Dr.Vinay Vakharia
Dr.Vinay Vakharia
Other namesV Vakharia
Pandit Deendayal Energy University Gandhinagar
Verified email at - Homepage
Cited by
Cited by
A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings
V Vakharia, VK Gupta, PK Kankar
Journal of Vibration and Control 21 (16), 3123-3131, 2015
A comparison of feature ranking techniques for fault diagnosis of ball bearing
PKK V. Vakharia, V.K. Gupta
Soft computing 20, 1601–1619, 2016
Prediction of compressive strength and portland cement composition using cross-validation and feature ranking techniques
V Vakharia, R Gujar
Construction and Building Materials 225, 292-301, 2019
Efficient fault diagnosis of ball bearing using ReliefF and Random Forest classifier
V Vakharia, VK Gupta, PK Kankar
Journal of the Brazilian Society of Mechanical Sciences and Engineering 39 …, 2017
Ball bearing fault diagnosis using supervised and unsupervised machine learning methods
V Vakharia, VK Gupta, PK Kankar
Int J Acoust Vib 20 (4), 244-250, 2015
Bearing fault diagnosis using feature ranking methods and fault identification algorithms
V Vakharia, VK Gupta, PK Kankar
Procedia Engineering 144, 343-350, 2016
Tool wear prediction in face milling of stainless steel using singular generative adversarial network and LSTM deep learning models
M Shah, V Vakharia, R Chaudhari, J Vora, DY Pimenov, K Giasin
The International Journal of Advanced Manufacturing Technology 121 (1), 723-736, 2022
Fault severity classification of ball bearing using SinGAN and deep convolutional neural network
P Akhenia, K Bhavsar, J Panchal, V Vakharia
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of …, 2022
Prediction and validation of alternative fillers used in micro surfacing mix-design using machine learning techniques
R Gujar, V Vakharia
Construction and Building Materials 207, 519-527, 2019
Estimation of lithium-ion battery discharge capacity by integrating optimized explainable-AI and stacked LSTM model
V Vakharia, M Shah, P Nair, H Borade, P Sahlot, V Wankhede
Batteries 9 (2), 125, 2023
Experimental investigations and prediction of WEDMed surface of Nitinol SMA using SinGAN and DenseNet deep learning model
V Vakharia, J Vora, S Khanna, R Chaudhari, M Shah, DY Pimenov, ...
journal of materials research and technology 18, 325-337, 2022
Detection of compound faults in ball bearings using multiscale-SinGAN, heat transfer search optimization, and extreme learning machine
V Suthar, V Vakharia, VK Patel, M Shah
Machines 11 (1), 29, 2022
Bandgap prediction of metal halide perovskites using regression machine learning models
V Vakharia, IE Castelli, K Bhavsar, A Solanki
Physics Letters A 422, 127800, 2022
A comparative study to predict bearing degradation using discrete wavelet transform (DWT), tabular generative adversarial networks (TGAN) and machine learning models
K Bhavsar, V Vakharia, R Chaudhari, J Vora, DY Pimenov, K Giasin
Machines 10 (3), 176, 2022
Diagnosis of bearing faults using multi fusion signal processing techniques and mutual information
V Dave, S Singh, V Vakharia
NISCAIR-CSIR, India, 2020
Texture Classification of Machined Surfaces Using Image Processing and Machine Learning Techniques
MBK Dhiren R Patel, Vinay Vakharia
FME Transaction 47 (4), 865-872, 2019
Modeling and prediction of surface roughness using multiple regressions: A noncontact approach
DR Patel, MB Kiran, V Vakharia
Engineering Reports 2 (2), e12119, 2020
Surface roughness prediction of machined components using gray level co-occurrence matrix and Bagging Tree
DR Patel, H Thakker, MB Kiran, V Vakharia
FME Transactions 48 (2), 468-475, 2020
Feature extraction and classification of machined component texture images using wavelet and artificial intelligence techniques
V Vakharia, MB Kiran, NJ Dave, U Kagathara
2017 8th International Conference on Mechanical and Aerospace Engineering …, 2017
Application of chi square feature ranking technique and random forest classifier for fault classification of bearing faults
V Vinay, GV Kumar, KP Kumar
Proceedings of the 22th international congress on sound and vibration …, 2015
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