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Dongxin Xue

Bio: Dongxin Xue is an academic researcher from Dalian University of Technology. The author has contributed to research in topics: Support vector machine & Feature vector. The author has an hindex of 3, co-authored 3 publications receiving 164 citations.

Papers
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Journal ArticleDOI
TL;DR: The experimental results indicate that HE can depict the characteristics of the bearing vibration signal more accurately and more completely than MSE, and the proposed approach based on HE can identify various bearing conditions effectively and accurately and is superior to that based on MSE.

143 citations

Journal Article
TL;DR: In this article, an early fault diagnosis method for roller bearings is proposed, based on empirical mode decomposition (EMD) and correlation coefficient (the normalized value of the cross-correlation function at the zero-lag point).
Abstract: In this paper, an incipient fault diagnosis method for roller bearings is proposed, based on empirical mode decomposition (EMD) and correlation coefficient (the normalized value of the cross-correlation function at the zero-lag point). The high frequency resonance phenomena begin to emerge as a defect forms gradually in the bearings. Therefore the high frequency content is sensitive to the change of the bearing conditions. Based on this, the EMD method is firstly applied to the bearing vibration signals to obtain some intrinsic mode functions (IMF) which contain different frequency bands from high to low. The first IMF of the signal to be detected, representing the high frequency band, is then selected to calculate the correlation coefficient between its frequency-domain signal and that of normal state. The correlation coefficient can demonstrate the fault evolution process and thus can detect an early fault. Finally the early faulty signals are analyzed by using the envelope analysis and the location of the fault is identified. The experimental results verify the effectiveness of the proposed method.

23 citations

Journal ArticleDOI
TL;DR: The experimental results indicate that the proposed approach based on IMF envelope SampEn can identify different fault types as well as levels of severity effectively and is superior to thatbased on IMF SampEn.
Abstract: In this paper, a new fault feature extraction method based on Intrinsic Mode Function (IMF) envelope sample entropy (SampEn) is proposed for rolling bearings fault diagnosis. First, the Empirical Mode Decomposition (EMD) method is utilized to decompose the vibration signals self-adaptively into a number of IMFs which represent different frequency bands from high to low. Second, the IMF envelope signals are used to highlight the fault-induced information in a structurally simpler and physically more meaningful way than the original signals. Thus, the shortcoming of SampEn assigning high values to uncorrelated random signals can be overcome. Finally, the IMF envelope SampEn serve as a fault feature vector to be input into multi-class classifier of Support Vector Machine (SVM) for identification of different bearing conditions. The experimental results indicate that the proposed approach based on IMF envelope SampEn can identify different fault types as well as levels of severity effectively and is superior to that based on IMF SampEn.

18 citations


Cited by
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Journal ArticleDOI
TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.

1,173 citations

Journal ArticleDOI
TL;DR: In this paper, a hybrid model for fault detection and classification of motor bearing is presented, where the permutation entropy (PE) of the vibration signal is calculated to detect the malfunctions of the bearing.

453 citations

Journal ArticleDOI
TL;DR: A new TCNN with the depth of 51 convolutional layers is proposed for fault diagnosis of ResNet-50 and achieves state-of-the-art results, which demonstrates that TCNN(ResNet- 50) outperforms other DL models and traditional methods.
Abstract: With the rapid development of smart manufacturing, data-driven fault diagnosis has attracted increasing attentions. As one of the most popular methods applied in fault diagnosis, deep learning (DL) has achieved remarkable results. However, due to the fact that the volume of labeled samples is small in fault diagnosis, the depths of DL models for fault diagnosis are shallow compared with convolutional neural network in other areas (including ImageNet), which limits their final prediction accuracies. In this research, a new TCNN(ResNet-50) with the depth of 51 convolutional layers is proposed for fault diagnosis. By combining with transfer learning, TCNN(ResNet-50) applies ResNet-50 trained on ImageNet as feature extractor for fault diagnosis. Firstly, a signal-to-image method is developed to convert time-domain fault signals to RGB images format as the input datatype of ResNet-50. Then, a new structure of TCNN(ResNet-50) is proposed. Finally, the proposed TCNN(ResNet-50) has been tested on three datasets, including bearing damage dataset provided by KAT datacenter, motor bearing dataset provided by Case Western Reserve University (CWRU) and self-priming centrifugal pump dataset. It achieved state-of-the-art results. The prediction accuracies of TCNN(ResNet-50) are as high as 98.95% ± 0.0074, 99.99% ± 0 and 99.20% ± 0, which demonstrates that TCNN(ResNet-50) outperforms other DL models and traditional methods.

319 citations

Journal ArticleDOI
TL;DR: Experimental results show that the proposed fault classification algorithm achieves high diagnosis accuracy for different working conditions of rolling bearing and outperforms some traditional methods both mentioned in this paper and published in other literature.

316 citations

Journal ArticleDOI
TL;DR: The rolling bearing fault diagnosis method based on LMD, MPE, LS and ISVM-BT is proposed and the experimental results indicate the proposed method is effective in identifying the different categories of rolling bearings.

221 citations