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Open AccessJournal ArticleDOI

Multi-Layer domain adaptation method for rolling bearing fault diagnosis

Xiang Li, +4 more
- 01 Apr 2019 - 
- Vol. 157, pp 180-197
TLDR
The proposed domain adaptation method offers a new and promising tool for intelligent fault diagnosis and can be efficiently extracted in this way, and the cross-domain testing performance can be significantly improved.
About: 
This article is published in Signal Processing.The article was published on 2019-04-01 and is currently open access. It has received 283 citations till now. The article focuses on the topics: Domain (software engineering) & Supervised learning.

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A comprehensive review on convolutional neural network in machine fault diagnosis

Jinyang Jiao, +3 more
- 05 Dec 2020 - 
TL;DR: This work attempts to review and summarize the development of the Convolutional Network based Fault Diagnosis (CNFD) approaches comprehensively, and points out the characteristics of current development, facing challenges and future trends.
Journal ArticleDOI

Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism

Xiang Li, +3 more
- 01 Aug 2019 - 
TL;DR: Attention mechanism is introduced to assist the deep network to locate the informative data segments, extract the discriminative features of inputs, and visualize the learned diagnosis knowledge.
Journal ArticleDOI

Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

Wei Zhang, +10 more
- 01 Feb 2020 - 
TL;DR: This paper proposes a deep learning-based fault diagnosis method to address the imbalanced data problem by explicitly creating additional training data and validated that the data-driven methods can significantly benefit from the data augmentation.
Journal ArticleDOI

Potential, challenges and future directions for deep learning in prognostics and health management applications

Olga Fink, +5 more
- 01 Jun 2020 - 
TL;DR: A thorough evaluation of the current developments, drivers, challenges, potential solutions and future research needs in the field of deep learning applied to Prognostics and Health Management (PHM) applications can be found in this paper.
Journal ArticleDOI

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

Dhiraj Neupane, +1 more
- 27 Apr 2020 - 
TL;DR: This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms and can be of good help for future researchers to start their work on machinery fault Detection and diagnosis using the C WRU dataset.
References
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Proceedings Article

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Sergey Ioffe, +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
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Visualizing Data using t-SNE

Laurens van der Maaten, +1 more
- 01 Jan 2008 - 
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
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Learning representations by back-propagating errors

David E. Rumelhart, +2 more
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TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Journal ArticleDOI

A Survey on Transfer Learning

Sinno Jialin Pan, +1 more
- 01 Oct 2010 - 
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
Posted Content

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Sergey Ioffe, +1 more
- 11 Feb 2015 - 
TL;DR: Batch Normalization as mentioned in this paper normalizes layer inputs for each training mini-batch to reduce the internal covariate shift in deep neural networks, and achieves state-of-the-art performance on ImageNet.
Related Papers (5)

A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

Wei Zhang, +4 more
- 22 Feb 2017 - 

Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data

Liang Guo, +4 more
- 01 Sep 2019 - 

Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study

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Domain Adaptation via Transfer Component Analysis

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Frequently Asked Questions (13)
Q1. What are the contributions in "Multi-layer domain adaptation method for rolling bearing fault diagnosis" ?

This paper proposes a novel domain adaptation method for rolling bearing fault diagnosis based on deep learning techniques. The experimental results of this study suggest the proposed domain adaptation method offers a new and promising tool for intelligent fault diagnosis. 

The two issues mentioned above will be focused on in further research, as well as the optimization of the hyper-parameters in the proposed method. 

The principal component analysis (PCA) is first adopted to reduce the dimensionality of the feature data to 50 and suppress signal noise. 

Since the network training process is implemented off-line, the longest average computing time of 865.5 seconds for 2000 epochs in this case is still acceptable in the proposed fault diagnosis framework. 

If the enhanced experimental settings of the proposed method is used regardless of the off-line computational burden for training, higher classification accuracy can also be achieved. 

Rolling element bearings are critical components in heavy-duty machineries, manufacturing systems etc. and have been widely applied in modern industries. 

As high as 99.17% cross-domain testing accuracy is obtained with the default experimental setting, and up to 99.76% accuracy can be achieved using the enhanced network configuration. 

In general, the proposed deep learning method combines two architectural ideas for better feature extraction of vibration signals, i.e. CNN and fullyconnected layer. 

The vibration signals used in this study were collected from the drive end of the motor in the test rig on four different health conditions: 1) normal condition (H); 2) outer race fault (OF); 3) inner race fault (IF); and 4) ball fault (BF). 

In the latter case, 95% and higher testing accuracies were achieved in [62–64] where 4 bearing health conditions or fewer were considered. 

The introduced punishment factor α, which determines the domain adaptation strength, may also have influence on the diagnosis accuracy. 

As shown in Figure 1, the domain shift problem is expected to be solved by jointly minimizing the classification error and the distribution discrepancy between the source and target domains. 

As FN becomes larger, the average testing accuracy increases stably, and it reaches 99.75% when 50 filters are adopted in each layer.