Deep Model Based Domain Adaptation for Fault Diagnosis

Lu, Weining; Liang, Bin; Cheng, Yu; Meng, Deshan; Yang, Jun; Zhang, Tao

doi:10.1109/tie.2016.2627020

Cited by 608 publications

(277 citation statements)

References 30 publications

Supporting

Mentioning

246

Contrasting

Order By: Relevance

“…As proposed in recent studies [60], a two-stage learning method for domain adaptation can be adopted, where the integrated objective in Equation 11is separated. Specifically, after initialization with the labeled source domain data, the generic network can be further trained with both the source and target domain data by minimizing the distribution discrepancies through layers (Equation (10)).…”

Section: (5) Two-stage Learningmentioning

confidence: 99%

Multi-Layer domain adaptation method for rolling bearing fault diagnosis

et al. 2019

View full text Add to dashboard Cite

In the past years, data-driven approaches such as deep learning have been widely applied on machinery signal processing to develop intelligent fault diagnosis systems. In real-world applications, domain shift problem usually occurs where the distribution of the labeled training data, denoted as source domain, is different from that of the unlabeled testing data, known as target domain. That results in serious diagnosis performance degradation. This paper proposes a novel domain adaptation method for rolling bearing fault diagnosis based on deep learning techniques. A deep convolutional neural network is used as the main architecture. The multi-kernel maximum mean discrepancies (MMD) between the two domains in multiple layers are minimized to adapt the learned representations from supervised learning in the source domain to be applied in the target domain. The domain-invariant features can be efficiently extracted in this way, and the cross-domain testing performance can be significantly improved. Experiments on a popular rolling bearing dataset are carried out to validate the effectiveness of the domain adaptation approach, and the diagnosis performance is extensively evaluated in different scenarios. Comparisons with other approaches and related works on the same dataset demonstrate the superiority of the proposed method. The experimental results of this study suggest the proposed domain adaptation method offers a new and promising tool for intelligent fault diagnosis.

show abstract

Section: (5) Two-stage Learningmentioning

confidence: 99%

Multi-Layer domain adaptation method for rolling bearing fault diagnosis

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In [15], Xie, Zhang, Duan et al proposed feature extraction and fusion using transfer component analysis. In [16], a neural network is trained such as the latent space minimises the Maximum Mean Discrepancy [17] between the features of the source and the target while maximising the detection accuracy of a fault classifier. Surprisingly, very few works have applied similar approaches to fleets of assets.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Fault Detection in Varying Operating Conditions

Michau

Fink

2019

2019 IEEE International Conference on Prognostics and Health Management (ICPHM)

View full text Add to dashboard Cite

Training data-driven approaches for complex industrial system health monitoring is challenging. When data on faulty conditions are rare or not available, the training has to be performed in a unsupervised manner. In addition, when the observation period, used for training, is kept short, to be able to monitor the system in its early life, the training data might not be representative of all the system normal operating conditions. In this paper, we propose five approaches to perform fault detection in such context. Two approaches rely on the data from the unit to be monitored only: the baseline is trained on the early life of the unit. An incremental learning procedure tries to learn new operating conditions as they arise. Three other approaches take advantage of data from other similar units within a fleet. In two cases, units are directly compared to each other with similarity measures, and the data from similar units are combined in the training set. We propose, in the third case, a new deep-learning methodology to perform, first, a feature alignment of different units with an Unsupervised Feature Alignment Network (UFAN). Then, features of both units are combined in the training set of the fault detection neural network. The approaches are tested on a fleet comprising 112 units, observed over one year of data. All approaches proposed here are an improvement to the baseline, trained with two months of data only. As units in the fleet are found to be very dissimilar, the new architecture UFAN, that aligns units in the feature space, is outperforming others.

show abstract

“…The second tool enabled features to be visualized at each layer of a DNN using regularized optimization in image space. Later, Lu et al [33] presented a novel deep NN model with domain adaptation for fault diagnosis. Two main conclusions were reached by comparisons with previous work: first, the proposed model can use domain adaptation while strengthening the representative information of the original data to achieve high classification accuracy in the target domain; second, several strategies were addressed to investigate the optimal hyperparameters of the model.…”

Section: Deep Neural Network (Dnns)mentioning

confidence: 99%

“…In addition, we have provided an explanatory tree diagram of this study (see Figure 1), and the organized literature is listed in Table 1. DL (51): RNN [4][5][6][7][8][9][10][11][12][13][14], CNN [15][16][17][18][19][20][21][22][23][24][25][26][27][28], DNN [29][30][31][32][33], RBM [34][35][36], others [1][2][3][37][38][39][40][41][42][43][44][45][46][47][48][49]…”

Section: Brief Introductionmentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

View full text Add to dashboard Cite

This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.Keywords: artificial intelligence; smart machine tools; learning algorithms; intelligent manufacturing; fault diagnosis and prognosis Brief IntroductionWe believe that a new epoch of the "Industrial Internet of Things (IIoT) plus artificial intelligence (AI)", characterized by big machinery data, data-driven techniques, ubiquitous networks, mass innovation, automatic intelligence, cross-border integration, and shared services, has arrived [1-3]. The fast development and combination of new AI and energy technologies, for materials, bioscience, the Internet, and new-generation information exchange, is a fundamental part of this new epoch. This will, in turn, permit game-changing transformation of models, ecosystems, and means in the light of their application to national security, well-being, and the economy. The main objective is a review and summary of recent achievement in data-based techniques, especially for complicated industrial applications, offering reference for further study from both an academic and practical point of view. Yin et al.[1] describes a brief evolutionary overview of data-based techniques over the last two decades. Recent development of modern industrial applications is presented mainly from the perspectives of monitoring and control. Their methodology, based on process measurements and model-data integrated techniques, will be introduced in the next study. Jeschke et al. [2] developed the core system science needed to enable the development of complex IIoT/manufacturing cyber-physical systems (CPS). Moreover, readers can learn the current state of IIoT and the concept of cybermanufacturing from this book. In 2014, Lund et al. [3] described the central issues contributing to, and characterizing, the worldwide and regional growth of the IoT. Besides, researchers can utilize the trend analysis of IoT their region markets in the future.There are many AI algorithms for machine health monitoring and other machine tool applications: The second-order recurrent neural networks (RNN) method for the learning and extraction of finite

show abstract

Deep Model Based Domain Adaptation for Fault Diagnosis

Cited by 608 publications

References 30 publications

Multi-Layer domain adaptation method for rolling bearing fault diagnosis

Multi-Layer domain adaptation method for rolling bearing fault diagnosis

Unsupervised Fault Detection in Varying Operating Conditions

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

Contact Info

Product

Resources

About