Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 188, P. 109929 - 109929
Published: Jan. 25, 2024
Language: Английский
Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 188, P. 109929 - 109929
Published: Jan. 25, 2024
Language: Английский
Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 121, P. 105961 - 105961
Published: Feb. 14, 2023
Language: Английский
Citations
145Applied Soft Computing, Journal Year: 2022, Volume and Issue: 127, P. 109419 - 109419
Published: Aug. 2, 2022
Language: Английский
Citations
133Information Sciences, Journal Year: 2022, Volume and Issue: 612, P. 576 - 593
Published: Sept. 6, 2022
Language: Английский
Citations
123Information Sciences, Journal Year: 2022, Volume and Issue: 619, P. 2 - 18
Published: Nov. 11, 2022
Language: Английский
Citations
119Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 121, P. 105942 - 105942
Published: Feb. 9, 2023
Language: Английский
Citations
104Information Sciences, Journal Year: 2023, Volume and Issue: 635, P. 328 - 344
Published: March 30, 2023
Language: Английский
Citations
83Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 121, P. 106004 - 106004
Published: Feb. 25, 2023
Language: Английский
Citations
74Knowledge-Based Systems, Journal Year: 2022, Volume and Issue: 256, P. 109846 - 109846
Published: Sept. 3, 2022
Language: Английский
Citations
73Information Sciences, Journal Year: 2022, Volume and Issue: 624, P. 110 - 127
Published: Dec. 27, 2022
Language: Английский
Citations
67Nondestructive Testing And Evaluation, Journal Year: 2022, Volume and Issue: 38(2), P. 275 - 296
Published: Sept. 5, 2022
The occurrence of multiple faults is a practical problem in the bearings rotating machines, and early diagnosis such issues an intelligent manner vital era industry 4.0. present work investigated various combinations bearing faults, including dual fault conditions. Two prevalent methods were employed: vibration monitoring using time-frequency scalograms extracted through Continuous Wavelet Transform (CWT) non-invasive Infrared Thermography (IRT). A 2-D Convolutional Neural Network (CNN) was used to classify conditions automated feature extraction. proposed methodology validated at two constant speed 19 Hz 29 continuously accelerated decelerated range - Hz. Adequate accuracy achieved both case vibration-based diagnosis, with 99.39 % 99.97 %. Meanwhile, IRT-based 100 classification for all These results signify robustness reliability varying
Language: Английский
Citations
65