Konstantinos C. Gryllias

Orcid: 0000-0002-8703-8938

According to our database1, Konstantinos C. Gryllias authored at least 13 papers between 2009 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
A Few-Shot Machinery Fault Diagnosis Framework Based on Self-Supervised Signal Representation Learning.
IEEE Trans. Instrum. Meas., 2024

2023
Deep continual transfer learning with dynamic weight aggregation for fault diagnosis of industrial streaming data under varying working conditions.
Adv. Eng. Informatics, January, 2023

2022
Simulation-Driven Domain Adaptation for Rolling Element Bearing Fault Diagnosis.
IEEE Trans. Ind. Informatics, 2022

Automatic multi-differential deep learning and its application to machine remaining useful life prediction.
Reliab. Eng. Syst. Saf., 2022

2021
Cyclostationary Analysis of Irregular Statistical Cyclicity and Extraction of Rotating Speed for Bearing Diagnostics With Speed Fluctuations.
IEEE Trans. Instrum. Meas., 2021

2020
Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery.
IEEE Trans. Instrum. Meas., 2020

Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network.
IEEE Trans. Ind. Informatics, 2020

2019
A general anomaly detection framework for fleet-based condition monitoring of machines.
CoRR, 2019

Gearbox Fault Diagnosis Using Convolutional Neural Networks And Support Vector Machines.
Proceedings of the 27th European Signal Processing Conference, 2019

2018
Advanced cyclostationary-based analysis for condition monitoring of complex systems.
Proceedings of the 26th European Signal Processing Conference, 2018

2012
A Support Vector Machine approach based on physical model training for rolling element bearing fault detection in industrial environments.
Eng. Appl. Artif. Intell., 2012

2011
Rolling element bearing fault detection in industrial environments based on a K-means clustering approach.
Expert Syst. Appl., 2011

2009
A Peak Energy Criterion (P. E.) for the Selection of Resonance Bands in Complex Shifted Morlet Wavelet (Csmw) Based Demodulation of Defective Rolling Element Bearings Vibration Response.
Int. J. Wavelets Multiresolution Inf. Process., 2009


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