Browsing by Author "Chavan, P."

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Development of psychoacoustics index for motorcycle exhaust noise by multiple regression method
(Elsevier Ltd, 2023) Anas, D.; Joladarashi, S.; Kadoli, R.; Chavan, P.; Bhangale, R.
Exhaust sound is an essential characteristic of a motorcycle when it comes to the interest of young motorcycle riders. It has become such an essential characteristic that riders, after looking into the performance, style, and ergonomics, look into the exhaust sound produced by the muffler. It has thus become critical for motorcycle manufacturers to tune their mufflers in such a way that they are appealing to the customers while adhering to sound emission regulations. As per the current trend of Indian motorcycle rider's scenario, more attention is given to sound attributes like beat feeling, sportiness, and pleasantness. This research paper aims to develop a psychoacoustic model that can rate the exhaust sound of motorcycles targeting the Indian customer's preferences and interests with regard to the engine exhaust sound. Presently, a study has been carried out on beat feeling, sportiness, and pleasantness, a perceivable attribute of sound in the family of motorcycles. Motorcycle buyers often perceive the exhaust sound of motorcycles in idling conditions at some distance away from the muffler, which formed the basis while recording exhaust sounds. Initially, exhaust sounds were recorded, which led to the calculation of psychometrics as objective variables. The calculated objective variables were given as input to the multiple regression model. A jury panel consisting of experienced NVH professionals and riders evaluated these sounds on a 7-point evaluation scale. These subjective ratings were given as the target variable to the multiple regression model. The obtained model was later validated with subjective data of motorcycles, including those at the prototype stage. Thus, a model has been established to aid exhaust designers in comparing their motorcycle at different prototype stages with the competitor's vehicle, allowing them to make an early decision with respect to the muffler design modifications, thereby reducing the time-consuming and expensive jury tests. Â© 2022
Helical Gearbox Fault Diagnosis Using Adaptive Artificial Neural Network and Adaptive Coyote Optimization
(Institute of Electrical and Electronics Engineers Inc., 2023) Bokil, P.P.; Joladarashi, S.; Kadoli, R.; Chavan, P.; Bhangale, R.
The Helical gearboxes (HG) are considered a significant part of providing power transmission of manufacturing administrations and are exposed to numerous failures because of their extended and intensive situation of acceleration. Therefore, to enhance the security and dependency of the HGs, monitoring the health condition and detecting different types of failures is essential. The estimation of HG failure detection majorly includes electric signals, the noise produced by airborne, lubricant examination, thermal images, and so on. Therefore, this research proposes an Adaptive Coyote Optimization-Adaptive Artificial Neural Network (A2CO-ANN) Gearbox fault diagnosis and missing data imputation for preventing the loss of significant data values. Moreover, the comparative analysis of the A2CO-ANN technique is examined using the available datasets DTS1 and DTS2 with the existing classifiers like Random Forest (RF), K-Nearest Neighbors (KNN), Decision tree (DT), Fuzzy, as well as Adaptive ANN is examined in terms of the performance metrics. Thus, the accuracy of the A2CO-ANN method on training percentage 80 for DTS1 and DTS2 is 91.54% and 90.05%, whereas the sensitivity rate is estimated as 98.26% and 98.35%, as well as the specificity rate, is valued as 84.08% and 81.09% respectively, which is increased than the traditional methods. Â© 2023 IEEE.