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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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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.Numerical 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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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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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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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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Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 1, doi: 10.1007/s11465-022-0725-z

Abstract: energy operator (EO) and its variants have received considerable attention in the field of bearing faultAs a result, the fault-related transients strengthened by these improved EO techniques are still subjectTo address these issues, this paper presents a novel EO fusion strategy for enhancing the bearing faultThird, the intrinsic manifolds are weighted to recover the fault-related transients.experimental verifications confirm that the proposed strategy is more effective for enhancing the bearing fault

Keywords： higher order energy operator     fault diagnosis     manifold learning     rolling element bearing     information    

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Method for solving the nonlinear inverse problem in gas face seal diagnosis based on surrogate models

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0689-z

Abstract: However, they have not been introduced into gas face seal diagnosis tasks because of the unacceptablecomputational cost of inferring the input fault parameters for the observed output or solving the inverse

Keywords： surrogate model     gas face seal     fault diagnosis     nonlinear dynamics     tribology    

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Basic research on machinery fault diagnostics: Past, present, and future trends

Xuefeng CHEN, Shibin WANG, Baijie QIAO, Qiang CHEN

Frontiers of Mechanical Engineering 2018, Volume 13, Issue 2,   Pages 264-291 doi: 10.1007/s11465-018-0472-3

Abstract:

Machinery fault diagnosis has progressed over the past decades with the evolution of machineries inHigh-value machineries require condition monitoring and fault diagnosis to guarantee their designed functionsdiagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signalOn the basis of the review of basic theory of machinery fault diagnosis and its practical applicationsdiagnosis.

Keywords： fault diagnosis     fault mechanism     feature extraction     signal processing     intelligent diagnostics    

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Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 2,   Pages 340-352 doi: 10.1007/s11465-021-0629-3

Abstract: Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years.However, many deep learning methods cannot fully extract fault information to recognize mechanical healthdifferent activation functions are trained through dimension reduction learning to obtain different faultThe integrated 2D feature images can effectively represent the fault characteristic contained in rawCompared with other classical deep learning methods, the proposed fault diagnosis method has considerable

Keywords： fault intelligent diagnosis     deep learning     deep convolutional neural network     high-dimensional samples    

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Research on Knowledge-based Method for Satellite Fault Diagnosis and Prediction

Yang Tianshe,Yang Kaizhong,Li Huaizu

Strategic Study of CAE 2003, Volume 5, Issue 6,   Pages 63-67

Abstract:

The fault diagnosis and prediction of satellites is a difficult problem due to the complex structureThis paper proposes a new method in which the use of multi-modal reasoning for satellite fault diagnosisThe method has been used in the development of the knowledge-based satellite fault diagnosis and recovery

Keywords： satellite     fault     diagnosis     prediction     multi-modal reasoning    

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Application of Uncertainty Reasoning Theory to Satellite Fault Detection and Diagnosis

Yang Tianshe,Li Huaizu,Cao Yuping

Strategic Study of CAE 2003, Volume 5, Issue 2,   Pages 68-74

Abstract: The reason is that detection and diagnosis of these faults requires reasonable reasoning and fault-tolerant

Keywords： satellite     fault     detection     diagnosis     uncertainty reasoning theory    

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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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Title Author Date Type Operation

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

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

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

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

Journal Article

Vibration Fault Diagnosis for Large Scale Steam Turbine Sets

Yu Wenhu,Song bin

Journal Article

Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

Journal Article

Method for solving the nonlinear inverse problem in gas face seal diagnosis based on surrogate models

Journal Article

Basic research on machinery fault diagnostics: Past, present, and future trends

Xuefeng CHEN, Shibin WANG, Baijie QIAO, Qiang CHEN

Journal Article

Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

Journal Article

Research on Knowledge-based Method for Satellite Fault Diagnosis and Prediction

Yang Tianshe,Yang Kaizhong,Li Huaizu

Journal Article

Application of Uncertainty Reasoning Theory to Satellite Fault Detection and Diagnosis

Yang Tianshe,Li Huaizu,Cao Yuping

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