scholarly journals Adaptive Transfer Learning Based on a Two-Stream Densely Connected Residual Shrinkage Network for Transformer Fault Diagnosis over Vibration Signals

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2130
Author(s):  
Xiaoyan Liu ◽  
Yigang He ◽  
Lei Wang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Operating Conditions ◽  
Data Set ◽  
Vibration Signals ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Transformer Fault

Vibration signal analysis is an efficient online transformer fault diagnosis method for improving the stability and safety of power systems. Operation in harsh interference environments and the lack of fault samples are the most challenging aspects of transformer fault diagnosis. High-precision performance is difficult to achieve when using conventional fault diagnosis methods. Thus, this study proposes a transformer fault diagnosis method based on the adaptive transfer learning of a two-stream densely connected residual shrinkage network over vibration signals. First, novel time-frequency analysis methods (i.e., Synchrosqueezed Wavelet Transform and Synchrosqueezed Generalized S-transform) are proposed to convert vibration signals into different images, effectively expanding the samples and extracting effective features of signals. Second, a Two-stream Densely Connected Residual Shrinkage (TSDen2NetRS) network is presented to achieve a high accuracy fault diagnosis under different working conditions. Furthermore, the Residual Shrinkage layer (RS layer) is applied as a nonlinear transformation layer to the deep learning framework to remove unimportant features and enhance anti-interference performance. Lastly, an adaptive transfer learning algorithm that can automatically select the source data set by using the domain measurement method is proposed. This algorithm accelerates the training of the deep learning network and improves accuracy when the number of samples is small. Vibration experiments of transformers are conducted under different operating conditions, and their results show the effectiveness and robustness of the proposed method.

scholarly journals Bearing Fault Diagnosis under Variable Working Conditions Based on Deep Residual Shrinkage Networks and Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xinyu Yang ◽  
Fulin Chi ◽  
Siyu Shao ◽  
Qiang Zhang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Operating Conditions ◽  
Mean Deviation ◽  
Variable Load ◽  
Vibration Data ◽  
Deep Learning Algorithm ◽  
Load Variable

Nowadays, deep learning has made great achievements in the field of rotating machinery fault diagnosis. But in the practical engineering scenarios, when facing a large number of unlabeled data and variable operating conditions, only using a deep learning algorithm may reduce the performance. In order to solve the above problem, this paper uses a method of combining transfer learning with deep learning. First, the deep shrinkage residual network is constructed by adding soft thresholds to extract the characteristics of bearing vibration data under noise redundancy. Then, the joint maximum mean deviation (JMMD) criterion and conditional domain adversarial (CDA) learning domain adapting network are used to align the source and target domains. At the same time, adding transferable semantic augmentation (TSA) regular items improves alignment performance between classes. Finally, the proposed model is verified by three experiments: variable load, variable speed, and variable noise, which overcomes the shortcomings of traditional deep learning and shallow transfer learning algorithms.

The Fault Diagnosis Method of Diesel Based on Wavelet Pocket and BP Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 910-913 ◽  
Author(s):  
Hong Hui Sun ◽  
Jun Xu ◽  
Qing Hua Zhang ◽  
Hong Xia Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Vibration Signals ◽  
Signal Energy ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Neural Network Algorithm

Because of the well time-frequency spectrum disposal capability of wavelet packet, the wavelet packet algorithm is used to analyze the time - frequency characteristics of diesel vibration signals. The signal energy distributing characteristics based on wavelet packet transform. are extracted and taken as diagnostic characteristic vector, then improved BP neural network algorithm that connects additional momentum with self-adaptive learning rate was used to classify and recognize faults of diesel valves. The experimental results show the fault diagnosis method of diesel based on wavelet pocket and BP neural network is effective and feasible.

scholarly journals Autonomous Assessment of Delamination Using Scarce Raw Structural Vibration and Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6239
Author(s):  
Asif Khan ◽  
Salman Khalid ◽  
Izaz Raouf ◽  
Jung-Woo Sohn ◽  
Heung-Soo Kim
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Experimental Testing ◽  
Laminated Composites ◽  
Structural Vibration ◽  
Support Vector ◽  
Learning Models ◽  
Time Frequency ◽  
Vibration Data

Deep learning has helped achieve breakthroughs in a variety of applications; however, the lack of data from faulty states hinders the development of effective and robust diagnostic strategies using deep learning models. This work introduces a transfer learning framework for the autonomous detection, isolation, and quantification of delamination in laminated composites based on scarce low-frequency structural vibration data. Limited response data from an electromechanically coupled simulation model and from experimental testing of laminated composite coupons were encoded into high-resolution time-frequency images using SynchroExtracting Transforms (SETs). The simulated and experimental data were processed through different layers of pretrained deep learning models based on AlexNet, GoogleNet, SqueezeNet, ResNet-18, and VGG-16 to extract low- and high-level autonomous features. The support vector machine (SVM) machine learning algorithm was employed to assess how the identified autonomous features were able to assist in the detection, isolation, and quantification of delamination in laminated composites. The results obtained using these autonomous features were also compared with those obtained using handcrafted statistical features. The obtained results are encouraging and provide a new direction that will allow us to progress in the autonomous damage assessment of laminated composites despite being limited to using raw scarce structural vibration data.

scholarly journals Fault Diagnosis under Variable Working Conditions Based on STFT and Transfer Deep Residual Network

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Yan Du ◽  
Aiming Wang ◽  
Shuai Wang ◽  
Baomei He ◽  
Guoying Meng
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Classification Accuracy ◽  
Working Condition ◽  
Learning Ability ◽  
Residual Network ◽  
Time Frequency ◽  
Residual Learning

Fault diagnosis plays a very important role in ensuring the safe and reliable operations of machines. Currently, the deep learning-based fault diagnosis is attracting increasing attention. However, fault diagnosis under variable working conditions has been a significant challenge due to the domain discrepancy problem. This problem is also unavoidable in deep learning-based fault diagnosis methods. This paper contributes to the ongoing investigation by proposing a new approach for the fault diagnosis under variable working conditions based on STFT and transfer deep residual network (TDRN). The STFT was employed to convert vibration signal to time-frequency image as the input of the TDRN. To address the domain discrepancy problem, the TDRN was developed in this paper. Unlike traditional deep convolutional neural network (DCNN) methods, by combining with transfer learning, the TDRN can make a bridge between two different working conditions, thereby using the knowledge learned from a working condition to achieve a high classification accuracy in another working condition. Moreover, since the residual learning is introducing, the TDRN can overcome the problems of training difficulty and performance degradation existing in traditional DCNN methods, thus further improving the classification accuracy. Experiments are conducted on the popular CWRU bearing dataset to validate the effectiveness and superiority of the proposed approach. The results show that the developed TDRN outperforms those methods without transfer learning and/or residual learning in terms of the accuracy and feature learning ability for the fault diagnosis under variable working conditions.

scholarly journals Development and Validation of a Deep Learning Algorithm and Open-Source Platform for the Automatic Labelling of Motion Capture Markers

2021 ◽  
Author(s):  
Allison L. Clouthier ◽  
Gwyneth B. Ross ◽  
Matthew P. Mavor ◽  
Isabel Coll ◽  
Alistair Boyle ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Open Source ◽  
Transfer Learning ◽  
Motion Capture ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Motion Capture Data ◽  
Commercial Software ◽  
Data Set

AbstractThe purpose of this work was to develop an open-source deep learning-based algorithm for motion capture marker labelling that can be trained on measured or simulated marker trajectories. In the proposed algorithm, a deep neural network including recurrent layers is trained on measured or simulated marker trajectories. Labels are assigned to markers using the Hungarian algorithm and a predefined generic marker set is used to identify and correct mislabeled markers. The algorithm was first trained and tested on measured motion capture data. Then, the algorithm was trained on simulated trajectories and tested on data that included movements not contained in the simulated data set. The ability to improve accuracy using transfer learning to update the neural network weights based on labelled motion capture data was assessed. The effect of occluded and extraneous markers on labelling accuracy was also examined. Labelling accuracy was 99.6% when trained on measured data and 92.8% when trained on simulated trajectories, but could be improved to up to 98.8% through transfer learning. Missing or extraneous markers reduced labelling accuracy, but results were comparable to commercial software. The proposed labelling algorithm can be used to accurately label motion capture data in the presence of missing and extraneous markers and accuracy can be improved as data are collected, labelled, and added to the training set. The algorithm and user interface can reduce the time and manual effort required to label optical motion capture data, particularly for those with limited access to commercial software.

Vibration Fault Diagnosis for Wind Turbine Based on Enhanced Supervised Locally Linear Embedding

2014 ◽  
Vol 1008-1009 ◽  
pp. 983-987
Author(s):  
Xiang Wang ◽  
Yuan Zheng
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Learning Algorithm ◽  
Operating Conditions ◽  
Fault Classification ◽  
Maintenance Cost ◽  
Locally Linear Embedding ◽  
Diagnosis Method ◽  
Linear Embedding ◽  
Locally Linear

Fault diagnosis for wind turbine is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine. Fault diagnosis is essentially a kind of pattern recognition. In this paper, a novel fault diagnosis method based on enhanced supervised locally linear embedding is proposed for wind turbine. The approach first performs the recently proposed manifold learning algorithm locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features corresponding to different fault modes. Enhanced supervised locally linear embedding not only can map them into a low-dimensional embedded space to achieve fault feature extraction, but also can deal with new fault samples. Finally fault classification is carried out in the embedded manifold space. The wind turbine gearbox ball bearing vibration fault signals are used to validate the proposed fault diagnosis method. The results indicate that the proposed approach obviously improves the fault classification performance and outperforms the other traditional approaches.

scholarly journals A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2599
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Yu Sun ◽  
Shuqing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Recognition Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Hydraulic Pump ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

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