Gear Fault Detection Using Recurrence Quantification Analysis and Support Vector Machine

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Journal of Software Engineering and Applications

ISSN Print: 1945-3116
ISSN Online: 1945-3124
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"Gear Fault Detection Using Recurrence Quantification Analysis and Support Vector Machine"
written by T. Haj Mohamad, Y. Chen, Z. Chaudhry, C. Nataraj,
published by Journal of Software Engineering and Applications, Vol.11 No.5, 2018

has been cited by the following article(s):

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CrossRef

[1]	Two-step vibration-based machine learning model for the fault detection and diagnosis in rotating machine and its blind application
	Sepulveda, J Sinha - Structural Health …, 2024

[2]	Experimental evaluation for detecting bevel gear failure using univariate statistical control charts
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[3]	Classification of Tomato Ripeness Based on Convolutional Neural Network Methods
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[4]	Hybrid Modeling of a Multidimensional Coupled Nonlinear System With Integration of Hamiltonian Mechanics
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[5]	Identification of wear mechanisms of main bearings of marine diesel engine using recurrence plot based on CNN model
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[6]	Fault Diagnosis of Bearings Using Recurrences and Artificial Intelligence Techniques
	Journal of Nondestructive Evaluation …, 2022

[7]	Research on Recurrence Plot Feature Quantization Method Based on Image Texture Analysis
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[8]	Integration of Nonlinear Dynamics and Machine learning for Diagnostics of a Single-Stage Gear Box
	Annual Conference of …, 2022

[9]	Early Detection of Cracks in a Gear-Train System Using Proper and Smooth Orthogonal Decompositions
	Advances in Nonlinear …, 2021

[10]	Application of Deep CNN-LSTM Network to Gear Fault Diagnostics
	2021

[11]	Proper and Smooth Orthogonal Decompositions for Detection of Inner Race Defects in Rolling Element Bearings with Variable Rotational Speeds
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[12]	New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network
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[13]	Fault identification and severity analysis of rolling element bearings using phase space topology
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[14]	A framework for Modeling flood Depth Using a Hybrid of Hydraulics and Machine Learning
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[15]	MULTI-LABEL CLASSIFIER PERFORMANCE EVALUATION WITH CONFUSION MATRIX
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[16]	Fault detection in rotating machinery by using the modal state observer approach
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[17]	A Review of Phase Space Topology Methods for Vibration-Based Fault Diagnostics in Nonlinear Systems
	2019

[18]	Proper and Smooth Orthogonal Decompositions for Detection of Inner Race Defects in Rolling Element Bearings
	Nonlinear Dynamics of Structures, Systems and Devices. Springer, Cham, 2019

[19]	An overview of PST for vibration based fault diagnostics in rotating machinery
	The 14th International Conference on Vibration Engineering and Technology of Machinery (VETOMAC XIV), 2018

[20]	Using the Gottwald and Melbourne's 0-1 test and the Hugichi fractal dimension to detect chaos in defective and healthy ball bearings
	2018

[1]	The effect of cutting parameters on defect detection based on recurrence analysis of cutting force signals obtained from GFRP composite milling Measurement, 2025 DOI:10.1016/j.measurement.2024.115406

[2]	The effect of cutting parameters on defect detection based on recurrence analysis of cutting force signals obtained from GFRP composite milling Measurement, 2025 DOI:10.1016/j.measurement.2024.115406

[3]	Physics-informed machine learning for modeling multidimensional dynamics Nonlinear Dynamics, 2024 DOI:10.1007/s11071-024-10163-3

[4]	Physics-informed machine learning for modeling multidimensional dynamics Nonlinear Dynamics, 2024 DOI:10.1007/s11071-024-10163-3

[5]	Subtractive manufacturing of composite materials with robotic manipulators: a comprehensive review The International Journal of Advanced Manufacturing Technology, 2024 DOI:10.1007/s00170-024-14427-5

[6]	Two-step vibration-based machine learning model for the fault detection and diagnosis in rotating machine and its blind application Structural Health Monitoring, 2024 DOI:10.1177/14759217241249055

[7]	Vibration Engineering and Technology of Machinery, Volume II Mechanisms and Machine Science, 2024 DOI:10.1007/978-981-99-8986-7_17

[8]	Experimental evaluation for detecting bevel gear failure using univariate statistical control charts Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2024 DOI:10.1007/s40430-024-04816-y

[9]	Vibration Engineering and Technology of Machinery, Volume II Mechanisms and Machine Science, 2024 DOI:10.1007/978-981-99-8986-7_17

[10]	Experimental evaluation for detecting bevel gear failure using univariate statistical control charts Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2024 DOI:10.1007/s40430-024-04816-y

[11]	Two-step vibration-based machine learning model for the fault detection and diagnosis in rotating machine and its blind application Structural Health Monitoring, 2024 DOI:10.1177/14759217241249055

[12]	Identification of wear mechanisms of main bearings of marine diesel engine using recurrence plot based on CNN model Wear, 2023 DOI:10.1016/j.wear.2023.204656

[13]	Hybrid modeling of a multidimensional coupled nonlinear system with integration of Hamiltonian mechanics Nonlinear Dynamics, 2023 DOI:10.1007/s11071-023-08618-0

[14]	[Retracted] Research on Recurrence Plot Feature Quantization Method Based on Image Texture Analysis Journal of Environmental and Public Health, 2022 DOI:10.1155/2022/2495024

[15]	Fault Diagnosis of Bearings Using Recurrences and Artificial Intelligence Techniques Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 2022 DOI:10.1115/1.4053773

[16]	Advances in Nonlinear Dynamics NODYCON Conference Proceedings Series, 2022 DOI:10.1007/978-3-030-81166-2_40

[17]	Research on Recurrence Plot Feature Quantization Method Based on Image Texture Analysis Journal of Environmental and Public Health, 2022 DOI:10.1155/2022/2495024

[18]	Application of Deep CNN-LSTM Network to Gear Fault Diagnostics 2021 IEEE International Conference on Prognostics and Health Management (ICPHM), 2021 DOI:10.1109/ICPHM51084.2021.9486591

[19]	Fault identification and severity analysis of rolling element bearings using phase space topology Journal of Vibration and Control, 2021 DOI:10.1177/1077546320926293

[20]	New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network Advances in Mechanical Engineering, 2020 DOI:10.1177/1687814020916593

[21]	A Framework for Modeling Flood Depth Using a Hybrid of Hydraulics and Machine Learning Scientific Reports, 2020 DOI:10.1038/s41598-020-65232-5

[22]	Nonlinear Dynamics of Structures, Systems and Devices 2020 DOI:10.1007/978-3-030-34713-0_49

[23]	New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network Advances in Mechanical Engineering, 2020 DOI:10.1177/1687814020916593

[24]	A Framework for Modeling Flood Depth Using a Hybrid of Hydraulics and Machine Learning Scientific Reports, 2020 DOI:10.1038/s41598-020-65232-5

[25]	Fault identification and severity analysis of rolling element bearings using phase space topology Journal of Vibration and Control, 2020 DOI:10.1177/1077546320926293

[26]	Fault detection in rotating machinery by using the modal state observer approach Journal of Sound and Vibration, 2019 DOI:10.1016/j.jsv.2019.06.022

[27]	A Review of Phase Space Topology Methods for Vibration-Based Fault Diagnostics in Nonlinear Systems Journal of Vibration Engineering & Technologies, 2019 DOI:10.1007/s42417-019-00157-6

[28]	An overview of PST for vibration based fault diagnostics in rotating machinery MATEC Web of Conferences, 2018 DOI:10.1051/matecconf/201821101004

[29]	Using the Gottwald and Melbourne’s 0-1 test and the Hugichi fractal dimension to detect chaos in defective and healthy ball bearings MATEC Web of Conferences, 2018 DOI:10.1051/matecconf/201824101017

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