the signed-rank and chi-square test-based similarity estimation frame is adopted to select source data sets, aiming to reduce the possibility of negative transfer. Then, the hybrid transfer model, including the fast TrAdaBoost and partial model-based transfer (PMT) algorithm, is carried out, whose weights are allocated in sample and feature, respectively. Related experiments were conducted on the drivetrain dynamics simulator, which proves that feature transfer is more suitable for low-quality source domains while sample transfer is more suitable for high-quality source domains. Compared with non-transfer strategy, transfer learning is a useful tool to solve a practical GFD problem when facing with multiple working conditions, thus enhancing the universality and application value in fault diagnosis field.

scholarly journals Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Chao Chen ◽  
Fei Shen ◽  
Jiawen Xu ◽  
Ruqiang Yan
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Rapid Development ◽  
Target Domain ◽  
Source Domain ◽  
Model Parameter ◽  
Operation Conditions ◽  
Gear Fault ◽  
Gear Fault Diagnosis

AbstractGear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications. However, the various working conditions would degrade the diagnostic performance and make gear fault diagnosis (GFD) more and more challenging. In this paper, a novel model parameter transfer (NMPT) is proposed to boost the performance of GFD under varying working conditions. Based on the previous transfer strategy that controls empirical risk of source domain, this method further integrates the superiorities of multi-task learning with the idea of transfer learning (TL) to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition (target domain) and another (source domain), and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable. For NMPT implementation, insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task. Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions. The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zitong Wan ◽  
Rui Yang ◽  
Mengjie Huang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Evaluation Method ◽  
Working Condition ◽  
Negative Effects ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Fault Feature Extraction ◽  
Better Than

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.

A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

2019 ◽  
Vol 234 (1) ◽  
pp. 168-182 ◽  
Author(s):  
Jialin Li ◽  
Xueyi Li ◽  
David He ◽  
Yongzhi Qu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Deep Neural Network ◽  
Domain Adaptation ◽  
Diagnostic Model ◽  
Target Domain ◽  
Vibration Signals ◽  
Learning Network

In recent years, research on gear pitting fault diagnosis has been conducted. Most of the research has focused on feature extraction and feature selection process, and diagnostic models are only suitable for one working condition. To diagnose early gear pitting faults under multiple working conditions, this article proposes to develop a domain adaptation diagnostic model–based improved deep neural network and transfer learning with raw vibration signals. A particle swarm optimization algorithm and L2 regularization are used to optimize the improved deep neural network to improve the stability and accuracy of the diagnosis. When using the domain adaptation diagnostic model for fault diagnosis, it is necessary to discriminate whether the target domain (test data) is the same as the source domain (training data). If the target domain and the source domain are consistent, the trained improved deep neural network can be used directly for diagnosis. Otherwise, the transfer learning is combined with improved deep neural network to develop a deep transfer learning network to improve the domain adaptability of the diagnostic model. Vibration signals for seven gear types with early pitting faults under 25 working conditions collected from a gear test rig are used to validate the proposed method. It is confirmed by the validation results that the developed domain adaptation diagnostic model has a significant improvement in the adaptability of multiple working conditions.

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