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Gurram M.,Kamala Institute of Technology and Science | Adepu K.,National Institute of Technology Warangal | Pinninti R.R.,Chaitanya Bharathi Institute of Technology | Gankidi M.R.,Indian Defence Research And Development Laboratory
Journal of Materials Research and Technology

The influence of grain refining elements such as copper (Cu) and aluminium (Al) on mechanical properties of AISI 430 ferritic stainless steel welds through gas tungsten arc welding process was studied. Cu (foil form) and Al powder of -100 μm mesh was added in the range from 1 to 3 g between the butt joint of ferritic stainless steel. In order to investigate the influence of post-weld heat treatment on the microstructure and mechanical properties of welds, post-weld annealing was adopted at 830 C, 30 min holding followed by water quenching. Corrosion behaviour of ferritic stainless steel welds was also studied. From this investigation, it is observed that the joints made by the addition of 2 g Al (2.4 wt.%) in post-weld annealed condition led to improved strength. There is a marginal improvement in the ductility and pitting corrosion resistance of ferritic stainless steel welds by the addition of 2 g Cu (0.18 wt.%) in post-weld annealed condition. The observed mechanical properties have been correlated with microstructure, fracture features and corrosion behaviour of ferritic stainless steel weldments. © 2013 Brazilian Metallurgical, Materials and Mining Association. Source

Pundlik Y.Y.,Kamala Institute of Technology and Science | Nirgude P.M.,Indian Central Power Research Institute
Proceedings - 2014 4th International Conference on Communication Systems and Network Technologies, CSNT 2014

Power Transformers are significant links between HVAC/HVDC transmission systems for changing the life of such aged transformers. Sometimes failure of power transformers may lead to failure of entire power grid. At present, there is large number of transformers being operated towards end of their working life. The main challenge for the High voltage substation maintenance engineers and asset managers is to extend useful life of transformers to avoid major catastrophic failure in them & resulting grid failures. A prorated model of transformer is prepared and tests were carried out. The model was subjected to accelerated aging with high voltage stress. This accelerated aging was followed by PDC test was carried out in frequency domain known as frequency domain spectroscopy (FDS). In this paper Polarization and Depolarization Current measurement (PDC) technique is applied to determine moisture content of a HV coil Model. © 2014 IEEE. Source

Sharmila V.,Kamala Institute of Technology and Science | Hari Krishna E.,Kakatiya University | Ashoka Reddy K.,Kakatiya Institute of Technology and Science
Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013

In this paper a cumulant based Teager energy operator (TEO) method is proposed to enhance the morphological features of noisy stress ECG signals which are corrupted with Gaussian noise, and other artifacts like baseline wander. It uses higher order statistical (HOS) cumulants in combination with nonlinear TEO required for the analysis of nonlinear and non stationary signals. Higher order cumulants suppress the Gaussian noise and TEO derives an energy from nonlinear components in time and frequency domains. It exhibits an attractive feature of retrieving a clean ECG from noisy recordings with extremely poor SNR of up to 10dB. The mean of Teager energy (TE) in time as well as frequency domains and improvement in signal to noise ratio (SNRI) are the statistical measures used for classification. Results reveal the efficacy of the proposed method in improving the signal to noise ratio and computing the energy in time and frequency domains. © 2013 IEEE. Source

Sharmila K.,Kamala Institute of Technology and Science | Krishna E.H.,Kakatiya University | Komalla N.R.,Government MGM Hospitals | Reddy K.A.,Kakatiya Institute of Technology and Science
MeMeA 2012 - 2012 IEEE Symposium on Medical Measurements and Applications, Proceedings

Sudden cardiac death (SCD) is a major health concern. In time domain, detection of such condition involving monitoring the 24-h ambulatory ECG is a big issue. Here, the presented work describes higher order spectral analysis carried out on the normal portion of SCD-ECG and the analyzed parameters are compared with that of healthy person ECG. The developed algorithm detects the chances of myocardial infarction in prior, on the basis of higher order spectral analysis of an SCD-ECG. Specifically, quadratic phase coupling techniques are applied on QRS complex to extract information from the SCD-ECG signal providing the basis with which a signal suggesting predisposition of the patient to suffer a cardiac arrest can be differentiated from a normal ECG signal. The algorithm requires short segments of ECG to detect the possibility of SCD. The proposed algorithm is tested on MIT-BIH database signals and the results obtained established that it is possible to analyze and predict whether an individual is susceptible to cardiac arrest or not. Certain parameters like energy are evaluated and analyzed from the normal portion of QRS complex of SCD-ECG and are compared with that of the healthy person ECG. This primitive idea can extend the research aspect in view of analyzing the ECG signal to identify the predisposition to other cardiac diseases. © 2012 IEEE. Source

Sharmila K.,Kamala Institute of Technology and Science | Krishna E.H.,Kakatiya University | Reddy K.N.,Government MGM Hospitals | Reddy K.A.,Kakatiya University
Conference Record - IEEE Instrumentation and Measurement Technology Conference

The electrocardiogram (ECG) is an electrical manifestation of contractile activity of the heart. The presence of parasite interference signals, like power line interference (PLI), Electromyogram (EMG) or myopotential signals and baseline drift interferences, could cause serious problems in the registration of ECG signals. Many a time, they pose problem in modern control and signal processing applications by being a narrow in-band interference near the frequencies carrying crucial information. The wavelet transform is a powerful time-frequency analysis tool analysis of complex non stationary signals. Principle Component Analysis (PCA) is a standard tool in modern data analysis because it is simple non-parametric method for extracting relevant information from complex data sets. In multi scale Principal Component Analysis (MSPCA), the PCA's ability to decorrelate the variables by extracting a linear relationship and wavelet analysis are utilised. This paper presents MSPCA method proposed for the enhancement of ECG signals, so that the enhanced signal can be used for clear identification of arrhythmias. In MSPCA, the principal components of the wavelet coefficients of the ECG data at each scale are computed forst and are then combined at relevant scales. In this application for ECG enhancement, the MSPCA served as a powerful tool when addressing problems related to noise reduction. Results revealed that Daubenchies based MSPCA out performed the basic wavelet based processing for ECG signal enhancement. © 2011 IEEE. Source

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