Gang Zhang

Bio: Gang Zhang is an academic researcher from Naval University of Engineering. The author has contributed to research in topics: Wavelet & Deep learning. The author has an hindex of 1, co-authored 11 publications receiving 10 citations.

Rotating Machinery Remaining Useful Life Prediction Scheme Using Deep-Learning-Based Health Indicator and a New RVM

Abstract: Remaining useful life (RUL) prediction plays a significant role in developing the condition-based maintenance and improving the reliability and safety of machines. This paper proposes a remaining useful life prediction scheme combining deep-learning-based health indicator and a new relevance vector machine. First, both one-dimensional time-series information and two-dimensional time-frequency maps are input into a hybrid deep-learning structure network consisting of convolutional neural network (CNN) and long short-term memory network (LSTM) to construct health indicator (HI). Then, the prediction results and confidence interval are calculated by a new RVM enhanced by a polynomial regression model. The proposed method is verified by the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed method in improving the prediction accuracy and analyzing the prediction uncertainty.

Rolling bearing fault diagnosis method based on dual-tree complex wavelet pack manifold domain noise reduction

Abstract: The invention relates to a rolling bearing fault diagnosis method based on dual-tree complex wavelet pack manifold domain noise reduction The rolling bearing fault diagnosis method based on the dual-tree complex wavelet pack manifold domain noise reduction comprises steps of using an accelerated speed sensor to collect a vibration signal of the rolling bearing, performing dual-tree complex wavelet pack decomposition on the vibration signal, maintaining wavelet pack coefficients of first two nodes, performing threshold noise reduction on wavelet coefficients of the rest nodes, performing single branch reconstruction on the wavelet pack coefficient of each node to perform a high dimensional signal space, using a t distribution random neighbor embedding method to extract low a dimensional manifold, performing inverse reconstruction on the low-dimensional manifold to obtain a high-dimensional space main manifold, obtaining a signal after noise reduction, performing Hilbert envelope demodulation on the signal after noise reduction to obtain an envelope frequency spectrum of the vibration signal, and realizing fault diagnosis of the rolling bearing according to an inner ring fault characteristic frequency and an outer ring fault characteristic frequency of the rolling bearing, a rolling body fault characteristic frequency and a retainer fault characteristic frequency

Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding

Abstract: The dimension reduction methods have been proved powerful and practical to extract latent features in the signal for process monitoring. A linear dimension reduction method called nonlocal orthogonal preserving embedding (NLOPE) and its nonlinear form named nonlocal kernel orthogonal preserving embedding (NLKOPE) are proposed and applied for condition monitoring and fault detection. Different from kernel orthogonal neighborhood preserving embedding (KONPE) and kernel principal component analysis (KPCA), the NLOPE and NLKOPE models aim at preserving global and local data structures simultaneously by constructing a dual-objective optimization function. In order to adjust the trade-off between global and local data structures, a weighted parameter is introduced to balance the objective function. Compared with KONPE and KPCA, NLKOPE combines both the advantages of KONPE and KPCA, and NLKOPE is also more powerful in extracting potential useful features in nonlinear data set than NLOPE. For the purpose of condition monitoring and fault detection, monitoring statistics are constructed in feature space. Finally, three case studies on the gearbox and bearing test rig are carried out to demonstrate the effectiveness of the proposed nonlinear fault detection method.

A health indicator construction method based on the information entropy of specific frequency band and GGRBM

Abstract: The traditional physics health indicator(PHI) has poor monotonicity and is insensitive to the incipient fault. In order to solve this problem, we propose a virtual health indicator(VHI) construction model, which is based on the information entropy of specific frequency domain and restricted Boltzmann machine. This model consists of two layers: physics health indicator construction layer and features fusion layer. In the physics health indicator construction layer, we propose a method based on the information entropy of specific frequency domain to construct the physics health indicator. In the features fusion layer, the monotonicity metric is used to select parts of the PHIs to form the feature set, which is then fused by the Gaussian-gaussian restricted Boltzmann machine to get a VHI. The experiment results show that the proposed model could make a VHI, which can effectively enhance the monotonicity of the degradation assessment curve.

Nonlinear global-local structure model and its application for condition monitoring and fault detection

Abstract: The dimension reduction methods have been proved powerful and practical to extract latent features hidden in the signal for process monitoring A novel nonlinear dimension reduction method called kernel orthogonal global-local preserving projections (KOGLPP) is proposed and applied for condition monitoring and fault detection To overcome the shortcomings of kernel locality preserving projections (KLPP) and kernel principal component analysis (KPCA), the KOGLPP model aims at preserving the global and local data structures simultaneously by constructing a dual-objective optimization function, and a tuning parameter is introduced to adjust the trade-off between the global and local data structures For the purpose of condition monitoring and fault detection, monitoring statistics are constructed in low dimensional feature space As KOGLPP combines the advantages of both KPCA and KLPP, KOGLPP is also more powerful in extracting potential useful data characteristics Finally, the effectiveness of the proposed nonlinear dimension reduction method is evaluated experimentally on a numerical example and a bearing test-rig The results indicate its potential applications as an effective and reliable tool for condition monitoring and fault detection

RenyiBS: Renyi entropy basis selection from wavelet packet decomposition tree for phonocardiogram classification

Abstract: Wavelet packet transform (WPT) is a powerful mathematical tool for analyzing nonlinear biomedical signals, such as phonocardiogram (PCG). WPT decomposes a PCG signal into a full binary tree of details and approximation coefficients. Appropriate nodes of the tree could be selected as a basis for generating features. Motivated by this, we propose the Renyi entropy basis selection (RenyiBS) method. In RenyiBS method, we use the Renyi entropy as an information measure to choose the best basis of the wavelet packet tree of PCG signals for feature selection and classification. The Renyi entropy estimates the spectral complexity of a signal, which is vital for characterizing nonlinear signals such as PCGs. After selecting the best basis, we define features on the coefficients of the selected nodes. Then, we classify PCGs using the support vector machine (SVM) classifier. In the simulation, we examine a set of 820 heart sound cycles, including normal heart sounds and three types of heart murmurs. The three murmurs examined include aortic regurgitation, mitral regurgitation, and aortic stenosis. We achieved the promising result of 99.74% accuracy, confirming the ability of Renyi entropy to select an appropriate basis of the wavelet packet tree and extracting the nonlinear behavior of particular heart sounds. Besides, the superiority of our proposed information measure in comparison with other information measures reported before is shown.

Ternary binary fractal wavelet sparse diagnosis method for rolling bearing fault

Abstract: The invention discloses a ternary binary fractal wavelet sparse diagnosis method for rolling bearing faults, and relates to a mechanical fault diagnosis method. The method comprises: carrying out multi-scale iterative decomposition on the vibration acceleration signal by adopting a finite impulse response filter bank to obtain 3.2J-1 wavelet subspaces; and testing the response function of each subspace through the unit pulse function, and reordering each wavelet subspace by calculating the frequency spectrum energy center of gravity of the response function. On each scale, a transition subspace is constructed by adding non-endpoint adjacent subspaces, so that a new ternary binary fractal wavelet frequency-scale division grid is realized. In order to carry out self-adaptive quantitative identification on potential periodic impact fault characteristics in each subspace, a periodic sparsity evaluation index is provided and is used for calculating the specific gravity of characteristic frequency multiplication energy in the signal envelope demodulation amplitude spectrum of each subspace in the total energy of the signal. And the type and the position of the fault can be determined according to the characteristic frequency corresponding to the maximum value of the periodic sparse characteristic index.