New Approach For Discrimination Research Articles

Discrimination between demagnetization and eccentricity faults in PMSMs using real and imaginary components of stator current spectral analysis

Permanent magnet demagnetization and rotor eccentricity are the main faults in permanents magnet synchronous motors (PMSMs). To detect these faults, motor current signature analysis (MCSA) has become the most common method in the field due to its simplicity, low computation time, and remote monitoring capability. Unfortunately, this method has a major drawback relating to the frequency signatures of demagnetization and eccentricity faults, which appear in the same frequency locations. To avoid this drawback while retaining the main advantages of the MCSA method, this paper proposes a new approach for discrimination between demagnetization and eccentricity faults in PMSMs. The proposed approach is based on the simultaneous monitoring of the real and imaginary components of the characteristic harmonics of these faults, which are obtained from stator current spectral analysis. This monitoring is based on the evolution of the characteristic harmonic signs in these two components. The positive or negative signs, make it possible to accurately discriminate between the effects produced by eccentricity faults from the effects produced by demagnetization faults. Finally, several experiments have been presented in this paper to demonstrate the capability and effectiveness of the proposed approach.

A Proposed Approach for Separation between Short Circuit Fault, Magnetic Saturation Phenomenon and Supply Unbalance in Permanent Magnet Synchronous Motor

This paper proposes a new approach for discrimination between short circuit fault, magnetic saturation phenomenon and supply voltage unbalance in permanent magnet synchronous motor. This proposed approach is based on tracking the simultaneous position in the polar coordinates of the amplitude and phase angle of the voltage and current indicator FFT signals of the harmonics characterizing the three phenomena. The voltage indicator set using three supply voltages to check the status of the power source. In the same way, the current indicator defined using three line currents to discriminate between the short circuit fault and the magnetic saturation phenomenon. To highlight the effectiveness and the capability of this approach, a series of simulations are performed on signals obtained from a permanent magnet synchronous motor mathematical model. This model is based on a 2D-extension of the modified winding function approach.

Non-destructive discrimination of the variety of sweet maize seeds based on hyperspectral image coupled with wavelength selection algorithm

A novel method for discriminating the varieties of sweet maize seeds was developed on the basis of hyperspectral imaging technology in the visible and near-infrared (Vis–NIR) region (326.7–1098.1 nm). First, the Vis–NIR hyperspectral images of nine varieties of sweet maize seeds were obtained with the orientations of germ up and down. Second, Savitzky–Golay (SG) smoothing and first derivative (FD) methods were used to highlight the differences of different maize seeds. Finally, a variety discrimination model was established by support vector machine (SVM) based on the effective wavelengths extracted by competitive adaptive reweighted sampling (CARS) algorithm. Additionally, the performance of other six comparative algorithms including successive projections algorithm (SPA), principal component analysis (PCA), factor analysis (FA), random projection (RP), independent component analysis (ICA), and t-distributed stochastic neighbor embedding (t-SNE) were compared with CARS. The classification models of SVM was also compared with Naive Bayes (NB), K-nearest neighbors (KNN), artificial neural networks (ANN), decision tree (DT), linear discriminant analysis (LDA) and logistic regression (LR) algorithms. Results showed that the SG + FD + CARS + SVM model achieved the best performance for discrimination of nine varieties of sweet maize seeds with classification accuracies of 94.07% and 94.86% for germ up and germ down orientations respectively, which is promising to be a new approach for discrimination the variety of sweet maize seeds.

Discrimination of polycyclic aromatic hydrocarbons based on fluorescence spectrometry coupled with CS-SVM

A new approach for discrimination of polycyclic aromatic hydrocarbons (PAHs) in environment was proposed based on fluorescence coupled with CS-SVM. Two groups of experiments were carried out on PAHs with similar spectra. The first one was PAHs any two among benzo[k]fluoranthene (BkF), benzo[b]fluoranthene (BbF) and benzo[a]pyrene (BaP). The second one was naphthalene (NAP) and fluorene (FLU) with similar spectra. The BaP-BkF mixture, BaP-BbF mixture and BbF-BkF mixture with similar fluorescence properties, cuckoo search algorithm (CS) optimizing support vector machine (SVM) was used to discriminate these three mixtures. By comparison with the basic grid search algorithm (GS), genetic algorithm (GA) and particle swarm optimization algorithm (PSO) optimizing SVM, it was found that fitting degree of CS was the best and the convergence speed was also the fastest. The test sample classification accuracy of CS-SVM can reach 100%, which was higher than that of GS-SVM, GA-SVM and PSO-SVM. In order to verify the validity of the proposed approach, all the above methods were applied to discriminate NAP and FLU with extremely similar spectra. The test sample classification accuracy can reach 100%. The satisfying results indicated that the proposed approach had potential to be an alternative approach for discriminating PAHs in environment.

Approach for discrimination and quantification of electroactive species: kinetics difference revealed by higher harmonics of Fourier transformed sinusoidal voltammetry.

Discrimination and quantification of electroactive species are traditionally realized by a potential difference which is mainly determined by thermodynamics. However, the resolution of this approach is limited to tens of millivolts. In this paper, we described an application of Fourier transformed sinusoidal voltammetry (FT-SV) that provides a new approach for discrimination and quantitative evaluation of electroactive species, especially thermodynamic similar ones. Numerical simulation indicates that electron transfer kinetics difference between electroactive species can be revealed by the phase angle of higher order harmonics of FT-SV, and the difference can be amplified order by order. Thus, even a very subtle kinetics difference can be amplified to be distinguishable at a certain order of harmonics. This method was verified with structurally similar ferrocene derivatives which were chosen as the model systems. Although these molecules have very close redox potential (<10 mV), discrimination and selective detection were achieved by as high as the thirteenth harmonics. The results demonstrated the feasibility and reliability of the method. It was also implied that the combination of the traditional thermodynamic method and this kinetics method can form a two-dimension resolved detection method, and it has the potential to extend the resolution of voltammetric techniques to a new level.

Characterization of Adulterated Milk by Two-Dimensional Infrared Correlation Spectroscopy

A new approach for discrimination of adulterated milk is reported using two-dimensional infrared (IR) correlation spectroscopy by multiway principal component analysis (MPCA) and least squares support vector machines (LS–SVM). First, the synchronous two-dimensional spectra of pure and adulterated milk were calculated. Then, MPCA was used to reduce the dimensions, extract features of two-dimensional correlation data set, and distinguish adulterated milk and pure milk. Finally, a LS-SVM model was developed using the scores of the first thirteen principal components from synchronous two-dimensional correlation spectra computed by MPCA as the input variables. The ratios of correct classification were 100% and 96.3% for calibration set and prediction set, respectively. The area under the receiver operating characteristic curves (ROC) of 0.991 for prediction set was obtained by LS–SVM. The results indicate that two-dimensional correlation infrared spectra combined with MPCA–LS–SVM may be a rapid screening technique for discrimination of adulterated milk with good accuracy.

A new approach for discrimination of objects on hyperspectral images

A new approach for discrimination of objects on hyperspectral images, which combines state-of-art image processing methods and multivariate image analysis, is proposed. The basic idea of the approach is to build a joint principal component space for all objects' pixels, detect patterns, pixels from a particular object shared in this space, and use quantitative evaluation of the patterns as the objects' features. The approach was particularly developed for dealing with challenging cases, when objects from different classes have many similar pixels. It has been tested on several real cases and showed very promising results.

New approach for discrimination of Vibrio vulnificus by real-time PCR before and after γ-irradiation

The effects of γ-irradiation on the destruction of Vibrio vulnificus by real-time PCR were studied. γ-irradiation was found to result in extensive reduction in the molecular size of DNA. Irradiation of viable cells (1 × 10 6 CFU/ml) at 1.08 KGy resulted in 100% destruction determined by plate counts, with most of the DNA from the irradiated cells having a bp-length of less than 1000. The use of a pair of primers to amplify a 1000-bp sequence of DNA from cells exposed to 1.08 KGy failed to yield amplification. In contrast, primers designed to amplify sequences of 700, 300, and 70-bp yielded amplification with C t values resulting in 13.4, 27.6, and 45.4% detection of genomic targets. When viable cells of V. vulnificus were exposed to 1.08, 3.0, and 5.0 kGy, the average molecular size of genomic DNA visualized in an agarose gel decreased with increasing dose, corresponding to an increased probability of amplification with primers targeting sequences of decreasing size.

Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model

Visible/near-infrared spectroscopy (NIRS), with the characteristics of high speed, non-destructiveness, high precision and reliable detection data, etc., is a pollution-free, rapid, quantitative and qualitative analysis method. A new approach for discrimination of varieties of tea by means of vis/NIR spectroscopy (325–1075 nm) was developed in this work. The relationship between the reflectance spectra and tea varieties was established. The spectral data was compressed by the wavelet transform (WT). The features from WT can be visualized in principal component (PC) space, which can lead to discovery of structures correlative with the different class of spectra samples. It appeared to provide a reasonable clustering of the varieties of tea. The scores of the first eight principal components computed by PCA had been applied as inputs to a back propagation neural network with one hidden layer. The 200 samples of eight varieties were selected randomly to build BP-ANN model. This model was used to predict the varieties of 40 unknown samples. The recognition rate of 100% was achieved. This model comes to be reliable and practicable.