Condition monitoring of gearbox

Gears are one of the most important elements of rotating machineries and plays a key role in many industrial applications. If there is an unexpected failure in the gearbox it may lead to large economic losses. The fault diagnostic of rotating elements has drawn attention for its role in preventing disastrous accidents and beneficially assuring maintenance. Recently, fault diagnosis has paved its way in the multidisciplinary direction. Vibration analysis has always been a crucial component of preventative maintenance methods. and plays a significant role in assessing the health of the machinery and has supported decisions on machinery maintenance. An early fault identification of the gearbox is feasible by analyzing the vibration signal using various signal processing techniques since the vibration signal of a gearbox contains the signature of the defect in gear. This work aims to address fault diagnosis method based on vibrational analysis on gear box. Here an attempt has been made to use a diagnosis technique that when applied to gearbox highlights faults and these fault detection techniques are based on vibrational analysis approach

Designing of voice-controlled drone using BT-voice control for Arduino

The hand-free drone project aims to create a drone that can be controlled through voice commands, eliminating the need for remote control or gestures. The system uses voice recognition technology to process the commands and act accordingly, using code to control the motors and achieve the desired outcome. This technology can be used in various applications, including military, surveillance, photography, gaming, and more

Rain Fall Prediction using Ada Boost Machine Learning Ensemble Algorithm

Every government takes initiative for the well-being of their citizens in terms of environment and climate in which they live. Global warming is one of the reason for climate change. With the help of machine learning algorithms in the flash light of Artificial Intelligence and Data Mining techniques, weather predictions not only rainfall, lightings, thunder outbreaks, etc. can be predicted. Management of water reservoirs, flooding, traffic - control in smart cities, sewer system functioning and agricultural production are the hydro-meteorological factors that affect human life very drastically. Due to dynamic nature of atmosphere, existing Statistical techniques (Support Vector Machine (SVM), Decision Tree (DT) and logistic regression (LR)) fail to provide good accuracy for rainfall forecasting. Different weather features (Temperature, Relative Humidity, Dew Point, Solar Radiation and Precipitable Water Vapour) are extracted for rainfall prediction. In this research work, data analysis using machine learning ensemble algorithm like Adaptive Boosting (Ada Boost) is proposed. Dataset used for this classification application is taken from hydrological department, India from 1901-2015. Overall, proposed algorithm is feasible to be used in order to qualitatively predict rainfall with the help of R tool and Ada Boost algorithm. Accuracy rate and error false rates are compared with the existing Support Vector Machine (SVM) algorithm and the proposed one gives the better result

Habit of metro surroundings and architectural shaping of natural crystals –a techno bridge between crystallography and civil constructional engineering

A scientific platform between crystallography and architectural engineering, recovering number of issues related to the architectural challenge and protect a building from high vibration and dust stimulated from metro- environment. Issue has been controlling through architectural shaping of natural crystal and lattice environment of the minerals. Author addresses the issues on interrelationship between geomorphologic characteristics of the Karnataka in the context of the Metro region and the crystal structure which has also been fetched out from same region. The author is also addresses the combinational analysis of the architectural and space-planning design of the region

Application of derivative maps to Homotopy

The perspective of unification of mathematical concepts of Group Actions and Homotopy have been the bottom line of our study.  We exploit this to a higher degree by investigating the derivatives associated with these.  Heading in this direction, this paper is about linking the derivative of Homotopy and the derivative of Group Action.  Firstly, we verify if the derivative of a Group Action is itself a Group action and whether the derivative of a Homotopy is a Homotopy.  For this purpose, we take only special Group Actions and Homotopies restricted to the Euclidean space.  We then discuss when the derivative of Group Action is a Homotopy and vice-versa.  Thus our aim here is to find if the derivative of a Homotopy can lead to the existence of a related Group Action and the relevant criteria that must be satisfied for such a relation.   This paper also investigates when the derivative of Homotopy between two functions is a Homotopy in addition to being a Group Action as well.  The derivative of Group Action and Homotopy are dealt with in an attempt to find if the derivative of Group Action is also a Homotopy.  Since the derivative of a special action is also an action, we verify if this action is a Homotopy.  Thus we interlink the derivative of the concepts of our study as theorems/propositions.  In particular, we obtain conditions for the derivative of a Homotopy to be a Group Action.Summarizing, this paper is about finding the derivative of Group Action and Homotopy if the presence of one leads to the existence of the other and if so when

Optimization and biodegradation of chromium present in leather industrial effluents using indigenous microorganisms isolated from leather industrial sludge

Microorganisms Paracoccus pantotrophus (OP288256) and Bacillus velezensis (OP289289) are used as individual cultures and Co cultures in the biodegradation of Chromium, under different optimized conditions. Isolated microorganisms from Leather industrial sludge are used for the biodegradation of Chromium. The Amount of Chromium degradation individually by Paracoccus pantotrophus, Bacillus velezensis and Co cultures of P. pantotrophus+B.velezensis was observed at pH 7. There was a maximum degradation of chromium by P.pantotrophus, B.velezensis and P. pantotrophus+B.velezensis seen at temperature of 35°C. Chromium degradation by Paracoccus pantotrophus was higher in the media supplemented with Fructose as the carbon source, whereas Bacillus velezensis showed maximum chromium degradation in media that contained Glucose as the carbon source. Thus, Co cultures showed a significant amount of chromium degradation in media that used Glucose and Fructose as carbon source. A significant amount of chromium was degraded by P.pantotrophus in the media containing Yeast Extract as the nitrogen source, whereas degradation by Bacillus velezensis was higher in the media with Peptone and P.pantotrophus+B. velezensis showed a maximum degradation in the media with Glucose and Peptone as the Nitrogen source. More the concentration of the Inoculum added to the media, the amount of chromium degradation gradually increased by individual culture and Co cultures . Significant increase in the chromium degradation observed for the incubation from Day 7 to Day 28, by individual organism and combined cultures. Bioremediation using Co cultured bacteria is an economical and environmentally better alternative to conventional remediation methods

Review on Electric Vehicle Technologies

Society is more concern by the causes and effect of Internal Combustion (IC) engine emission on the climate and environment. The major reason due to which the automobile sector had to conceive, discover, design, build, and bring the Electric vehicle (EV) technology into existence. Electric vehicle has the potential to address greenhouse emission and also it acts as an emerging tool for reducing air pollution and providing a clean transportation system. Just in few numbers of years the rapid rise in EV technology has been observed with a huge growth and demand of public. Keeping the advantages and disadvantages in mind of EV from environmental point of view has been discussed. The most important factor for EV technology used is the batteries, hence a thorough study of battery technology- from Lithium batteries to lead acid batteries is analysed. The charging method, standards, and optimization techniques is also been discussed with the essential characteristics of EV technology used in vehicle. Further future trends, demand, supply in EV technology is provided

Structural and elastic properties of Strontium doped Phosphate bioactive glasses

Bioactive glasses are a class of biomaterials which have the potential of becoming suitable candidates for osteogenesis. The glasses made with phosphate as the former have exceptional properties. The functional characteristics are due to the lower values of viscosity and dispersion and higher values of refractive indices. These glasses show high transparency in the range of UV spectrum. Inclusion of oxides first group and second group elements i.e., alkali and alkaline earth metal oxides respectively into the glass network modify the properties and increase the chemical resistance of phosphate glasses. Different compositions of strontium doped phosphate glasses were prepared using melt quench technique. X – Ray Diffraction (XRD) studies were performed in order to confirm the amorphous nature . The effect of adding modifier into the glass matrix was evaluated using FTIR characterization. The elastic parameters like moduli and Poisson’s ratio were calculated using Makishima and Mackenzie model. It was validated that modifiers have a significant impact on glass structure and bioactivity. These glasses are found to be capable in reducing burden on metallic biomaterials for osteogenesis and hence contribute for sustainable environment

Improvement in Electrochemical Performance of Lithium Rich Li2RuO3 Cathode With Co-doping Strategy

Lithium-rich layered oxides based on Ru are interesting as cathode materials because they have high energy density and reversible capacity. However, due to the problems of weak structural stability and voltage decay, their commercial utility is limited. To address this, we use a DFT+U quantum mechanics to investigate the co-doping strategy on Li2RuO3 (LRO) for improved battery performance. In particular, the effect of two co-dopants Ti and Co has been studied. The co-doping strategy has been found to significantly improve the structural and thermal stability of LRO. By slowing the oxygen removal reaction, Ti and Co improve structural stability. Co-doping with Ti and Co increases the maximum open circuit voltage at least by 5.5% and decreases the voltage reduction by a minimum of 44%. Bandgap is also increased by a minimum of 6% with co-doping. In particular, Li2Ru0.5Ti0.375Co0.125O3  provides the highest maximum voltage of 4.4V with 61% decrease in the voltage reduction and 40% lower bandgap (0.45eV)

Prediction System for Covid-19 Upcoming Cases Using Ensemble Classification

An epidemic of the novel destructive Coronavirus has been spreading rapidly around the world since 2019 and has caused a great number of deaths. Providing patients with appropriate and most timely care is crucial to combating COVID-19 spread. Testing for the disease must be done quickly and accurately. Therefore, this paper developed an ensemble classification-based country-wise COVID-19 upcoming cases prediction model. This ensemble classification and prediction model shows the upcoming month's Corona virus cases, including newly confirmed cases, recovered cases, and deaths. This analysis is carried out based on these three cases occurring in different countries on sequential dates. The proposed model uses three famous classifiers, namely ANN, Gaussian Process and SVM which have different learning characteristics and architectures at the first stage. In the second stage, they combine their predictions with average calculations. Training and assessment of the proposed model were conducted using 75065 observations comprised of 61 features from John Hopkins University in Maryland. For data preparation, the envisioned work clusters the dataset based on world countries affected by COVID-19 separately. As a result, this set of clusters fetched data once again based on death, newly confirmed, and recovered cases. The experimental result shows the proposed ensemble model provides better performance when compared with previous classification algorithms