SPEED SENSOR FAULT DETECTION AND DIAGNOSIS OF INDUCTION MOTOR USING FUZZY BASED FAULT TOLERANT ALGORITHM

This paper presents a three phase squirrel cage induction motor operates using indirect field control is used and speed sensor fault is diagnosis by fuzzy logic control. Induction motors are highly reliable, they are susceptible to many types of faults that can became catastrophic and cause production shutdowns, personal injuries, and waste of raw material. Induction motor faults can be detected in an initial stage in order to prevent the complete failure of the system and unexpected production costs. So,new fault tolerant algorithm is used to detect the faults and  diagnosis the fault, with the help of current estimated and speed estimated blocks. Fault tolerant algorithm was used for diagnosis current and speed sensor faults.Here, IFOC techniques is used to control the switches of the inverter and system performance is analysed. Estimated current control block is used to diagnosis  speed and  current sensor  which is estimated by logic based decision algorithm. In this paper modeling and simulation of three phase induction motor was deceloped using IFOC and fuzzy based new fault tolerant algorithm is also developed by using MATLAB/SIMULINK software

Incidence of Foramen Meningo - Orbitale in South Indian Population

Foramen meningo-orbitale is a small inconsistent foramen usually found on the roof or the lateral wall of orbit forming an additional connection between the orbit and the middle cranial fossa. It is usually single but may also be multiple transmitting the orbital branch of middle meningeal artery. In the current study we investigated 97 adult dried human skulls it was found to be present in 43 skulls (44.32%), it was unilateral 27 skulls (27.83%) and found bilaterally in 16 skulls (16.49%). The incidence of this foramen may be of surgical significance for surgeries related to the anterior cranial fossa and also to ophthalmologist

Dropwise Condensation and Heat Transfer

The dropwise condensation is obtained on a copper surface by modifying the texture of the bare surface using the thermo-solution immersion method. In this method, the solution of 0.003–0.007 M of ethanol and myristic acid is used, and heating the plate in the solution at 40–65°C for 2–5 h using hot plate apparatus. The heat-transfer coefficient of the dropwise condensation is increased on the prepared superhydrophobic surface that exhibits very low surface energy causing the non-wetting nature of the water droplet on the prepared surface. The contact angle of the water droplet is measured on the obtained superhydrophobic copper surface, giving the average value of 160° ± 2° with a low-inclination angle of 2°. The maximum contact angle of 162° is obtained by adjusting the composition of the solution, the temperature of the solution, and immersion time at 0.005 M, 45°, and 3 h, respectively. Further, the prepared superhydrophobic surface is experimented with for dropwise condensation, which provides a high heat-transfer coefficient of 196 W/m2 K over the bare surface providing around 186 W/m2 K. The condensation rate of water droplet fall-off time is about 1 s on the superhydrophobic surface, and 2 s for bare surface is obtained against the mass flow rate of 300 lph

Photo-inhibition Effect from Strong Electron Withdrawing Nitro Group in N-[(E)(4-Bromophenyl)Methylidene]-4 Nitroaniline

Light induced effect of N-[(E)-(4-bromophenyl)methylidene]-4nitroaniline was investigated using UV-Vis spectrophotometer. This study revealed that the presence of strong electron withdrawing nitro group inhibited the photo-reactivity of the compound. Mainly, molecular structure and functional groups have tremendous influence on chromophoric compounds. The photoisomerization effect was not found in this compound, due to the photo-inhibition of nitro group present in the molecular system

Tourism Carrying Capacity for Beaches of South Andaman Island, India

The Andaman and Nicobar Islands (ANI) is one of the largest tourist areas in India attracting both the international and domestic tourists each year. The Island Administration has a vision to develop the islands as an upmarket island destination for ecotourism. Among the island group, the South Andaman region is the most visited tourist destination and beaches of these islands have great potential for tourism attractions. The present work is an attempt to understand the potential of these beaches by assessing the carrying capacity in terms of number of visitors that can be allowed over a period of time, which will further help with better tourism management. The methodology used to estimate the tourism carrying capacity (TCC) is based on the physical and ecological conditions of each site and the existing infrastructure. The total effective carrying capacity (ECC) estimated for the beaches of Port Blair area (126,301 visitors/day) reveals that the current tourism activity is in lower level compared to its carrying capacity. Such carrying capacity assessments can be used as an input into the regular planning process. Preliminary estimates suggest that A&N Islands can be promoted for high value-low volume, eco-friendly, and environmentally sustainable tourism

Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Crystal structure of methyl 1-methyl-2-oxospiro[indoline-3,2′-oxirane]-3′-carboxylate

Acknowledgements The authors thank Dr Babu Vargheese, SAIF, IIT, Madras, India, for the data collection.Peer reviewedPublisher PD