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Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 4, doi: 10.1007/s11465-022-0713-3

Abstract: Gearbox fault diagnosis based on vibration sensing has drawn much attention for a long time.Therefore, using gearbox housing vibration signal to identify gear meshing excitation signal is of greatNumerical simulation and experimental results demonstrate the proposed method can realize gear faultdiagnosis better than the original housing vibration signal and has the potential to be generalized toThe identified GMS has a clear physical meaning and is thus very useful for fault diagnosis of the complicated

Keywords： gearbox fault diagnosis     meshing stiffness     identification     transfer path     signal processing    

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Intelligent fault diagnostic system based on RBR for the gearbox of rolling mills

Lixin GAO, Lijuan WU, Yan WANG, Houpei WEI, Hui YE

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 4,   Pages 483-490 doi: 10.1007/s11465-010-0118-6

Abstract: This paper presents an intelligent system that is necessary for diagnostic accuracy and efficiency in the iron and steel industry. A rule-based reseaning (RBR) intelligent diagnostic system has been developed based on many successful diagnostic applications. It can solve the difficulty in knowledge acquisition and has more precision. Its application results prove that the usability of the system is good and it will increasingly attain perfection.

Keywords： rule-based reasoning     fault diagnosis     intelligent system     gear box    

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Digital twin-assisted gearbox dynamic model updating toward fault diagnosis

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 2, doi: 10.1007/s11465-023-0748-0

Abstract: One of the core challenges of intelligent fault diagnosis is that the diagnosis model requires numerousIn this study, a digital twin-assisted dynamic model updating method for fault diagnosis is thus proposedthe proposed method is successfully applied to the dynamic model updating of a single-stage helical gearbox; the virtual data generated by this model can be used for gear fault diagnosis.

Keywords： digital twin     gearbox     model construction     model updating     physical–virtual interaction    

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Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Diego CABRERA,Fernando SANCHO,René-Vinicio SÁNCHEZ,Grover ZURITA,Mariela CERRADA,Chuan LI,Rafael E. VÁSQUEZ

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 3,   Pages 277-286 doi: 10.1007/s11465-015-0348-8

Abstract:

This paper addresses the development of a random forest classifier for the multi-class fault diagnosis

Keywords： fault diagnosis     spur gearbox     wavelet packet decomposition     random forest    

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Multiple fault separation and detection by joint subspace learning for the health assessment of wind

Zhaohui DU, Xuefeng CHEN, Han ZHANG, Yanyang ZI, Ruqiang YAN

Frontiers of Mechanical Engineering 2017, Volume 12, Issue 3,   Pages 333-347 doi: 10.1007/s11465-017-0435-0

Abstract:

The gearbox of a wind turbine (WT) has dominant failure rates and highest downtime loss among allThus, gearbox health assessment for maintenance cost reduction is of paramount importance.The concurrence of multiple faults in gearbox components is a common phenomenon due to fault inductionHowever, most of the existing studies focusing on multiple fault diagnosis always suffer from inappropriateand verified by the analysis of a data set of a 750 kW WT gearbox.

Keywords： joint subspace learning     multiple fault diagnosis     sparse decomposition theory     coupling feature separation     wind turbine gearbox    

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Machine learning for fault diagnosis of high-speed train traction systems: A review

Frontiers of Engineering Management doi: 10.1007/s42524-023-0256-2

Abstract: Therefore, performing fault monitoring and diagnosis on the traction system of the HST is necessary.diagnosis.Machine learning has made considerably advancements in traction system fault diagnosis; however, a comprehensiveThen, the research and application of machine learning in traction system fault diagnosis are comprehensivelyFinally, the challenges for accurate fault diagnosis under actual operating conditions are revealed,

Keywords： high-speed train     traction systems     machine learning     fault diagnosis    

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Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical faultdiagnosis of bearings

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 814-828 doi: 10.1007/s11465-021-0650-6

Abstract: The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery.However, the inexplicability and low generalization ability of fault diagnosis models still bar themdiagnosis of bearings.diagnosis, 2) the better interpretability of the model output with hierarchical decision making, andThe multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition

Keywords： bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network    

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New method of fault diagnosis of rotating machinery based on distance of information entropy

Houjun SU, Tielin SHI, Fei CHEN, Shuhong HUANG

Frontiers of Mechanical Engineering 2011, Volume 6, Issue 2,   Pages 249-253 doi: 10.1007/s11465-011-0124-3

Abstract:

This paper introduces the basic conception of information fusion and some fusion diagnosis methodsof the information fusion, a new quantitative feature index monitoring and diagnosing the vibration faultThrough calculation it has been proved that this method can effectively distinguish different fault typesThen, the accuracy of rotor fault diagnosis can be improved through the curve chart of the distance of

Keywords： rotating machinery     information fusion     fault diagnosis     Information entropy     distance of the information    

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Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 829-839 doi: 10.1007/s11465-021-0652-4

Abstract: Existing fault diagnosis methods usually assume that there are balanced training data for every machineIt will degrade the performance of fault diagnosis methods significantly.To address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-baseddiagnosis for rotating machinery.diagnosis towards imbalanced training dataset through graph feature learning.

Keywords： imbalanced fault diagnosis     graph feature learning     rotating machinery     autoencoder    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis.Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and isTo solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which canThe results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in faultdiagnosis.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Tacholess order-tracking approach for wind turbine gearbox fault detection

Yi WANG, Yong XIE, Guanghua XU, Sicong ZHANG, Chenggang HOU

Frontiers of Mechanical Engineering 2017, Volume 12, Issue 3,   Pages 427-439 doi: 10.1007/s11465-017-0452-z

Abstract: The gearbox of a wind turbine is the most important transmission unit; it generally exhibits complexHowever, spectral analysis is based on a stationary assumption and thus inapplicable to the fault diagnosisThis constraint limits the application of spectral analysis to wind turbine diagnosis in industrial applicationsAlthough order-tracking methods have been proposed for wind turbine fault detection in recent years,resamples the signal at equiangular increments, and calculates the order spectrum for wind turbine fault

Keywords： wind turbine     variable-speed operating conditions     Vold-Kalman filtering     tacholess order tracking    

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Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0692-4

Abstract: Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability ofVibration and discharge pressure signals are two common signals used for the fault diagnosis of axialrelated to multi-sensor data fusion for the pump fault diagnosis are limited.This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial pistonResults show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of

Keywords： axial piston pump     fault diagnosis     convolutional neural network     multi-sensor data fusion    

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Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet

Yun KONG, Tianyang WANG, Zheng LI, Fulei CHU

Frontiers of Mechanical Engineering 2017, Volume 12, Issue 3,   Pages 406-419 doi: 10.1007/s11465-017-0419-0

Abstract:

Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis hassense, the periodic impulsive components induced by a localized defect are hard to extract, and the faultAiming to extract the fault feature of planet gear effectively, we propose a novel feature extractionThe experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposedmethod is effective at the feature extraction and fault diagnosis for the planet gear with a localized

Keywords： wind turbine     planet gear fault     feature extraction     spectral kurtosis     time wavelet energy spectrum    

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Vibration Fault Diagnosis for Large Scale Steam Turbine Sets

Yu Wenhu,Song bin

Strategic Study of CAE 2001, Volume 3, Issue 1,   Pages 44-50

Abstract:

This paper describes the development of vibration fault diagnosis for steam turbines being used inThe problems of vibration fault diagnosis research work are also pointed out.Importance to knowledge scope of diagnosis and the research of the amplitude and phase transfer characteristicSystem of performance and vibration remote monitoring and diagnosis for large-scale steam turbine setsFinally, this paper presents the develop trends of vibration fault diagnosis for steam turbines

Keywords： steam turbine sets     fault diagnosis     vibration     performance diagnosis    

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Weak characteristic information extraction from early fault of wind turbine generator gearbox

Xiaoli XU, Xiuli LIU

Frontiers of Mechanical Engineering 2017, Volume 12, Issue 3,   Pages 357-366 doi: 10.1007/s11465-017-0423-4

Abstract:

Given the weak early degradation characteristic information during early fault evolution in gearboxsignificantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.

Keywords： wind turbine generator gearbox     µ-singular value decomposition     local mean decomposition     weak characteristicinformation extraction     early fault warning    

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Title Author Date Type Operation

Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals

Journal Article

Intelligent fault diagnostic system based on RBR for the gearbox of rolling mills

Lixin GAO, Lijuan WU, Yan WANG, Houpei WEI, Hui YE

Journal Article

Digital twin-assisted gearbox dynamic model updating toward fault diagnosis

Journal Article

Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Diego CABRERA,Fernando SANCHO,René-Vinicio SÁNCHEZ,Grover ZURITA,Mariela CERRADA,Chuan LI,Rafael E. VÁSQUEZ

Journal Article

Multiple fault separation and detection by joint subspace learning for the health assessment of wind

Zhaohui DU, Xuefeng CHEN, Han ZHANG, Yanyang ZI, Ruqiang YAN

Journal Article

Machine learning for fault diagnosis of high-speed train traction systems: A review

Journal Article

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical faultdiagnosis of bearings

Journal Article

New method of fault diagnosis of rotating machinery based on distance of information entropy

Houjun SU, Tielin SHI, Fei CHEN, Shuhong HUANG

Journal Article

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Journal Article

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article

Tacholess order-tracking approach for wind turbine gearbox fault detection

Yi WANG, Yong XIE, Guanghua XU, Sicong ZHANG, Chenggang HOU

Journal Article

Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

Journal Article

Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet

Yun KONG, Tianyang WANG, Zheng LI, Fulei CHU

Journal Article

Vibration Fault Diagnosis for Large Scale Steam Turbine Sets

Yu Wenhu,Song bin

Journal Article

Weak characteristic information extraction from early fault of wind turbine generator gearbox

Xiaoli XU, Xiuli LIU

Journal Article