Bridging the gap between AI and the industry — A study on bearing fault detection in PMSM-driven systems using CNN and inverter measurement-Reference-Cited by-同舟云学术

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Bridging the gap between AI and the industry — A study on bearing fault detection in PMSM-driven systems using CNN and inverter measurement

Published:2023-11 Issue: Volume:126 Page:106834
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Mueller Philipp N.,Woelfl Lukas,Can Suat

Funder

Ministerium für Wirtschaft, Innovation, Digitalisierung und Energie des Landes Nordrhein-Westfalen

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference130 articles.

1. Estimation of PC-SAFT binary interaction coefficient by artificial neural network for multicomponent phase equilibrium calculations;Abbasi;Fluid Phase Equilib.,2020

2. Wavelet analysis and its statistical applications;Abramovich;J. R. Stat. Soc. Ser. D,2000

3. Mechanical fault detection in permanent magnet synchronous motors using equal width discretization-based probability distribution and a neural network model;Akar;Turk. J. Electr. Eng. Comput. Sci.,2015

4. Fault diagnosis and fault-tolerant control for large-scale safety-critical facilities;Baoran,2018

5. Ball bearing damage detection using traditional signal processing algorithms;Bediaga;IEEE Instrum. Meas. Mag.,2013

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1. Insights into modern machine learning approaches for bearing fault classification: A systematic literature review;Results in Engineering;2024-09

2. CFENet: A contrastive frequency-sensitive learning method for gas-insulated switch-gear fault detection under varying operating conditions using acoustic signals;Engineering Applications of Artificial Intelligence;2024-09

3. Convolutional Neural Networks Based on Resonance Demodulation of Vibration Signal for Rolling Bearing Fault Diagnosis in Permanent Magnet Synchronous Motors;Energies;2024-08-29

4. Review on deep learning classifiers for faults diagnosis of rotating industrial machinery;Service Oriented Computing and Applications;2024-07-29

5. Bi-level binary coded fully connected classifier based on residual network 50 with bottom and deep level features for bearing fault diagnosis;Engineering Applications of Artificial Intelligence;2024-07

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