Author
YuDejie
Bio: YuDejie is an academic researcher from Hunan University. The author has contributed to research in topics: Entropy (energy dispersal) & Wavelet packet decomposition. The author has an hindex of 1, co-authored 1 publications receiving 417 citations.
Papers
More filters
TL;DR: The analysis results from roller bearing signals with inner-race and out-race faults show that the diagnosis approach based on neural network by using EMD to extract the energy of different frequency bands as features can identify roller bearing fault patterns accurately and effectively and is superior to that based on wavelet packet decomposition and reconstruction.
481 citations
Cited by
More filters
TL;DR: This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics.
1,410 citations
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
TL;DR: In this article, a mathematical analysis to select the most significant intrinsic mode functions (IMFs) is presented, and the chosen features are used to train an artificial neural network (ANN) to classify bearing defects.
594 citations
TL;DR: An effective and reliable deep learning method known as stacked denoising autoencoder (SDA), which is shown to be suitable for certain health state identifications for signals containing ambient noise and working condition fluctuations, is investigated.
591 citations
TL;DR: This work proposed a novel deep neural network model with domain adaptation for fault diagnosis, which can find the solution to this problem by adapting the classifier or the regression model trained in a source domain for use in a different but related target domain.
Abstract: In recent years, machine learning techniques have been widely used to solve many problems for fault diagnosis. However, in many real-world fault diagnosis applications, the distribution of the source domain data (on which the model is trained) is different from the distribution of the target domain data (where the learned model is actually deployed), which leads to performance degradation. In this paper, we introduce domain adaptation, which can find the solution to this problem by adapting the classifier or the regression model trained in a source domain for use in a different but related target domain. In particular, we proposed a novel deep neural network model with domain adaptation for fault diagnosis. Two main contributions are concluded by comparing to the previous works: first, the proposed model can utilize domain adaptation meanwhile strengthening the representative information of the original data, so that a high classification accuracy in the target domain can be achieved, and second, we proposed several strategies to explore the optimal hyperparameters of the model. Experimental results, on several real-world datasets, demonstrate the effectiveness and the reliability of both the proposed model and the exploring strategies for the parameters.
527 citations