After passing out 12 th standard, a student has many options to choose for higher education in India. He or she can choose a career oriented course or an academic course. Choosing a career is a critical decision. Now -a-days the multicriteria decision making methods (MCDM) are gaining importance for the selection of best suited alternative among the available alternatives. In this paper multi criteria decision making methods viz. Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) were applied to choose the best career option. Multiple options have been considered as alternatives and some specific criteria were taken into account individually to select best career option. AHP is used to derive the weights of criteria and appropriate course selection and final ranking of all courses available after 12 th have been obtained from both the methods: TOPSIS and AHP. The experimental work shows that the final ranking of both the applied methods is equivalent even though the final priorities are differ in values.

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Roller bearings are essential parts extensively used in many industries such as automobile, sugar factories, cement industries, weaving mills, chemical industries, and other process industries. The catastrophic failure of such bearings results into unplanned shutdowns, discontinuity of manufacturing process, and heavy maintenance cost. The vibration analysis of the roller bearing is a vital factor in the rotating machines because its performance significantly affects the safety and operational life of the rotating machines and subsequently entire plant. The object of this paper is to study how to predict the vibration characteristics of the rotor-bearing system by using the mathematical model. In the present research work, a empirical model for the vibration characteristics of the roller bearing has been established using FLTθ system. The new mathematical model considers the influences of the bearing variables on the vibration of the rotor system. Furthermore, a new model on bearing system is carried out by using dimensional analysis (DA) and the defect frequencies and vibration characteristics of the bearing system are obtained. The effects of speed and load along with other variables on vibration characteristics have been studied by establishing an empirical model. Experiments were conducted to validate the developed empirical model. The method proposed in this paper is based on FLTθ method of DA. The vibration characteristics thus obtained provides a complete and systematic theory and technique in this aspect.

Since the last decade, gearbox systems have been requiring increasing power, and consequently, the complexity of systems has escalated. Inevitably, this complexity has resulted in the need for the troubleshooting of gearbox systems. With a growing trend of health monitoring in rotating machines, diagnostic and prognostic studies have become focused on diagnosing existing and potential failures in gearbox systems. In this context, this study develops the architecture of the cloud-based cyber-physical system for condition monitoring of gearbox. Empirically collected vibration signals of gear wear at various time intervals are processed using Empirical Mode Decomposition (EMD) algorithm. A Euclidian-based distance evaluation technique is applied to select the most sensitive features of car gear wear. Artificial Neural Network is trained using extracted features to monitor the gearbox for the future dataset. Comparison of the performance results revealed that the ANN is superior to the other EMD methods. The present methodology was found efficient and reliable for condition monitoring of industrial gearbox.

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