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Waveform of vibration signals from bearing under four conditions: (a) normal, (b) inner race fault, (c) ball fault, and (d) outer race fault.
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Dimensionality reduction is a crucial task in machinery fault diagnosis. Recently, as a popular dimensional reduction technology, manifold learning has been successfully used in many fields. However, most of these technologies are not suitable for the task, because they are unsupervised in nature and fail to discover the discriminate structure in t...
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Dimensionality reduction techniques reduce the dimensions of the input space to facilitate the exploration of structures in high-dimensional data. Two general dimensionality reduction approaches exist: manifold learning and projection. Manifold-learning methods attempt to find a sub-space in which the high-dimensional distances can be preserved.
In this paper, we propose a sparseness constraint NMF method, named graph regularized matrix factorization with sparse coding (GRNMF-SC). By combining manifold learning and sparse coding techniques together, GRNMF-SC can efficiently extract the basic vectors from the data space, which preserves the intrinsic manifold structure and also the local fe...
Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral imagery (HS...
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Dimensionality reduction techniques reduce the dimensions of the input space to facilitate the exploration of structures in high-dimensional data. Two general dimensionality reduction approaches exist: manifold learning and projection. Manifold learning methods attempt to find sub-spaces in which the high-dimensional distances are preserved.
Detection of defects on patterned semiconductor wafers is a critical step in wafer production. Many inspection methods and apparatus have been developed for this purpose. We recently presented an anomaly detection approach based on geometric manifold learning techniques. This approach is data-driven, with the separation of the anomaly from the back...
Citations
... The kernel-trick has been successfully utilized to extend linear feature extraction methods for nonlinear cases, such as performing a linear method in a higher dimensional kernel feature space by a kernel function. Kernel PCA (KPCA) is the kernel extension of PCA [4]. KPCA provides superior performances for nonlinear fault classification than its linear form (PCA). ...
A semi-supervised Laplacian Eigenmaps algorithm for machine fault detection is proposed. The purpose of the algorithm is to efficiently extract the manifold geometric characteristics of nonlinear vibration signal samples, and to determine fault classification of operating equipment so that the accuracy of fault detection can be improved. The data acquisition and pre-processing of the vibration signal is firstly implemented from monitoring equipment, then hybrid domain feature is obtained, and the initial sample set can be built. This is followed by implementing the semi-supervised Laplacian Eigenmaps algorithm so that the sensitive nature characteristics of manifold can be obtained from the device initial sample set. In order to establish the intelligent diagnostic model, the Least square Support vector machine (LS-SVM) is then adopted, which fault diagnosis and decisions can be achieved in the feature space of the low-dimensional manifold. The experiment results of using the IRIS data, gearbox and compressor fault data show the proposed method has more advantage when compared with the PCA and Laplacian Eigenmaps on improving the accuracy of fault detection.
In the feature extraction of mechanical fault detection field, manifold learning is one of the effective nonlinear techniques. In this paper, aiming for the situations of noise sensitivity to manifold learning algorithms, an improved Laplacian Eigenmap (I-LapEig) algorithm is proposed and applied to the process of fault feature extraction. The new method takes advantage of local principal component analysis to eliminate the influence of noise points by reconstructing the neighborhood relation amongst the samples, and maintain the global intrinsic manifold structure, which enhances the performance of the feature extraction. To determine the parameters of I-LapEig algorithm, an adaptive neighborhood choose approach is presented. The K-nearest neighbor classifier is also adopted to implement feature classification and recognition. The experimental results on S-curve, rotor bed data, and compressor fault data show that the new method can effectively improve the performance of noise reduction in the feature extraction process when compared with the conventional local linear embedding and Laplacian Eigenmaps.
