Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application

Han, Te; Liu, Chao; Wei-guo, Yang; Jiang, Dongxiang

doi:10.1016/j.isatra.2019.08.012

Cited by 410 publications

(201 citation statements)

References 40 publications

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“…Here, the MMD regularizer is used to punish the feature difference between training data and test data. By extending the marginal distribution adaptive (MDA) to the joint distribution adaptive (JDA), Han et al [17] proposed a new fault diagnosis method that can adapt the conditional distribution of unmarked target data by using the discriminant structure in source domain. Through a more accurate distribution matching, this method can get better diagnosis performance.…”

Section: Preliminary Workmentioning

confidence: 99%

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

et al. 2020

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In recent years, machine learning techniques have been proven to be a promising tool for early fault detection of rolling bearings. In many actual applications, however, bearing whole-life data are not easy to be historically accumulated, while insufficient data may result in training a detection model that is not good enough. If utilizing the available data under different working conditions to facilitate model training, the data distribution of different bearings are usually quite different, which does not meet the precondition of i n d e p e n d e n t a n d i d e n t i c a l d i s t r i b u t i o n ( i . i . d ) and tends to cause performance reduction. In addition, disturbed by the unstable noise under complex conditions, most of the current detection methods are inclined to raise false alarms, so that the reliability of detection results needs to be improved. To solve these problems, a robust detection method for bearings early fault is proposed based on deep transfer learning. The method includes offline stage and online stage. In the offline stage, by introducing a deep auto-encoder network with domain adaptation, the distribution inconsistency of normal state data among different bearings can be weakened, then the common feature representation of the normal state is obtained. With the extracted common features, a new state assessment method based on the robust deep auto-encoder network is proposed to evaluate the boundary between normal state and early fault state in the low-rank feature space. By training a support vector machine classifier, the detection model is established. In the online stage, along with the data batch arriving sequentially, the features of target bearing are extracted using the common representation learnt in the offline stage, and online detection is conducted by feeding them into the SVM model. Experimental results on IEEE PHM Challenge 2012 bearing dataset and XJTU-SY dataset show that the proposed approach outperforms several state-of-the-art detection methods in terms of detection accuracy and false alarm rate.

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Section: Preliminary Workmentioning

confidence: 99%

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

et al. 2020

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“…(3) Generally, the deep learning techniques, including CNN, SIFT + CNN, LSTM, and CDFL, perform better than the traditional methods. It can be explained by the fact that the deep learning methods, with stronger feature learning and representation capacity, always present a superior performance to the methods that require manual feature extraction [33]. (4) e major shortcoming of the proposal is the computational cost.…”

Section: Shock and Vibrationmentioning

confidence: 99%

Fault Diagnosis of Induction Motors Using Recurrence Quantification Analysis and LSTM with Weighted BN

Xiao

Huang

Qin

et al. 2019

Shock and Vibration

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Motor fault diagnosis has gained much attention from academic research and industry to guarantee motor reliability. Generally, there exist two major approaches in the feature engineering for motor fault diagnosis: (1) traditional feature learning, which heavily depends on manual feature extraction, is often unable to discover the important underlying representations of faulty motors; (2) state-of-the-art deep learning techniques, which have somewhat improved diagnostic performance, while the intrinsic characteristics of black box and the lack of domain expertise have limited the further improvement. To cover those shortcomings, in this paper, two manual feature learning approaches are embedded into a deep learning algorithm, and thus, a novel fault diagnosis framework is proposed for three-phase induction motors with a hybrid feature learning method, which combines empirical statistical parameters, recurrence quantification analysis (RQA) and long short-term memory (LSTM) neural network. In addition, weighted batch normalization (BN), a modification of BN, is designed to evaluate the contributions of the three feature learning approaches. The proposed method was experimentally demonstrated by carrying out the tests of 8 induction motors with 8 different faulty types. Results show that compared with other popular intelligent diagnosis methods, the proposed method achieves the highest diagnostic accuracy in both the original dataset and the noised dataset. It also verifies that RQA can play a bigger role in real-world applications for its excellent performance in dealing with the noised signals.

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“…Recently, transfer learning, mainly the domain adaptation branch, has been applied to various fields and achieved excellent results. It has also gained extensive attention in the field of fault monitoring and diagnosis [19][20][21][22]. In powerful deep structure applications, preprocess is not required.…”

Section: Introductionmentioning

confidence: 99%

Generative Transfer Learning for Intelligent Fault Diagnosis of the Wind Turbine Gearbox

Guo

Zhang

et al. 2020

Sensors

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Intelligent fault diagnosis algorithms based on machine learning and deep learning techniques have been widely used in industrial applications and have obtained much attention as well as achievements. In real industrial applications, working loads of machines are always changing. Hence, directly applying the traditional algorithms will cause significant degradation of performance with changing conditions. In this paper, a novel domain adaptation method, named generative transfer learning (GTL), is proposed to tackle this problem. First, raw datasets were transformed to time–frequency domain based on short-time Fourier transformation. A domain discriminator was then built to distinguish whether the data came from the source or the target domain. A target domain classification model was finally acquired by the feature extractor and the classifier. Experiments were carried out for the fault diagnosis of a wind turbine gearbox. The t-distributed stochastic neighbor embedding technique was used to visualize the output features for checking the effectiveness of the proposed algorithm in feature extraction. The results showed that the proposed GTL could improve classification rates under various working loads. Compared with other domain adaptation algorithms, the proposed method exhibited not only higher accuracy but faster convergence speed as well.

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Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application

Cited by 410 publications

References 40 publications

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

Fault Diagnosis of Induction Motors Using Recurrence Quantification Analysis and LSTM with Weighted BN

Generative Transfer Learning for Intelligent Fault Diagnosis of the Wind Turbine Gearbox

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