A New Feature Selection Algorithm Based on the Mean Impact Variance

The selection of fewer or more representative features from multidimensional features is important when the artificial neural network (ANN) algorithm is used as a classifier. In this paper, a new feature selection method called the mean impact variance (MIVAR) method is proposed to determine the feature that is more suitable for classification. Moreover, this method is constructed on the basis of the training process of the ANN algorithm. To verify the effectiveness of the proposed method, the MIVAR value is used to rank the multidimensional features of the bearing fault diagnosis. In detail, (1) 70-dimensional all waveform features are extracted from a rolling bearing vibration signal with four different operating states, (2) the corresponding MIVAR values of all 70-dimensional features are calculated to rank all features, (3) 14 groups of 10-dimensional features are separately generated according to the ranking results and the principal component analysis (PCA) algorithm and a back propagation (BP) network is constructed, and (4) the validity of the ranking result is proven by training this BP network with these seven groups of 10-dimensional features and by comparing the corresponding recognition rates. The results prove that the features with larger MIVAR value can lead to higher recognition rates

Kondo physics in antiferromagnetic Weyl semimetal Mn3+xSn1-x films

Topology and strong electron correlations are crucial ingredients in emerging quantum materials, yet their intersection in experimental systems has been relatively limited to date. Strongly correlated Weyl semimetals, particularly when magnetism is incorporated, offer a unique and fertile platform to explore emergent phenomena in novel topological matter and topological spintronics. The antiferromagnetic Weyl semimetal Mn3Sn exhibits many exotic physical properties such as a large spontaneous Hall effect and has recently attracted intense interest. In this work, we report synthesis of epitaxial Mn3+xSn1-x films with greatly extended compositional range in comparison with that of bulk samples. As Sn atoms are replaced by magnetic Mn atoms, the Kondo effect, which is a celebrated example of strong correlations, emerges, develops coherence, and induces a hybridization energy gap. The magnetic doping and gap opening lead to rich extraordinary properties as exemplified by the prominent DC Hall effects and resonance-enhanced terahertz Faraday rotation

A time-resolved fluorescence microsphere-lateral flow immunochromatographic strip for quantitative detection of Pregnanediol-3-glucuronide in urine samples

Introduction: Pregnanediol-3-glucuronide (PdG), as the main metabolite of progesterone in urine, plays a significant role in the prediction of ovulation, threatened abortion, and menstrual cycle maintenance.Methods: To achieve a rapid and sensitive assay, we have designed a competitive model-based time-resolved fluorescence microsphere-lateral flow immunochromatography (TRFM-LFIA) strip.Results: The optimized TRFM-LFIA strip exhibited a wonderful response to PdG over the range of 30–2,000 ng/mL, the corresponding limit of detection (LOD) was calculated as low as 8.39 ng/mL. More importantly, the TRFM-LFIA strip was innovatively used for the quantitative detection of PdG in urine sample, and excellent recovery results were also obtained, ranging from 97.39% to 112.64%.Discussion: The TRFMLFIA strip possessed robust sensitivity and selectivity in the determination of PdG, indicating the great potential of being powerful tools in the biomedical and diagnosis region

A Disposable Organophosphorus Pesticides Enzyme Biosensor Based on Magnetic Composite Nano-Particles Modified Screen Printed Carbon Electrode

A disposable organophosphorus pesticides (OPs) enzyme biosensor based on magnetic composite nanoparticle-modified screen printed carbon electrodes (SPCE) has been developed. Firstly, an acetylcholinesterase (AChE)-coated Fe3O4/Au (GMP) magnetic nanoparticulate (GMP-AChE) was synthesized. Then, GMP-AChE was absorbed on the surface of a SPCE modified by carbon nanotubes (CNTs)/nano-ZrO2/prussian blue (PB)/Nafion (Nf) composite membrane by an external magnetic field. Thus, the biosensor (SPCE│CNTs/ZrO2/PB/Nf│GMP-AChE) for OPs was fabricated. The surface of the biosensor was characterized by scanning electron micrography (SEM) and X-ray fluorescence spectrometery (XRFS) and its electrochemical properties were studied by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The degree of inhibition (A%) of the AChE by OPs was determined by measuring the reduction current of the PB generated by the AChE-catalyzed hydrolysis of acetylthiocholine (ATCh). In pH = 7.5 KNO3 solution, the A was related linearly to the concentration of dimethoate in the range from 1.0 × 10−3–10 ng·mL−1 with a detection limit of 5.6 × 10−4 ng·mL−1. The recovery rates in Chinese cabbage exhibited a range of 88%–105%. The results were consistent with the standard gas chromatography (GC) method. Compared with other enzyme biosensors the proposed biosensor exhibited high sensitivity, good selectivity with disposable, low consumption of sample. In particular its surface can be easily renewed by removal of the magnet. The convenient, fast and sensitive voltammetric measurement opens new opportunities for OPs analysis

Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation

The important issue in planetary gear fault diagnosis is to extract the dependable fault characteristics from the noisy vibration signal of planetary gearbox. To address this critical problem, an envelope manifold demodulation method is proposed for planetary gear fault detection in the paper. This method combines complex wavelet, manifold learning, and frequency spectrogram to implement planetary gear fault characteristic extraction. The vibration signal of planetary gear is demodulated by wavelet enveloping. The envelope energy is adopted as an indicator to select meshing frequency band. Manifold learning is utilized to reduce the effect of noise within meshing frequency band. The fault characteristic frequency of the planetary gear is shown by spectrogram. The planetary gearbox model and test rig are established and experiments with planet gear faults are conducted for verification. All results of experiment analysis demonstrate its effectiveness and reliability

Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features

Nonlinear characteristics are ubiquitous in the vibration signals produced by rolling element bearings. Fractal dimensions are effective tools to illustrate nonlinearity. This paper proposes a new approach based on Multiscale General Fractal Dimensions (MGFDs) to realize fault diagnosis of rolling element bearings, which are robust to the effects of variation in operating conditions. The vibration signals of bearing are analyzed to extract the general fractal dimensions in multiscales, which are in turn utilized to construct a feature space to identify fault pattern. Finally, bearing faults are revealed by pattern recognition. Case studies are carried out to evaluate the validity and accuracy of the approach. It is verified that this approach is effective for fault diagnosis of rolling element bearings under various operating conditions via experiment and data analysis

A Fault Detection Method for Electrohydraulic Switch Machine Based on Oil-Pressure-Signal-Sectionalized Feature Extraction

A turnout switch machine is key equipment in a railway, and its fault condition has an enormous impact on the safety of train operation. Electrohydraulic switch machines are increasingly used in high-speed railways, and how to extract effective fault features from their working condition monitoring signal is a difficult problem. This paper focuses on the sectionalized feature extraction method of the oil pressure signal of the electrohydraulic switch machine and realizes the fault detection of the switch machine based on this method. First, the oil pressure signal is divided into three stages according to the working principle and action process of the switch machine, and multiple features of each stage are extracted. Then the max-relevance and min-redundancy (mRMR) algorithm is applied to select the effective features. Finally, the mini batch k-means method is used to achieve unsupervised fault diagnosis. Through experimental verification, this method can not only derive the best sectionalization mode and feature types of the oil pressure signal, but also achieve the fault diagnosis and the prediction of the status of the electrohydraulic switch machine