Local Fisher Discriminant Analysis Research Articles

Fault diagnosis for blast furnace ironmaking process based on randomized local fisher discriminant analysis

AbstractFault diagnosis plays a vital role in ensuring the operation safety of blast furnaces and improving the quality of molten iron in the ironmaking and steelmaking industry. The blast furnace ironmaking process (BFIP) is intrinsically nonlinear. To address the nonlinearity issue of BFIP, a novel fault diagnosis approach that combines the randomized method, local structure information, and Fisher discriminant analysis is proposed in this paper. Using a randomized feature map, the process data is first mapped onto a randomized explicit low‐dimensional feature space. Compared to kernel methods, explicit low‐dimensional random Fourier features considerably reduce the computational cost, particularly for real‐time fault diagnosis for a large training dataset or a large‐scale process. Additionally, the local structure information contained in the randomized low‐dimensional feature space is extracted. The fault diagnosis performance is improved through the exploration of the local structure of random Fourier features. Finally, the blast furnace iron‐marking process state is determined using Bayesian inference. Case studies on a real‐world BFIP are carried out to demonstrate the superior performance of the proposed method in comparison with other related methods.

Multi-Scale Dense Graph Attention Network for Hyperspectral Classification

In recent years, numerous deep learning-based methods have gained increasing attention in hyperspectral classification, particularly the Graph Neural Network, which exhibits superior capabilities in structural description. However, a single graph structure is not suitable for hyperspectral feature representation. Therefore, we propose a novel Multiple-Scale graph network structure, known as the Multi-Scale Dense Graph Attention network for hyperspectral classification. Firstly, semi-supervised local Fisher discriminant analysis and superpixel segmentation were employed for dimensionality reduction and multi-scale graph construction, respectively. Secondly, Spectral-Spatial convolution is applied to extract shallow features from the image. Subsequently, an improved graph self-attention network is sequentially applied to each scale graph, and the different scale graphs are densely connected through spatial feature alignment modules, designed using twice matrix multiplication. Finally, the combined pixel-level feature map from multiple graph spaces is derived, and Spectral-Spatial convolution is employed to fuse the abundant feature maps for hyperspectral classification. Experimental results on various hyperspectral datasets demonstrate the superiority of our MSDesGATnet over many state-of-the-art methods. The code is available at https://github.com/l7170/MSDesGAT.git.

ILFDA Model: An Online Soft Measurement Method Using Improved Local Fisher Discriminant Analysis

With the advancement of soft measurement, just-in-time learning (JITL) has become a widely adopted framework for online soft-sensing modeling in industrial processes. However, traditional JITL model approaches often rely on simple similarity measures like Euclidean distance, resulting in the underutilization of labeled data. This paper proposes a supervised, improved local Fisher discriminant analysis method based on a JITL framework and local Fisher discriminant analysis (LFDA) to improve data utilization efficiency. In particular, by incorporating the indirect correlation information matrix, this method integrates the inter-class and intra-class dispersion matrix, overcoming the limitation of the LFDA algorithm that only captures direct data correlations. We select two different carbon depositions in the Methanol-to-Olefin reaction system for comparative experiments and use the root mean squared error (RMSE) and R-square (R2) to evaluate the effectiveness of the proposed method. Fitting results show that two kinds of carbon depositions were better than the control model, namely the RMSE of the model were 0.1431 and 0.1513, R2 were 0.8952 and 0.8707.

Discriminative manifold domain adaptation for cross-domain fault diagnosis of rotating machineries

Domain adaptation aims to solve the issue of distribution shift. Manifold learning uses Riemann metrics as nonlinear distance to overcome the limitation of Euclidean metric in domain adaptation. However, it ignores the feature distortion and relationship change caused by the feature mapping framework. Class overlap leads to degradation of classifier performance. Thus, this study proposes a new discriminative manifold domain adaptation (DMDA) framework to solve these problems. DMDA can align manifold structures and domain distributions through discriminative manifold alignment (DMA) and joint distribution alignment, respectively. The DMA method is developed via a new pseudo label voting mechanism, the local fisher discriminant analysis, and the geodesic flow kernel. It can achieve the manifold structure alignment and learn the discriminative features. Moreover, a structural risk minimization classifier is constructed to achieve Laplacian regularization and align the joint distributions of the source domain and target domain. It also implements the fault classification task. The experimental results on bearing and planetary gearbox datasets prove that DMDA has better fault transfer diagnosis performance than the typical domain adaptation methods based on machine learning.

Exponential Local Fisher Discriminant Analysis with Sparse Variables Selection: A Novel Fault Diagnosis Scheme for Industry Application

Local Fisher discriminant analysis (LFDA) has been widely applied to dimensionality reduction and fault classification fields. However, it often suffers from small sample size (SSS) problem and incorporates all process variables without emphasizing the key faulty ones, thus leading to degraded fault diagnosis performance and poor model interpretability. To this end, this paper develops the sparse variables selection based exponential local Fisher discriminant analysis (SELFDA) model, which can overcome the two limitations of basic LFDA concurrently. First, the responsible faulty variables are identified automatically through the least absolute shrinkage and selection operator, and the current optimization problem are subsequently recast as an iterative convex optimization problem and solved by the minimization-maximization method. After that, the matrix exponential strategy is implemented on LFDA, it can essentially overcome the SSS problem by ensuring that the within-class scatter matrix is always full-rank, thus more practical in real industrial practices, and the margin between different categories is enlarged due to the distance diffusion mapping, which is benefit for the enhancement of classification accuracy. Finally, the Tennessee Eastman process and a real-world diesel working process are employed to validate the proposed SELFDA method, experimental results prove that the SELFDA framework is more excellent than the other approaches.

Unsupervised domain adaptation via transferred local Fisher discriminant analysis

Unsupervised domain adaptation via transferred local Fisher discriminant analysis

Sparse Local Fisher Discriminant Analysis for Gas-Water Two-Phase Flow Status Monitoring With Multisensor Signals

Gas-water two-phase flow has typically stable flow statuses and constantly changing transition flow statuses. Accurate identification and real-time monitoring of flow status are conducive to the in-depth study of two-phase flow and the safe operation of industrial process. A monitoring strategy based on sparse local Fisher discriminant analysis (SLFDA) is proposed in this article. First, multisensor signals are obtained to reflect flow process information. Second, the least absolute shrinkage and selection operator is used to find the sparse discriminant directions to determine the key variables relevant to the flow process from multiple sensor signals. Then, the weight coefficient matrixes of SLFDA keep the original structure of the same flow status data and make the data of different flow statuses more separated, which distinguish different flow statuses to the maximum extent. Finally, two monitoring indexes including the discriminant index and the stability index are established to analyze the dynamic flow process, which enable a concurrent monitoring of both flow evolution and instability to realize fine-scale description of flow process. SLFDA can monitor various flow statuses through only one projection discriminant matrix by transforming high-dimensional signals into features representing the flow characteristics, which avoids model traversal and improves monitoring efficiency. Further study of flow status features provides meaningful physical interpretation and in-depth process analysis with consideration of actual flow process. The application on the data of gas-water two-phase flow in horizontal pipe demonstrates the feasibility and efficacy of the method.

DlncRNALoc: A discrete wavelet transform-based model for predicting lncRNA subcellular localization.

The prediction of long non-coding RNA (lncRNA) subcellular localization is essential to the understanding of its function and involvement in cellular regulation. Traditional biological experimental methods are costly and time-consuming, making computational methods the preferred approach for predicting lncRNA subcellular localization (LSL). However, existing computational methods have limitations due to the structural characteristics of lncRNAs and the uneven distribution of data across subcellular compartments. We propose a discrete wavelet transform (DWT)-based model for predicting LSL, called DlncRNALoc. We construct a physicochemical property matrix of a 2-tuple bases based on lncRNA sequences, and we introduce a DWT lncRNA feature extraction method. We use the Synthetic Minority Over-sampling Technique (SMOTE) for oversampling and the local fisher discriminant analysis (LFDA) algorithm to optimize feature information. The optimized feature vectors are fed into support vector machine (SVM) to construct a predictive model. DlncRNALoc has been applied for a five-fold cross-validation on the three sets of benchmark datasets. Extensive experiments have demonstrated the superiority and effectiveness of the DlncRNALoc model in predicting LSL.

Online Signature Verification for Forgery Detection

The increasing trend of using e-versions of document transmission and storage requires the electronic verification of sender/author. This research presents an efficient and robust online handwritten signature verification system targeting verification rates better than the available state-of-the-art systems in the presence of skilled forgeries. Fourier analysis is employed on the signatures to represent feature vectors in higher dimensional space followed by Local Fisher Discriminant Analysis to obtain compress representation while enhancing inter-class scatter between signature patterns. Signature modeling is performed using m-mediod-based modeling approach where m-mediods are put on to represent data distribution in each class. Connected component labeling is applied to binarized images of Urdu text to extract ligatures which are separated into primary ligatures and diacritics. Fast Euclidean Distance is used as dis(similarity). A total of 2414 signature samples including skilled forgeries are considered in our study. The evaluation of the proposed system on Japanese signature dataset provided by SigWiComp2013 realized promising results than the competitors.

A novel imbalanced fault diagnosis method integrated KLFDA with improved cost‐sensitive learning ANBSVM

AbstractFault diagnosis, as an important approach to ensure the safety and stability of industrial processes, has been widely studied in recent years. During the running process, it is noted that the normal data are always much more than the fault data, which demonstrates imbalanced characteristics and leads to a negative effect on the overall accuracy of fault diagnosis. Targeting the problem, a novel imbalanced fault diagnosis method integrated kernel local Fisher discriminant analysis (KLFDA) with improved adaptive near‐Bayesian support vector machine (ANBSVM) is proposed in this paper. First, KLFDA is used to extract the non‐linear features while maintaining the local spatial structure of the data by introducing flow pattern learning. Second, considering the imbalance characteristics of the data, the data set is divided into a majority class (normal data) and a minority class (fault data). The density distributions of the two classes in their overlapping region are characterized by the proportional function of variance. Third, by minimizing the Bayesian error under the proportion function, the weight factors are adaptively obtained and then introduced into the objective function of the support vector machine (SVM). Namely, a cost sensitivity‐based ANBSVM classifier for fault diagnosis is constructed. Finally, by the simulation experiment on the Tennessee Eastman (TE) process, the comparison results show that the proposed ANBSVM‐based fault diagnosis method makes progress in the performance of fault diagnosis with higher diagnostic accuracy and F1 score.

A multi‐fault diagnosis method based on improved SMOTE for class‐imbalanced data

AbstractWith the development of industrial processes, how to effectively diagnose the faults in an increasingly complex production process has attracted widespread attention. It is worth noting that there may be multiple types of faults in the actual industrial process, and there is an extreme class imbalance between the normal samples and the fault samples. Therefore, it is of practical significance to carry out research on the multi‐fault diagnosis method for class‐imbalanced data. In this paper, a multi‐fault diagnosis method based on improved synthetic minority sampling technology (SMOTE) is proposed. First, aiming at the class imbalance, an improved SMOTE algorithm based on Mahalanobis distance (Mahalanobis distance‐based SMOTE [MSMOTE]) is proposed for oversampling. As the Euclidean distance in the traditional SMOTE algorithm does not consider the coupling relationship between features, the Mahalanobis distance is introduced, which is not dependent on the scale and eliminates the influence of different dimensions. Second, in order to better obtain the global and local information of the sample, the kernel local Fisher discriminant analysis (KLFDA) algorithm is used for feature extraction. Third, a multi‐fault diagnosis model based on the AdaBoost.M2 classifier is constructed in which the decision tree is introduced as the weak classifier. The Adaboost.M2 algorithm integrates multiple decision trees by setting the sample weight, the label weight, and the classifier weight, which effectively improve the classification accuracy by only using the decision tree. Finally, the Tennessee Eastman process is used to conduct case studies. For the comparison results, the proposed multi‐fault diagnosis method based on improved SMOTE has higher accuracy and F1‐Score.

Fault Diagnosis of Rotating Machinery Based on Local Centroid Mean Local Fisher Discriminant Analysis

Fault Diagnosis of Rotating Machinery Based on Local Centroid Mean Local Fisher Discriminant Analysis

KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis.

Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of principal components (DAPC). However, genetic features produced from both approaches could fail to correctly characterize population structure for complex scenarios involving admixture. In this study, we introduce Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC), a supervised non-linear approach for inferring individual geographic genetic structure that could rectify the limitations of these approaches by preserving the multimodal space of samples. We tested the power of KLFDAPC to infer population structure and to predict individual geographic origin using neural networks. Simulation results showed that KLFDAPC has higher discriminatory power than PCA and DAPC. The application of our method to empirical European and East Asian genome-wide genetic datasets indicated that the first two reduced features of KLFDAPC correctly recapitulated the geography of individuals and significantly improved the accuracy of predicting individual geographic origin when compared to PCA and DAPC. Therefore, KLFDAPC can be useful for geographic ancestry inference, design of genome scans and correction for spatial stratification in GWAS that link genes to adaptation or disease susceptibility.

Incipient fault detection for dynamic chemical processes based on enhanced CVDA integrated with probability information and fault-sensitive features

Recently, canonical variate dissimilarity analysis (CVDA) has emerged as an efficient incipient fault monitoring method for dynamic chemical processes. Nevertheless, the basic CVDA method omits the mining of the probability information hidden in the canonical variate vectors and does not make full use of the fault-sensitive features provided by the prior fault data. Aiming at these two limitations, this paper presents an enhanced CVDA method, called fault-sensitive probability-related CVDA (FSPRCVDA), for better monitoring incipient faults. Firstly, the traditional CVDA is improved to the probability-related CVDA (PRCVDA) by applying the Kullback–Leibler divergence to measure the probability distribution changes of canonical variates. Secondly, fault-sensitive features are extracted by local Fisher discriminant analysis on the normal and prior fault data. The extracted fault-sensitive features are used to construct the auxiliary model for assisting the primary PRCVDA model, which leads to the holistic FSPRCVDA model. The case study on a continuous stirred tank reactor system shows that the proposed FSPRCVDA method has higher incipient fault detection rate than the basic CVDA method.

Local Discriminant Subspace Learning for Gas Sensor Drift Problem

Sensor drift is one of the severe issues that gas sensors suffer from. To alleviate the sensor drift problem, a gas sensor drift compensation approach is proposed based on local discriminant subspace projection (LDSP). The proposed approach aims to find a subspace to reduce the distribution difference between two domains, i.e., the source and target domain. Similar to domain regularized component analysis (DRCA) which is a recently proposed sensor drift correction method, the mean distribution discrepancy is minimized in the common subspace in our approach. LDSP extends DRCA in two aspects, i.e., it not only takes the label information of the source data into consideration to reduce the possibility of the case that samples in the subspace with different class labels stay close to each other, but also borrows the idea of locality-preserving projection to deal with multimodal data. Specifically, inspired by local Fisher discriminant analysis (LFDA), the label information is utilized to maximize the local between-class variance of source data in the latent common subspace and simultaneously minimize the local within-class variance. The formulation of LDSP is a generalized eigenvalue problem that can be readily solved. The experimental results have shown the proposed method outperforms other gas sensor drift compensation methods in terms of classification accuracy on two public gas sensor drift datasets.

Rotating Machinery Fault Diagnosis Method Based on Improved Semisupervised Generative Confrontation Network

Error diagnosis and detection have become important in modern production due to the importance of spinning equipment. Artificial neural network pattern recognition methods are widely utilized in rotating equipment fault detection. These methods often need a large quantity of sample data to train the model; however, sample data (especially fault samples) are uncommon in engineering. Preliminary work focuses on dimensionality reduction for big data sets using semisupervised methods. The rotary machine’s polar coordinate signal is used to build a GAN network structure. ANN and tiny samples are utilized to identify DCGAN model flaws. The time-conditional generative adversarial network is proposed for one-dimensional vibration signal defect identification under data imbalance. Finally, auxiliary samples are gathered under similar conditions, and CCNs learn about target sample characteristics. Convolutional neural networks handle the problem of defect identification with small samples in different ways. In high-dimensional data sets with nonlinearities, low fault type recognition rates and fewer marked fault samples may be addressed using kernel semisupervised local Fisher discriminant analysis. The SELF method is used to build the optimum projection transformation matrix from the data set. The KNN classifier then learns low-dimensional features and detects an error kind. Because DCGAN training is unstable and the results are incorrect, an improved deep convolutional generative adversarial network (IDCGAN) is proposed. The tests indicate that the IDCGAN generates more real samples and solves the problem of defect identification in small samples. Time-conditional generation adversarial network data improvement lowers fault diagnosis effort and deep learning model complexity. The TCGAN and CNN are combined to provide superior fault detection under data imbalance. Modeling and experiments demonstrate TCGAN’s use and superiority.

Research on complex attribute big data classification based on iterative fuzzy clustering algorithm

In order to overcome the low classification accuracy of traditional methods, this paper proposes a new classification method of complex attribute big data based on iterative fuzzy clustering algorithm. Firstly, principal component analysis and kernel local Fisher discriminant analysis were used to reduce dimensionality of complex attribute big data. Then, the Bloom Filter data structure is introduced to eliminate the redundancy of the complex attribute big data after dimensionality reduction. Secondly, the redundant complex attribute big data is classified in parallel by iterative fuzzy clustering algorithm, so as to complete the complex attribute big data classification. Finally, the simulation results show that the accuracy, the normalized mutual information index and the Richter’s index of the proposed method are close to 1, the classification accuracy is high, and the RDV value is low, which indicates that the proposed method has high classification effectiveness and fast convergence speed.

A Novel Three-stage Feature Fusion Methodology and its Application in Degradation State Identification for Hydraulic Pumps

Abstract The performance of feature is essential to the degradation state identification for hydraulic pumps. The initial feature set extracted from the vibration signal of the hydraulic pump is often high-dimensional and contains redundant information, which undermines the effectiveness of the feature set. The novel three-stage feature fusion scheme proposed in this paper aims to enhance the performance of the original features extracted from the vibration signal. First, sparse local Fisher discriminant analysis (SLFDA) performs intra-set fusion within the two original feature sets, respectively. SLFDA has a good effect on samples with intra-class multimodality, and the feature set fused by it has obvious multivariate normal distribution characteristics, which is conducive to the next fusion. Second, our modified intra-class correlation analysis (MICA) is used to fuse two feature sets in the second stage. MICA is a CCA (Canonical correlation analysis) -based method. A new class matrix is used to modify the covariance matrix between two feature sets, which allows MICA to conveniently inherit the discriminating structure while fusing features. Finally, we propose a feature selection algorithm based on kernel local Fisher discriminant analysis (KLFDA) and kernel canonical correlation analysis (KCCA) to select the desired features. This algorithm based on Max-Relevance and Min-Redundancy (mRMR) framework solves the problem that CCA cannot properly evaluate the correlation between features and the class variable, as well as accurately evaluates the correlation among features. Based on the experimental data, the proposed method is compared with several popular methods, and the feature fusion methods used in some previous studies related to the fault diagnosis of rotating machinery are compared with it as well. The results show that the fusion effectiveness of our method is better than other methods, which obtains higher recognition accuracy.

Optimal multi-kernel local fisher discriminant analysis for feature dimensionality reduction and fault diagnosis

To achieve a more desirable fault diagnosis accuracy by applying multi-domain features of vibration signals, it is significative and challenging to refine the most representative and intrinsic feature components from the original high dimensional feature space. A novel dimensionality reduction method for fault diagnosis is proposed based on local Fisher discriminant analysis (LFDA) which takes both label information and local geometric structure of the high dimensional features into consideration. Multi-kernel trick is introduced into the LFDA to improve its performance in dealing with the nonlinearity of mapping high dimensional feature space into a lower one. To obtain an optimal diagnosis accuracy by the reduced features of low dimensionality, binary particle swarm optimization (BPSO) algorithm is utilized to search for the most appropriate parameters of kernels and K-nearest neighbor (kNN) recognition model. Samples with labels are used to train the optimal multi-kernel LFDA and kNN (OMKLFDA-kNN) fault diagnosis model to obtain the optimal transformation matrix. Consequently, the trained fault diagnosis model implements the recognition of machinery health condition with the most representative feature space of vibration signals. A bearing fault diagnosis experiment is conducted to verify the effectiveness of proposed diagnostic approach. Performance comparison with some other methods are investigated, and the improvement for fault diagnosis of the proposed method are confirmed in different aspects.

A Multiview Spectral–Spatial Feature Extraction and Fusion Framework for Hyperspectral Image Classification

Hyperspectral images (HSIs) can provide abundant and diverse features which are helpful for classification, such as spectral, texture, and shape features. Combining these features can enhance the ability to describe the characteristics of different classes of land covers. However, most of the existing methods usually stack features from multiple views to construct high-dimensional data and then learn information from it which may waste information inherent in different feature spaces. In this letter, we proposed a multiview spectral–spatial feature extraction (FE) and fusion framework for the analysis and classification of the HSIs. First, different and complementary spatial features extracted by extended multiattribute profiles (EMAPs), gray-level cooccurrence matrix (GLCM), and Gabor from the original HSI are, respectively, stacked with the spectral bands to construct multiview data set for one single scene of HSI. Thus, each sample can be represented in different spectral–spatial domains. Then, a semisupervised FE method, which combines local fisher discriminant analysis (LFDA) that explores discriminative information from limited labeled samples and the improved neighborhood preserving embedding (NPE) that aims at maintaining the local neighborhood structure from a global perspective, is applied on the multiview data set to eliminate redundant information and obtain multiview spectral–spatial features. Note that the improved NPE which adds spatial interpixel correlations to similarity measure between samples is applied on all the samples rather than unlabeled samples, and hence, the best spatial nearby neighbors for each sample could be found from the whole data. Finally, we integrate these multiview features with diversity and complementarity to construct the intact feature representation for each sample. The experimental results show that the proposed method outperforms the state-of-the-art multiview FE methods for HSI classification.