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Sepehr Maleki
Sepehr Maleki
University of Southampton, University of Lincoln, Siemens
Bestätigte E-Mail-Adresse bei lincoln.ac.uk
Titel
Zitiert von
Zitiert von
Jahr
Unsupervised anomaly detection with LSTM autoencoders using statistical data-filtering
S Maleki, S Maleki, NR Jennings
Applied Soft Computing 108, 107443, 2021
1012021
Development and Realisation of Changepoint Analysis for the Detection of Emerging Faults on Industrial Systems
S Maleki, C Bingham, Y Zhang
IEEE Transactions on Industrial Informatics 12 (3), 1180-1187, 2016
322016
Failure identification for 3D linear systems
S Maleki, P Rapisarda, L Ntogramatzidis, E Rogers
Multidimensional Systems and Signal Processing 26, 481-502, 2015
122015
A geometric approach to 3D fault identification
S Maleki, P Rapisarda, L Ntogramatzidis, E Rogers
nDS'13; Proceedings of the 8th International Workshop on Multidimensional …, 2013
92013
Performance analysis of a twin-shaft gas turbine with fault in the variable stator guide vane system of the axial compressor
S Cruz-Manzo, S Maleki, V Panov, F Agbonzikilo, Y Zhang, A Latimer
Proceedings of the 9th International Gas Turbine Conference, Brussels …, 2018
62018
Analysis of performance of a twin-shaft gas turbine during hot-end damage in the gas generator turbine
S Cruz-Manzo, S Maleki, V Panov, F Agbonzikilo, Y Zhang, A Latimer
Turbo Expo: Power for Land, Sea, and Air 51128, V006T05A010, 2018
62018
Robust hierarchical clustering for novelty identification in sensor networks: With applications to industrial systems
S Maleki, C Bingham
Applied Soft Computing 85, 105771, 2019
52019
Performance analysis of a twin shaft Industrial Gas Turbine at fouling conditions
S Cruz-Manzo, S Maleki, Y Zhang, V Panov, A Latimer
2017 IEEE International Conference on Prognostics and Health Management …, 2017
52017
Failure identification for linear repetitive processes
S Maleki, P Rapisarda, E Rogers
Multidimensional Systems and Signal Processing 26, 1037-1059, 2015
52015
A one-class clustering technique for novelty detection and isolation in sensor networks
S Maleki, C Bingham
2017 IEEE International Conference on Computational Intelligence and Virtual …, 2017
42017
Model-based compensation of sensor failure in industrial gas turbine
V Panov, S Maleki
Proceedings of the 1st Global Power and Propulsion Forum, GPPF, 2017
32017
Operational pattern analysis for predictive maintenance scheduling of industrial systems
Y Zhang, C Bingham, M Gallimore, S Maleki
IEEE International Conference on Computational Intelligence and Virtual …, 2015
32015
Review evolution of dual-resource-constrained scheduling problems in manufacturing systems: modeling and scheduling methods’ trends
A Delgoshaei, MKAM Ariffin, S Maleki, Z Leman
Soft Computing 27 (24), 18489-18528, 2023
12023
Performance analysis and prediction of compressor fouling condition for a twin-shaft engine
S Maleki, S Cruz-Manzo, C Bingham, V Panov
GPPS-2018-035, 2nd Global Power and Propulsion Forum 2018, 10–12 Jan, 2018 …, 2018
12018
A geometric approach to fault identification in linear repetitive processes
S Maleki, Z Shang, P Rapisarda
12014
A music recommender system based on compact convolutional transformers
N Pourmoazemi, S Maleki
Expert Systems with Applications, 124473, 2024
2024
C (NN) FD-a deep learning framework for turbomachinery CFD analysis
G Bruni, S Maleki, SK Krishnababu
IEEE Transactions on Industrial Informatics, 2024
2024
C (NN) FD--deep learning predictions of tip clearance variations on multi-stage axial compressors aerodynamic performance
G Bruni, S Maleki, SK Krishnababu
arXiv preprint arXiv:2310.04264, 2023
2023
Deep learning modelling of tip clearance variations on multi-stage axial compressors aerodynamics
G Bruni, S Maleki, SK Krishnababu
arXiv e-prints, arXiv: 2310.04264, 2023
2023
Performance analysis of a twin-shaft gas turbine with fault in the variable stator guide vane system of the axial compressor
A Latimer, F Agbonzikilo, S Cruz-Manzo, S Maleki, V Panov, Y Zhang
University of Lincoln, 2018
2018
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