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For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. In this work, the time domain features and time-frequency-domain features extracted from several successive segments of current signals make up a feature vector, which is adopted for fault diagnosis of PMDCMs. Many redundant features will lead to a decrease in diagnosis efficiency and increase the computation cost, so it is necessary to eliminate redundant features and features that have negative effects. This paper presents a novel supervised filter feature selection method for reducing data dimension by employing the Gaussian probability density function (GPDF) and named Gaussian vote feature selection (GVFS). To evaluate the effectiveness of the proposed GVFS, we compared it with the other five filter feature selection methods by utilizing the PMDCM's data. Additionally, Gaussian naive Bayes (GNB), k -nearest neighbor algorithm (k -NN), and support vector machine (SVM) are utilized for the construction of fault diagnosis models. Experimental results show that the proposed GVFS has a better diagnostic effect than the other five feature selection methods, and the average accuracy of fault diagnosis improves from 97.89% to 99.44%. This paper lays the foundation of fault diagnosis for PMDCMs and provides a novel filter feature selection method. [ABSTRACT FROM AUTHOR]

Accurate identification of the tool wear states in machining of titanium alloys continues to be a thorny problem. Rapid construction of accurate and effective tool wear predictive models with regard to various titanium alloys is indispensable for promoting the development of intelligent manufacturing. This article presents a novel WOA-SVM model that integrates support vector machine (SVM) and whale optimization algorithm (WOA), which is utilized for accurate estimation of the tool wear in end milling of titanium alloy Ti-6Al-4V under variable cutting conditions. The signal features in three domains are extracted from the original cutting forces and constitute the “complete features” that are adopted as the monitoring features. Besides, neighborhood preserving embedding (NPE) is utilized to fuse the monitoring features and realize dimension reduction, which aims at improving the modeling efficiency of the novel WOA-SVM model. The experimental results show that under the premise of guaranteeing prediction accuracy, the utilization of NPE reduces the modeling time-consumption of WOA-SVM by more than 90%. In comparison with the commonly used methods (such as PSO-SVM and GSA-SVM), WOA-SVM takes on comparable prediction accuracy and can reduce the modeling time-consumption by more than 30% in most cases. Moreover, the WOA-SVM model has better prediction accuracy and stability than other classical methods, such as ${k}$ -nearest neighbor ($k$ -NN), feedforward neural network (FFNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and classification and regression tree (CART). Therefore, the combination of NPE and WOA-SVM provides a guarantee for rapidly constructing accurate tool wear predictive models in machining of titanium alloys. [ABSTRACT FROM AUTHOR]

For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. Only using the signal features of current in a single segment is not conducive to fault diagnosis for PMDCMs. In this work, multi-segment feature extraction is presented for improving the effect of fault diagnosis of PMDCMs. Additionally, a support vector machine (SVM), a classification and regression tree (CART), and the k-nearest neighbor algorithm (k-NN) are utilized for the construction of fault diagnosis models. The time domain features extracted from several successive segments of current signals make up a feature vector, which is adopted for fault diagnosis of PMDCMs. Experimental results show that multi-segment features have a better diagnostic effect than single-segment features; the average accuracy of fault diagnosis improves by 19.88%. This paper lays the foundation of fault diagnosis for PMDCMs through multi-segment feature extraction and provides a novel method for feature extraction. [ABSTRACT FROM AUTHOR]

A practical prediction method for the Total volatile basic nitrogen (TVB-N) content evaluation of beef covered by the packaging films was proposed by using visible-near infrared hyperspectral imaging (HSI). Hyperspectral image under three conditions: beef without films, beef covered by appressed films, and beef covered by non-touch films was obtained. The results indicated that the influence on prediction accuracy of TVB-N content with the presence of films could be properly eliminated by spectral preprocessing methods and modeling methods. Moreover, the successive projections algorithm (SPA) combined with least squares support vector machines (SPA-LSSVM) was the best predictive model for beef without film, beef covered by non-touch polypropylene and polyethylene, and determination coefficient in prediction (R P 2) were 0.9167, 0.9157, and 0.9423 respectively, and root mean square errors in prediction (RMSEP) was 1.7808, 1.8758, and 1.4304 respectively. The competitive adaptive reweighted sampling (CARS) combined with LSSVM (CARS-LSSVM) was more effective for beef covered by appressed films of polypropylene and polyethylene, and R P 2 were 0.9046 and 0.9533 respectively, and RMSEP was 2.0444 and 1.2858 respectively. This study demonstrated that it is feasible to predict the TVB-N content of packaged beef using visible-near infrared hyperspectral technology. • Hyperspectral technology was used to predict the TVB-N content of packaged beef. • Three conditions were designed to investigate the influence of packaging films on the predicted results. • The reflectance spectrum of beef with film was affected by the dual effects of films scattering and transmission. • CARS-LSSVM was suitable for the condition of beef covered by appressed films, while SPA-LSSVM was better for non-touch films. [ABSTRACT FROM AUTHOR]

• A novel IRBF-RVM model is proposed for the diagnosis of atrial fibrillation (AF). • The kernel parameter of IRBF-RVM has a much larger selectable region than RBF-RVM. • IRBF-RVM performs better than SVM in terms of rapid modeling and sparseness. • IRBF-RVM outperforms k -NN, Naive Bayes, FFNN, AdaBoost and RF, in terms of prediction accuracy. • The generalization performance of IRBF-RVM is not inferior to other powerful machine learning methods. Atrial fibrillation (AF) is one of the common cardiovascular diseases, and electrocardiography (ECG) is a key indicator for the detection and diagnosis of AF and other heart diseases. In this study, an improved machine learning method is proposed for rapid modeling and accurate diagnosis of AF. This paper presents a novel IRBF-RVM model that combines the integrated radial basis function (IRBF) and relevance vector machine (RVM), which is utilized for the diagnosis of AF. The synchronous 12-lead ECG signals are collected from the human body surface so as to fully reflect the electrical activity of the whole heart. RR intervals of the QRS-waves in ECG signals are obtained by means of the classical Pan–Tompkins algorithm. The RR-features extracted from RR intervals are adopted as the diagnostic features for AF patients. In addition, the conventional RBF-RVM model, support vector machine (SVM) and other machine learning methods are also investigated for the diagnosis of AF so as to reflect the advantage of the proposed IRBF-RVM model. The open MIT-BIH arrhythmia database (MITDB) is also used to evaluate the predictive performance of these state-of-the-art methods. Altogether 1056 AF patients and 904 healthy people are participated in this study and validate the effectiveness of each channel of the 12-lead ECG signals. Experimental results show that the classification rate of IRBF-RVM can reach up to 98.16% by recurring to Channel II of the 12-lead ECG signals. IRBF-RVM absorbs the advantages of IRBF, which makes the kernel parameter of IRBF-RVM have a much larger selectable region than RBF-RVM. In addition, RVM has faster modeling and recognition speed in comparison with SVM. This work lays the foundation for the application of RVM to accurate diagnosis of AF. [ABSTRACT FROM AUTHOR]

• A novel tool wear predictive model based on KPCA_IRBF and RVM is developed. • KPCA_IRBF helps to improve the prediction accuracy of RVM. • KPCA_IRBF helps to ameliorate the confidence interval (CI) of RVM. • CI is an extra advantage over the traditional methods such as PLS, ANN and SVM. • In comparison with SVM, RVM has high sparseness and comparable prediction accuracy. In order to realize real-time and accurate monitoring of the tool wear in machining process, this paper presents a tool wear predictive model based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and relevance vector machine (RVM). The traditional methods such as partial least squares regression (PLS), artificial neural network (ANN) and support vector machine (SVM) can only provide predicted values which have no probabilistic significance. As a sparse probabilistic model, RVM can provide both the predicted value and the corresponding confidence interval (CI). However, the existence of process noises and redundancy will seriously affect the prediction accuracy and the stability of CI. As a new dimension-increment technique, KPCA_IRBF helps to weaken the negative effects of process noises and redundancy by increasing the dimensionality of monitoring features. The fused features obtained by KPCA_IRBF are more sensitive to the change of tool wear. Two different cutting experiments are carried out to verify the effectiveness of KPCA_IRBF in improving the prediction accuracy and ameliorating the CI of RVM. The experimental results show that KPCA_IRBF can reduce the root mean square error (RMSE) of RVM by more than 30% and compress the average width of CI by more than 90%. To further show the advantages of RVM, the traditional methods such as PLS, ANN and SVM are also utilized to realize tool wear prediction. This paper lays the foundation for the application of RVM to industrial field. [ABSTRACT FROM AUTHOR]