Multi-block statistics local kernel principal component analysis algorithm and its application in nonlinear process fault detection

RUL prediction for rolling bearings based on Convolutional Autoencoder and status degradation model

Deep subclass reconstruction network for fault diagnosis of rotating machinery under various operating conditions

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.

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

In this paper, the isogeometric method is developed to study mechanical buckling behavior of nanocomposite plates reinforced by graphene sheets with temperature-dependent (TD) material properties in thermal environment. The plate is separately subjected to in-plane uniaxial, biaxial and shear loadings. It is assumed that the plate has different number of layers. By considering different volume fraction for each layer of graphene sheets, different functionally graded (FG) patterns of graphene sheets may be achieved. Furthermore, in some cases, it is considered that more than one FG patterns exist along the plate thickness. The energy statement of the plate is obtained using a logarithmic higher-order shear deformation theory (HSDT). Then, the isogeometric method is used to establish the desired eigenvalue problem. The comparison and convergence studies are presented for a wide range of numerical examples in all considered cases to show the correctness and ability of the solution. Afterwards, by presenting a set of numerical examples, the effects of plate significant parameters on the critical buckling load of the plate are examined. It is shown that the highest critical buckling loads occur when the plate has the minimum number of layers. 

On the mechanical buckling analysis of FG-GRC laminated plates with temperature-dependent material properties using isogeometric approach

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.

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

Random vibration study of functionally graded porous curved beams with elastically restrained ends

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

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

In this study, a simple and unified process is established for transient vibration analysis of functionally graded material (FGM) sandwich plates in thermal environment. The temperature field, considered constant in the plane, is distributed along the thickness with uniform, linear and nonlinear profiles. For the material properties, both temperature and position dependence are taken into account. A further refined zigzag plate theory accounting for partitioned transverse displacements and piecewise-continuous in-plane displacements is developed within the framework of Hamilton’s principle including thermal effects. Appropriately and simplicity representation of the deformation states is provided in the governing equations. A spectral analysis technique, namely, method of reverberation ray matrix (MRRM), is employed to calculate the transient vibration responses of FGM sandwich plates with general boundary conditions and arbitrary external loadings. The artificial spring technology and the equivalent wave source vector are introduced to improve the numerical stability and parametric adjustability of MRRM. The accuracy, flexibility and efficiency of the proposed process are discussed using many numerical examples. On this basis, the effects of the boundary parameters, FGM gradient index, core-to-facesheet thickness ratio, thermal properties and external loadings on the transient vibration behaviors of FGM sandwich plates are thoroughly investigated.

A Unified Transient Vibration Analysis of FGM Sandwich Plates in Thermal Environment Based on a Further Refined Zigzag Plate Theory

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.

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Weige Liang

Papers

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

Random vibration study of functionally graded porous curved beams with elastically restrained ends

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

A Unified Transient Vibration Analysis of FGM Sandwich Plates in Thermal Environment Based on a Further Refined Zigzag Plate Theory

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