The Experts below are selected from a list of 27 Experts worldwide ranked by ideXlab platform
Lei Wang - One of the best experts on this subject based on the ideXlab platform.
-
bearing fault diagnosis using a whale optimization algorithm optimized orthogonal matching pursuit with a combined time frequency Atom Dictionary
Mechanical Systems and Signal Processing, 2018Co-Authors: Xin Zhang, Qiang Miao, Lei WangAbstract:Abstract Condition monitoring and fault diagnosis of rolling element bearings are significant to guarantee the reliability and functionality of a mechanical system, production efficiency, and plant safety. However, this is almost invariably a formidable challenge because the fault features are often buried by strong background noises and other unstable interference components. To satisfactorily extract the bearing fault features, a whale optimization algorithm (WOA)-optimized orthogonal matching pursuit (OMP) with a combined time–frequency Atom Dictionary is proposed in this paper. Firstly, a combined time–frequency Atom Dictionary whose Atom is a combination of Fourier Dictionary Atom and impact time–frequency Dictionary Atom is designed according to the properties of bearing fault vibration signal. Furthermore, to improve the efficiency and accuracy of signal sparse representation, the WOA is introduced into the OMP algorithm to optimize the Atom parameters for best approximating the original signal with the Dictionary Atoms. The proposed method is validated through analyzing the bearing fault simulation signal and the real vibration signals collected from an experimental bearing and a wheelset bearing of high-speed trains. The comparisons with the respect to the state of the art in the field are illustrated in detail, which highlight the advantages of the proposed method.
Xin Zhang - One of the best experts on this subject based on the ideXlab platform.
-
bearing fault diagnosis using a whale optimization algorithm optimized orthogonal matching pursuit with a combined time frequency Atom Dictionary
Mechanical Systems and Signal Processing, 2018Co-Authors: Xin Zhang, Qiang Miao, Lei WangAbstract:Abstract Condition monitoring and fault diagnosis of rolling element bearings are significant to guarantee the reliability and functionality of a mechanical system, production efficiency, and plant safety. However, this is almost invariably a formidable challenge because the fault features are often buried by strong background noises and other unstable interference components. To satisfactorily extract the bearing fault features, a whale optimization algorithm (WOA)-optimized orthogonal matching pursuit (OMP) with a combined time–frequency Atom Dictionary is proposed in this paper. Firstly, a combined time–frequency Atom Dictionary whose Atom is a combination of Fourier Dictionary Atom and impact time–frequency Dictionary Atom is designed according to the properties of bearing fault vibration signal. Furthermore, to improve the efficiency and accuracy of signal sparse representation, the WOA is introduced into the OMP algorithm to optimize the Atom parameters for best approximating the original signal with the Dictionary Atoms. The proposed method is validated through analyzing the bearing fault simulation signal and the real vibration signals collected from an experimental bearing and a wheelset bearing of high-speed trains. The comparisons with the respect to the state of the art in the field are illustrated in detail, which highlight the advantages of the proposed method.
Jian Yang - One of the best experts on this subject based on the ideXlab platform.
-
Learning a structure adaptive Dictionary for sparse representation based classification
Neurocomputing, 2016Co-Authors: Heyou Chang, Meng Yang, Jian YangAbstract:Dictionary learning (DL), playing a key role in the success of sparse representation, has led to state-of-the-art results in image classification tasks. Among the existing supervised Dictionary learning methods, the label of each Dictionary Atom is predefined and fixed, i.e., each Dictionary Atom is either associated to all classes or assigned to a single class. In this paper, we propose a structure adaptive Dictionary learning (SADL) method to learn the relationship between Dictionary Atoms and classes, which is indicated by a binary association matrix and jointly optimized with the Dictionary. The binary association matrix can not only represent class-specific Dictionary Atoms, but also hyper-class Dictionary Atoms shared by multiple classes. Furthermore, discrimination is explored by introducing Fisher criterion on coding coefficient and reducing between-class Dictionary coherence. The extensive experimental results have shown that the proposed SADL can achieve better performance than previous supervised Dictionary learning methods on various classification databases.
Li Zhang - One of the best experts on this subject based on the ideXlab platform.
-
structured latent label consistent Dictionary learning for salient machine faults representation based robust classification
IEEE Transactions on Industrial Informatics, 2017Co-Authors: Zhao Zhang, Weiming Jiang, Mingbo Zhao, Li ZhangAbstract:This paper investigates the salient machine faults representation-based classification issue by Dictionary learning. A novel structured latent label consistent Dictionary learning (LLC-DL) model is proposed for joint discriminative salient representation and classification. Our LLC-DL deals with the tasks by solving one objective function that aims to minimize the structured reconstruction error, structured discriminative sparse-code error and classification error simultaneously. Also, LLC-DL decomposes given signals into a sparse reconstruction part over structured latent weighted discriminative Dictionary, a salient feature extraction part and an error part fitting noise. Specifically, the Dictionary is learnt Atom by Atom, where each Dictionary Atom is learnt with a latent vector that reduces the disturbance between interclass Atoms. The structured coding coefficients are calculated via minimizing the reconstruction error and discriminative sparse code error simultaneously. The salient representations are learnt by embedding signals onto a projection and a robust linear classifier is then trained over the learned salient features directly so that features can be ensured to be optimal for classification, where robust l 2 , 1-norm imposed on the classifier can make the prediction results more accurate. By including a salient feature extraction term, the classification approach of LLC-DL is very efficient, since there is no need to involve an extra time-consuming sparse reconstruction process with the well-trained Dictionary for each test signal. Extensive simulations versify the effectiveness of our algorithm.
Qiang Miao - One of the best experts on this subject based on the ideXlab platform.
-
bearing fault diagnosis using a whale optimization algorithm optimized orthogonal matching pursuit with a combined time frequency Atom Dictionary
Mechanical Systems and Signal Processing, 2018Co-Authors: Xin Zhang, Qiang Miao, Lei WangAbstract:Abstract Condition monitoring and fault diagnosis of rolling element bearings are significant to guarantee the reliability and functionality of a mechanical system, production efficiency, and plant safety. However, this is almost invariably a formidable challenge because the fault features are often buried by strong background noises and other unstable interference components. To satisfactorily extract the bearing fault features, a whale optimization algorithm (WOA)-optimized orthogonal matching pursuit (OMP) with a combined time–frequency Atom Dictionary is proposed in this paper. Firstly, a combined time–frequency Atom Dictionary whose Atom is a combination of Fourier Dictionary Atom and impact time–frequency Dictionary Atom is designed according to the properties of bearing fault vibration signal. Furthermore, to improve the efficiency and accuracy of signal sparse representation, the WOA is introduced into the OMP algorithm to optimize the Atom parameters for best approximating the original signal with the Dictionary Atoms. The proposed method is validated through analyzing the bearing fault simulation signal and the real vibration signals collected from an experimental bearing and a wheelset bearing of high-speed trains. The comparisons with the respect to the state of the art in the field are illustrated in detail, which highlight the advantages of the proposed method.