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Remaining useful life estimation based on nonlinear feature reduction and support vector regression

Engineering applications of artificial intelligence, 2013-08, Vol.26 (7), p.1751-1760

2013

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Health assessment and life prediction of cutting tools based on support vector regression

Journal of intelligent manufacturing, 2015-04, Vol.26 (2), p.213-223

2015

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Advancing Damage Assessment of CFRP-Composite through BILSTM and Hilbert Upper Envelope Analysis

Russian journal of nondestructive testing, 2023-12, Vol.59 (12), p.1241-1258

2023

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Nondestructive Testing of Composite Materials: An In-Depth Study Using Ultrasonic Immersion Inspection

Russian journal of nondestructive testing, 2024-07, Vol.60 (7), p.750-763

2024

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Tool wear condition monitoring based on continuous wavelet transform and blind source sep

International journal of advanced manufacturing technology, 2018, Vol.97 (7)

2018

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Deep convolutional neural networks for Bearings failure predictionand temperature correlation

Journal of Vibroengineering, 2018-12, Vol.20 (8), p.2878-2891

2018

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Remaining useful life prediction of cutting tools using wavelet packet transform and extreme learning machine

Algerian Journal of Signals and Systems, 2018-12, Vol.3 (4), p.156-165

2018

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Fault prognostic of bearings by using support vector data description

2012 IEEE Conference on Prognostics and Health Management, 2012, p.1-7

2012

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Deep Learning for Fault Diagnosis based on short-time Fourier transform

2018 International Conference on Smart Communications in Network Technologies (SaCoNeT), 2018, p.288-293

2018

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A data-driven prognostic approach based on wavelet transform and extreme learning machine

2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B), 2017, p.1-4

2017

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A hybrid CNN-BiLSTM approach-based variational mode decomposition for tool wear monitoring

International journal of advanced manufacturing technology, 2022-03, Vol.119 (5-6), p.3803-3817

2022

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Tool wear condition monitoring based on continuous wavelet transform and blind source separation

International journal of advanced manufacturing technology, 2018-08, Vol.97 (9-12), p.3311-3323

2018

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Bearing Fault Event-Triggered Diagnosis Using a Variational Mode Decomposition-Based Machine Learning Approach

IEEE transactions on energy conversion, 2022-03, Vol.37 (1), p.466-474

2022

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Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition

International journal of advanced manufacturing technology, 2021-05, Vol.114 (1-2), p.145-157

2021

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Advancing bearing fault diagnosis under variable working conditions: a CEEMDAN-SBS approach with vibro-electric signal integration

International journal of advanced manufacturing technology, 2024-05, Vol.132 (5-6), p.2753-2772

2024

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Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Entropy (Basel, Switzerland), 2021-06, Vol.23 (6), p.697

2021

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Crack localisation in composite cantilever beams using natural frequency measurements

Nondestructive testing and evaluation, 2024-10, Vol.39 (7), p.1807-1830

2024

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Damage assessment of composite material based on variational mode decomposition and BiLSTM

International journal of advanced manufacturing technology, 2023-11, Vol.129 (3-4), p.1801-1815

2023

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A hybrid approach for gearbox fault diagnosis based on deep learning techniques

International journal of advanced manufacturing technology, 2024-07, Vol.133 (5-6), p.2861-2874

2024

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New health indicators for the monitoring of bearing failures under variable loads

Structural health monitoring, 2024-09, Vol.23 (5), p.2922-2941

2024

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ANFIS-based Framework for the Prediction of Bearing’s Remaining Useful Life

International journal of prognostics and health management, 2024-01, Vol.15 (1)

2024

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Rotating machine bearing health prognosis using a data driven approach based on KS‐density and BiLSTM

IET science, measurement & technology, 2024-08

2024

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Enhancing bearing and gear fault diagnosis: A VMD-PSO approach with multisensory signal integration

Journal of vibration and control, 2024-09

2024

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Tool wear condition monitoring based on wavelet transform and improved extreme learning machine

Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science, 2020-03, Vol.234 (5), p.1057-1068

2020

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Crack Detection through the Change in the Normalized Frequency Shape

Vibration, 2018-09, Vol.1 (1), p.56-68

2018

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Remaining useful life estimation based on nonlinear feature reduction and support vector regression

Health assessment and life prediction of cutting tools based on support vector regression

Advancing Damage Assessment of CFRP-Composite through BILSTM and Hilbert Upper Envelope Analysis

Nondestructive Testing of Composite Materials: An In-Depth Study Using Ultrasonic Immersion Inspection

Tool wear condition monitoring based on continuous wavelet transform and blind source sep

Deep convolutional neural networks for Bearings failure predictionand temperature correlation

Remaining useful life prediction of cutting tools using wavelet packet transform and extreme learning machine

Fault prognostic of bearings by using support vector data description

Deep Learning for Fault Diagnosis based on short-time Fourier transform

A data-driven prognostic approach based on wavelet transform and extreme learning machine

A hybrid CNN-BiLSTM approach-based variational mode decomposition for tool wear monitoring

Tool wear condition monitoring based on continuous wavelet transform and blind source separation

Bearing Fault Event-Triggered Diagnosis Using a Variational Mode Decomposition-Based Machine Learning Approach

Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition

Advancing bearing fault diagnosis under variable working conditions: a CEEMDAN-SBS approach with vibro-electric signal integration

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Crack localisation in composite cantilever beams using natural frequency measurements

Damage assessment of composite material based on variational mode decomposition and BiLSTM

A hybrid approach for gearbox fault diagnosis based on deep learning techniques

New health indicators for the monitoring of bearing failures under variable loads

ANFIS-based Framework for the Prediction of Bearing’s Remaining Useful Life

Rotating machine bearing health prognosis using a data driven approach based on KS‐density and BiLSTM

Enhancing bearing and gear fault diagnosis: A VMD-PSO approach with multisensory signal integration

Tool wear condition monitoring based on wavelet transform and improved extreme learning machine

Crack Detection through the Change in the Normalized Frequency Shape

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