Magnetoresistance sensor-based rotor fault detection in induction motor using non-decimated wavelet and streaming data

In this paper, the giant magnetoresistance broken rotor (GBR) method is used to diagnose the induction motor (IM) rotor bar fault at an early stage from outward magnetic flux developed by IM.The outward magnetic field signal has anti-clockwise radiation due to broken rotor bar current.In this paper, the outward magnetic signal is acquired using a giant magnetoresistance (GMR) sensor. In the GBR method, IM rotor fault is analysed with a non-decimated wavelet transform (NDWT)-based outward magnetic signal. Experimental result shows the difference in statistical features and energy levels of sub-bands of NDWT for healthy and faulty IM. Least square-support vector machine(LS-SVM)-based classification results are verified by confusion matrix based on 150 outward magnetic signals from a healthy and damaged rotor (broken rotor). The proposed method identifies IM rotor faults with 95% sensitivity, 90% specificity and 92.5% classification accuracy. Furthermore, run-time IM condition monitoring is performed through the ThinkSpeak internet of things (IoT) platform for collecting outer magnetic signal data. ThinkSpeak streaming data of outward magnetic field help detect rotor fault at the initial stage and understand the growth of rotor fault in the motor. The proposed GBR method overcomes sensitivity, translation-invariance limitations of existing IM rotor fault diagnosis methods

BRAZILIAN ARCHIVES OF BIOLOGY AND TECHNOLOGY A N I N T E R N A T I O N A L J O U R N A L Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation

ABSTRACT The robustness and speed of image classification is still a challenging task in satellite image processing. This paper introduces a novel image classification technique that uses the particle filter framework (PFF)-based optimisation technique for satellite image classification. The framework uses a template-matching algorithm, comprising fast marching algorithm (FMA) and level set method (LSM)-based segmentation which assists in creating the initial templates for comparison with other test images. The created templates are trained and used as inputs for the optimisation. The optimisation technique used in this proposed work is multikernel sparse representation (MKSR). The combined execution of FMA, LSM, PFF and MKSR approaches has resulted in a substantial reduction in processing time for various classes in a satellite image which is small when compared wit

ANTIOXIDANT, FREE RADICAL SCAVENGING ACTIVITY AND GC-MS STUDIES ON PEDILANTHUS TITHYMALOIDES (L.) POIT

Objective: To evaluate the methanolic extract of the leaves of Pedilanthus tithymaloides for total phenol, total flavonoid, total antioxidant and free radical scavenging ability and detect the phytoconstituents using GC-MS. Methods: The total phenols were quantified using Folin-Ciocalteu method. Aluminium chloride method and Phosphomolybdenum method were used to quantify total flavonoid and total antioxidant contentrespectively. In addition to the above, Ferric thiocyanate assay, the thiobarbituric acid assay,Ferric Reducing Antioxidant Power assay and ABTS assay were performed to know the antioxidant potency of the methanolic extract of leaves of Pedilanthus tithymaloides. The phytoconstituents was detected using GC-MS. Results: The leaves of Pedilanthus tithymaloides recorded a phenolic content of 10.98±0.08 mg TAE/g DW, flavonoid content of 11.49±0.15 µg QE/g DW and total antioxidant content of 6.64±0.05 mg TAE/g DW. The study also revealed significant free radical scavenging ability of the plant leaves as assessed by FTC, TBA, FRAP and ABTS assays. The structural elucidation by GC-MS analysis revealed five different compounds, includingthree esters, an amine and an alkaloid. Conclusion: The study proves the anticipative potential ability of Pedilanthus tithymaloides, suggesting its exploitation in pharmaceutical applications

Computational analysis of third-grade liquid flow with cross diffusion effects: application to entropy modeling

The key goal of this current study is to analyze the entropy generation with cross diffusion effects. The third-grade type non-Newtonian fluid model is used in this study. The current flow problem is modelled with stretching plate. Modified Fourier heat flux is replaced the classical heat flux. The appropriate transformation is availed to convert the basic boundary layers equations into ODEs and then verified by homotopy algorithm. The consequences of various physical quantities on temperature, velocity, entropy and concentration profile are illustrated graphically

Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation

ABSTRACT The robustness and speed of image classification is still a challenging task in satellite image processing. This paper introduces a novel image classification technique that uses the particle filter framework (PFF)-based optimisation technique for satellite image classification. The framework uses a template-matching algorithm, comprising fast marching algorithm (FMA) and level set method (LSM)-based segmentation which assists in creating the initial templates for comparison with other test images. The created templates are trained and used as inputs for the optimisation. The optimisation technique used in this proposed work is multikernel sparse representation (MKSR). The combined execution of FMA, LSM, PFF and MKSR approaches has resulted in a substantial reduction in processing time for various classes in a satellite image which is small when compared with Support Vector Machine (SVM) and Independent Component Discrimination Analysis (ICDA)based image classifications obtained for comparison purposes. This study aims to improve the robustness of image classification based on overall accuracy (OA) and kappa coefficient. The variation of OA with this technique, between different classes of a satellite image, is only10%, whereas that with the SVM and ICDA techniques is more than 50%