Titlebook: Vibration Engineering and Technology of Machinery, Volume I; Select Proceedings o Rajiv Tiwari,Y. S. Ram Mohan,Mayank Tiwari Conference pro

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,Performance Enhancement of Bistable Energy Harvester with Asymmetric Potential Function Using an Eles. However, their energy harvesting capabilities are limited. Performance enhancement methods for bistable energy harvesters (BEHs) with symmetric potential well have been investigated in detail in the literature. Due to the eccentricity of the buckled beam and repulsive forces due to magnetic inte

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Vibration Reduction in Ambulance Using Modified Stretcher with Vibration Absorber,ed stretcher which will reduce the harmful vibrations which are transmitted to the stretcher from suspension system of the current ambulance. Vibration data is collected experimentally from the stretcher and chassis of a moving ambulance for different road conditions. This data is then used for anal

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Experimental Identification of Unbalance and Crack Parameters in an Internally Damped Rotor System l role in highly reliable operations without intervening in the machine’s rundown time. In the present work, experimental identification of dynamic characteristic parameters of active magnetic bearing (AMB) together with other fault parameters, such as the additive stiffness of fatigue crack, the in

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Effect of Bluff Body on the Performance of Flutter Based Energy Harvester,vester. A cantilever flexible plate is used for the wind tunnel experiment, and PZT types piezoelectric material is employed for energy harvesting purposes. A high-speed camera is used to visualize the vibration of the flexible plate, and displacement is sensed using a piezoelectric transducer. It h

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A Systematic Review of Rotor Unbalance Diagnosis in Rotating Machinery Based on Machine Learning Al To date, various machine learning algorithms have been proposed in the literature as they have proven to be fruitful in detecting rotor unbalance and this research topic is fast-growing. Therefore, this present paper aims at a comprehensive review of the development and advancement of machine learn