Local Tangent Space Alignment Algorithm Research Articles

Adaptive self-attention LSTM for RUL prediction of lithium-ion batteries

To achieve an accurate remaining useful life (RUL) prediction for lithium-ion batteries (LIBs), this study proposes an adaptive self-attention long short-term memory (SA-LSTM) prediction model. The innovations of the designed prediction model include the following. (1) It features an optimized local tangent space alignment algorithm, which allows the extraction of an indirect health indicator (HI) that can precisely describe battery degeneration from charge data. The extracted HI exhibits a high correlation with the standard capacity, thus facilitating RUL estimation. (2) By introducing a masked multi-head self-attention module into the time-series prediction model based on LSTM, critical information in the sequences is captured and the prediction performance is improved. (3) An online self-tuning mechanism for the weights and biases of neural networks is designed to correct cumulative estimation errors in long-term predictions and reduce the effects of local fluctuations and regeneration. The proposed prediction model enables the HI values in future cycles to be iteratively estimated using the one-step-ahead method, and the RUL can be forecast once the predicted signal falls. Experimental results indicate the effectiveness and superiority of the proposed prediction method.

Improved Multi-Bandwidth Mode Manifold for Enhanced Bearing Fault Diagnosis

Variational mode decomposition (VMD) has been proved to be useful for extraction of fault-induced transients of rolling bearings. Multi-bandwidth mode manifold (Triple M, TM) is one variation of the VMD, which units multiple fault-related modes with different bandwidths by a nonlinear manifold learning algorithm named local tangent space alignment (LTSA). The merit of the TM method is that the bearing fault-induced transients extracted contain low level of in-band noise without optimization of the VMD parameters. However, the determination of the neighborhood size of the LTSA is time-consuming, and the extracted fault-induced transients may have the problem of asymmetry in the up-and-down direction. This paper aims to improve the efficiency and waveform symmetry of the TM method. Specifically, the multi-bandwidth modes consisting of the fault-related modes with different bandwidths are first obtained by repeating the recycling VMD (RVMD) method with different bandwidth balance parameters. Then, the LTSA algorithm is performed on the multi-bandwidth modes to extract their inherent manifold structure, in which the natural nearest neighbor (Triple N, TN) algorithm is adopted to efficiently and reasonably select the neighbors of each data point in the multi-bandwidth modes. Finally, a weight-based feature compensation strategy is designed to synthesize the low-dimensional manifold features to alleviate the asymmetry problem, resulting in a symmetric TM feature that can represent the real fault transient components. The major contribution of the improved TM method for bearing fault diagnosis is that the pure fault-induced transients are extracted efficiently and are symmetrical as the real. One simulation analysis and two experimental applications in bearing fault diagnosis validate the enhanced performance of the improved TM method over the traditional methods. This research proposes a bearing fault diagnosis method which has the advantages of high efficiency, good waveform symmetry and enhanced in-band noise removal capability.

Blind false data injection attacks in smart grids subject to measurement outliers

ABSTRACT False data injection attacks (FDIAs) can manipulate measurement data from Supervisory Control and Data Acquisition (SCADA) system and threat state estimation in smart grids. Blind FDIAs (BFDIAs) enhance traditional FDIAs, which eliminate the limitation of grasping measurement Jacobian matrix H in advance, but when there are outliers in measurement data, attack performance is degraded. In this paper, improved BFDIAs are proposed. In off-line phase, low-dimensional measurement matrix without outliers calculated by Linear Local Tangent Space Alignment algorithm (LLTSA) is sent into Continuous Deep Belief Network (CDBN) as training data to learn their probability distribution. In on-line phase, real-time low-dimensional measurement matrix with outliers are sent into the trained model as inputs, and outputs are reconstructed by the probability distribution in off-line phase, which eliminates the influence of outliers indirectly. Simulations are implemented on PJM 5-bus and IEEE 14-bus systems to verify the performance of proposed strategy compared with PCA-based BFDIAs.

A novel industrial process monitoring method based on improved local tangent space alignment algorithm

With the rapid development of manufacturing industries and the complexity increment of processes, a large number of streaming data with high-dimensionality and nonlinearity are obtained in actual industrial processes. As a result, it is essential for process monitoring to make dimension reduction. In this paper, a novel process monitoring method combining improved local tangent space alignment (ILTSA) and support victor data description (SVDD) is put forward. Considering the local geometric structures and the correlations among global variables of the original dataset, ILTSA algorithm is proposed to obtain the projection matrix from original space to feature one. Firstly, since the similarities in both spatial and temporal scales of streaming data cannot be neglected, time weighted distance is defined as the measurement of neighborhoods determination in ILTSA. Secondly, the objective function of ILTSA is reconstructed by introducing mutual information (MI) matrix of global variables. Finally, SVDD algorithm is adopted to establish the monitoring model. The corresponding monitoring statistic and control limit are calculated as well. A real hot strip mill process (HMSP) is employed to validate the proposed method and the result demonstrates its efficiency.

HSIC Regularized LTSA

Hilbert-Schmidt Independence Criterion (HSIC) measures statistical independence between two random variables. However, instead of measuring the statistical independence between two random variables directly, HSIC first transforms two random variables into two Reproducing Kernel Hilbert Spaces (RKHS) respectively and then measures the kernelled random variables by using Hilbert-Schmidt (HS) operators between the two RKHS. Since HSIC was first proposed around 2005, HSIC has found wide applications in machine learning. In this paper, a HSIC regularized Local Tangent Space Alignment algorithm (HSIC-LTSA) is proposed. LTSA is a well-known dimensionality reduction algorithm for local homeomorphism preservation. In HSIC-LTSA, behind the objective function of LTSA, HSIC between high-dimensional and dimension-reduced data is added as a regularization term. The proposed HSIC-LTSA has two contributions. First, HSIC-LTSA implements local homeomorphism preservation and global statistical correlation during dimensionality reduction. Secondly, HSIC-LTSA proposes a new way to apply HSIC: HSIC is used as a regularization term to be added to other machine learning algorithms. The experimental results presented in this paper show that HSIC-LTSA can achieve better performance than the original LTSA.

Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery

A new method for extracting the low-dimensional feature automatically with self-organization mapping manifold is proposed for the detection of rotating mechanical nonlinear faults (such as rubbing, pedestal looseness). Under the phase space reconstructed by single vibration signal, the self-organization mapping (SOM) with expectation maximization iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention. After that, the local tangent space alignment algorithm is adopted to compress the high-dimensional phase space into low-dimensional feature space. The proposed method takes advantages of the manifold learning in low-dimensional feature extraction and adaptive neighborhood construction of SOM and can extract intrinsic fault features of interest in two dimensional projection space. To evaluate the performance of the proposed method, the Lorenz system was simulated and rotation machinery with nonlinear faults was obtained for test purposes. Compared with the holospectrum approaches, the results reveal that the proposed method is superior in identifying faults and effective for rotating machinery condition monitoring.

Fusion Algorithm Based on Improved Linear Local Tangent Space Alignment Algorithm

A uncorrelated adaptive discriminant linear local tangent space alignment (UDALLTSA) is proposed based on improved linear local tangent space alignment algorithm. The algorithm uses an adaptive neighborhood selection to select the appropriate neighborhood, and introduces curvature to amend the model, modifies the constraints of the objective function by use inter-class scatter matrix, and constraints on basis vectors to compute the best projection matrix. By comparing the results of the experiments, it shows that after integrating the discriminant information into the algorithm , uncorrelated constraints and adaptive neighborhood selection can well improve the recognition rate and robustness, thus, possessing good noise immunity, and eliminating redundant information of base vectors, make this fusion algorithm a supervised learning algorithm.

Adaptive Discriminant Linear Local Tangent Space Alignment Algorithm on Face Recognition

This paper proposes an adaptive discriminant linear local tangent space alignment algorithm DALLTSA. On the basis of LLTSA algorithm adding adaptive and discriminant gets DALLTSA.DALLTSA not only combines characteristics in DLLTSA that maintain the local geometry and meets the maximum between-class scatter matrix, but also dynamically selects K-neighbor better to reflect the degree of polymerization between samples. Finally, the face recognition experiments based on Gabor [1] filter and DALLTSA shows that this algorithm improves the recognition rate and robustness.

Face Recognition Based on Gabor Filter and Improved Linear Local Tangent Space Alignment Algorithm

In this paper, Gabor filtering and linear local tangent space alignment algorithm and its improved algorithm are used on face recognition. The Gabor wavelet transform can detect the image information in different directions and scales, according to its selective direction and frequency characteristics. The LLTSA reduces the dimension of the sample while the LLTSA and the other improved algorithms extract secondary feature. Experiment and analyze the average recognition rate of the LLTSA and its improved algorithms with the variation of dimension. The experiment results show the effectiveness of the method, increasing the face recognition accuracy.

Feature Extraction of Diesel Engine Wear Fault Based on Local Tangent Space Alignment

In view of diesel engine wear fault feature extraction, feature extraction of diesel engine wear fault based on Local Tangent Space Alignment (LTSA) was put forward. This paper analyzes LTSA algorithm which reveals the characteristics of manifold learning. Take diesel engine fault diagnosis test rig as example, vibration information was got through imitating different kinds of diesel engine wear fault. LTSA algorithm was applied for dimensionality reduction. LTSA algorithm’s classification performance was compared in accordance with recognition rate of multi-class SVM. The experimental results show that LTSA has high recognition rate and is a very effective feature extraction method for diesel engine wear fault.

Multi-Layer Kernel Learning Method Faced on Roller Bearing Fault Diagnosis

Bearing fault is the major fault of the rotating machinery, in order to better identify the fault of bearing, the multi-layer kernel learning methods based on local tangent space alignment (LTSA) and support vector machine (SVM) are proposed. In this method, the supervised learning is embedded into the improved local tangent space alignment algorithm, realize fault feature extraction and new data processing for equipment fault signal, and then correctly classify the faults by non-linear support vector machine. The experiment result for roller bearing fault diagnosis shows that SILTSA-SVM method has better diagnosis effect to related methods.

Signal Denoise Method Based on Fractal Dimension,the Higher Order Statistics and Local Tangent Space Arrangement

In denoise method for nonlinear time series based on principle manifold learning, reduction targets are chosen at random, using linear method of singular value decomposition solving local tangent space coordinate, these caused efficiency and effect of denoise lower. To solve this problem, a new denoise method based on based on the fractal dimension, higher order statistics and local tangent space arrangement is proposed. The intrinsic dimension is estimated as dimension of reduction targets by fractal geometry method, the data outside intrinsic dimension space will be regarded as noise signal to be eliminated . At the same time, making use of restraining characteristic to colored noise of high-order cumulan, covariance matrix is constructed with the fourth-order cumulant function instead of second-order moment function covariance matrix ,local tangent space alignment algorithm based on fourth-order cumulan is also proposed. Noise reduction experiments on lorenz signal and fan’s vibrating signal show that method proposed in this paper has better denoise effect.

Application of Incremental Local Tangent Space Alignment Algorithm to Rolling Bearings Fault Diagnosis

In view of the problem of extrapolating the embedding of a manifold learning from the given data points to incremental data points,the incremental local tangent space alignment algorithm is presented.The method of global coordinates matrix iteration and lower-dimensional coordinates computation is introduced.For incremental learning,the global coordinates of affect points are recomputed,and the updated global coordinates matrix is used to construct global coordinate of the new data points.The lower-dimensional embedding coordinates of the incremental points are formulated by the updated global coordinate matrix and lower-dimensional embedding coordinates of the given data points.The lower-dimensional embedding coordinates of all points are updated by the eigenvalue iteration algorithm.Experiments with the proposed method are carried out to identify the four different mode of the vibration signal of rolling bearing.The experiments result of the incremental points embedding and scatter of the different sorts demonstrates that the incremental local tangent space alignment algorithm can extrapolate the given data points manifold to the incremental learning and the incremental data points can embed effectively to the previously learned manifold.

Locally affine patch mapping and global refinement for image super-resolution

This paper deals with the super-resolution (SR) problem based on a single low-resolution (LR) image. Inspired by the local tangent space alignment algorithm in [16] for nonlinear dimensionality reduction of manifolds, we propose a novel patch-learning method using locally affine patch mapping (LAPM) to solve the SR problem. This approach maps the patch manifold of low-resolution image to the patch manifold of the corresponding high-resolution (HR) image. This patch mapping is learned by a training set of pairs of LR/HR images, utilizing the affine equivalence between the local low-dimensional coordinates of the two manifolds. The latent HR image of the input (an LR image) is estimated by the HR patches which are generated by the proposed patch mapping on the LR patches of the input. We also give a simple analysis of the reconstruction errors of the algorithm LAPM. Furthermore we propose a global refinement technique to improve the estimated HR image. Numerical results are given to show the efficiency of our proposed methods by comparing these methods with other existing algorithms.

Fault Diagnosis Method Based on Supervised Manifold Learning and SVM

In order to better identify the fault of bearing,one new fualt diagnosis method based on supervised Linear local tangent space alignment (SLLTSA) and support vector machine (SVM) is proposed..In this methd, the supervised learning is embedded into the linear local tangent space alignment algorithm,making full use of experience category information for fault feature extraction, and then using linear transformation matrix to fast process the new monitoring data, finally distinguishing fault status of the incremental data by nonlinear SVM algorithm. The experiment result for roller bearing fault diagnosis shows that SLLTSA-SVM method has better diagnosis effect than related unsupervised methods.

Fault Diagnosis Method Based on Supervised Incremental Local Tangent Space Alignment and SVM

To enhance the effect of fault diagnosis, a new fualt diagnosis method based on supervised incremental local tangent space alignment (SILTSA) and support vector machine (SVM) is proposed. The supervised learning approach is embedded into the incremental local tangent space alignment algorithm, to realize fault feature extraction and new data processing for equipment fault signal, and then correctly classify the faults by non-linear support vector machines. The experiment result for roller bearing fault diagnosis shows that SILTSA-SVM method has better diagnosis effect to related methods

A Better Scaled Local Tangent Space Alignment Algorithm

Recently, a new manifold learning algorithm, LTSA (local tangent space alignment), has been proposed. It is efficient for many nonlinear dimension reduction problems but unfit for large data sets and newcome data. In this paper, an improved algorithm called partitional local tangent space alignment (PLTSA) is presented, which is based on VQPCA (vector quantization principal component analysis) and LTSA. In the algorithm, the sample space is first divided into overlapping blocks using the X-Means algorithm. Then each block is projected to its local tangent space to get local low-dimensional coordinates of the points in it. At last, the global low-dimensional embedded manifold is obtained by local affine transformations. PLTSA is better than VQPCA in that it gives the global coordinates of the data. It works on a much smaller optimization matrix than that of LTSA and leads to a better-scaled algorithm. The algorithm also provides a set of transformations that allow to calculate the global embedded coordinates of the newcome data. Experiments illustrate the validity of this algorithm.