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Dr. Purushottam Gangsar
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Year
Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review
P Gangsar, R Tiwari
Mechanical systems and signal processing 144, 106908, 2020
3672020
Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine …
P Gangsar, R Tiwari
Mechanical Systems and Signal Processing 94, 464-481, 2017
1442017
A support vector machine based fault diagnostics of Induction motors for practical situation of multi-sensor limited data case
P Gangsar, R Tiwari
Measurement 135, 694-711, 2019
682019
Artificial intelligence application in fault diagnostics of rotating industrial machines: A state-of-the-art review
V Singh, P Gangsar, R Porwal, A Atulkar
Journal of Intelligent Manufacturing 34 (3), 931-960, 2023
512023
Multifault diagnosis of induction motor at intermediate operating conditions using wavelet packet transform and support vector machine
P Gangsar, R Tiwari
Journal of Dynamic Systems, Measurement, and Control 140 (8), 081014, 2018
442018
Diagnostics of mechanical and electrical faults in induction motors using wavelet-based features of vibration and current through support vector machine algorithms for various …
P Gangsar, R Tiwari
Journal of the Brazilian Society of Mechanical Sciences and Engineering 41 …, 2019
362019
Multiclass fault taxonomy in rolling bearings at interpolated and extrapolated speeds based on time domain vibration data by SVM algorithms
P Gangsar, R Tiwari
Journal of Failure Analysis and Prevention 14, 826-837, 2014
252014
Taxonomy of induction-motor mechanical-fault based on time-domain vibration signals by multiclass SVM classifiers
P Gangsar, R Tiwari
Intelligent Industrial Systems 2, 269-281, 2016
242016
Artificial neural network–based fault diagnosis for induction motors under similar, interpolated and extrapolated operating conditions
A Chouhan, P Gangsar, R Porwal, CK Mechefske
Noise & Vibration Worldwide 52 (10), 323-333, 2021
172021
Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions
A Chouhan, P Gangsar, R Porwal, CK Mechefske
Vibroengineering Procedia 30, 55-60, 2020
172020
Online diagnostics of mechanical and electrical faults in induction motor using multiclass support vector machine algorithms based on frequency domain vibration and current signals
P Gangsar, R Tiwari
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B …, 2019
162019
Unbalance detection in rotating machinery based on support vector machine using time and frequency domain vibration features
P Gangsar, RK Pandey, M Chouksey
Noise & Vibration Worldwide 52 (4-5), 75-85, 2021
152021
A review on deep learning based condition monitoring and fault diagnosis of rotating machinery
P Gangsar, AR Bajpei, R Porwal
Noise & vibration worldwide 53 (11), 550-578, 2022
82022
Diagnostics of combined mechanical and electrical faults of an electromechanical system for steady and ramp-up speeds
P Gangsar, M Chouksey, A Parey, Z Ali
Journal of Vibration Engineering & Technologies 10 (4), 1431-1450, 2022
52022
Effect of noise on support vector machine based fault diagnosis of IM using vibration and current signatures
P Gangsar, R Tiwari
MATEC Web of Conferences 211, 03009, 2018
52018
Analysis of Time, frequency and wavelet based features of vibration and current signals for fault diagnosis of induction motors using SVM
P Gangsar, R Tiwari
Gas Turbine India Conference 58516, V002T05A027, 2017
52017
Performance analysis of support vector machine and wavelet packet transform based fault diagnostics of induction motor at various operating conditions
P Gangsar, R Tiwari
Proceedings of the 10th International Conference on Rotor Dynamics–IFToMM …, 2019
42019
An intelligent and robust fault diagnostics for an electromechanical system using vibration and current signals
P Gangsar, Z Ali, M Chouksey, A Parey
Recent Advances in Manufacturing, Automation, Design and Energy Technologies …, 2022
32022
An Intelligent and Robust Fault Diagnostics for an Electromechanical System using Vibration and Current Signals
AP Purushottam Gangsar, Zeeshan Ali, Manoj Chouksey
International Conference on Future Technology (ICOFT)-2020, NIT, Puducherry, 2020
12020
Intelligent Diagnosis for Fuel Line Fault of Diesel Engine Based on Vibration Signatures
P Chaudhari, P Gangsar, N Dharmadhikari, S Pawar, D Mandke
SAE Technical Paper, 2024
2024
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