A rotor fault diagnosis method based on BP-Adaboost weighted by non-fuzzy solution coefficients

Liu, Yang; Zhao, Chencheng; Liang, Haiying; Lü, Huanhuan; Cui, NingYuan; Bao, KuiYuan

doi:10.1016/j.measurement.2022.111280

Cited by 17 publications

(7 citation statements)

References 21 publications

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“…In addition, to demonstrate the advantages of the RF classifier in the newly proposed method, 9 other common classifiers, AdaBoost [25], BPNN [26], DA [27], DT [28], GNN [29], KNN [30], NB [19], SVM [32], and CNN [33], are selected for comparison. In all classification models, the parameter optimization method is the same as RF, using Bayesian optimization, trying to minimize the cross-validation error for classification algorithms by varying the parameters.…”

Section: Comparison Of Classifiersmentioning

confidence: 99%

“…Based on the constructed fault features, scholars have developed many useful fault classification methods, such as AdaBoost [25], BP neural network (BPNN) [26], discriminant analysis (DA) [27], decision tree (DT) [28], Gaussian mixture model (GMM) [29], K-nearest neighbour (KNN) [30], naive Bayes (NB) [19], random forest (RF) [31], support vector machine (SVM) [32], and others. Liu et al adopted the AdaBoost combined binary classifier and applied it to the fault multiclassification problem [25]. Li [31].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions

Huang

Xu²,

Cao³

et al. 2022

Shock and Vibration

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Fault diagnosis of rolling bearings under variable speed is a common issue in engineering practice, but it lacks an effective diagnosis algorithm, while approaches developed for steady speed cannot be directly applied. Therefore, for effectively identifying bearing faults under variable speed, this paper proposed a multiscale fractional dimensionless indicator (MSFDI) and put forward a fault diagnosis method with random forest (RF). It can overcome the feature space aliasing problem of traditional dimensionless indicators, which will lead to increased diagnosis uncertainty. The multiorder fractional Fourier transform is carried out on bearing signals to get a series of fractional Fourier domain components, which will be used to construct the original MSFDI feature set. Moreover, reliefF selects the sensitive MSFDIs as the input of the RF algorithm to determine the health condition. The effectiveness of the proposed method is verified by experiments and case studies.

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Section: Comparison Of Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions

Huang

Xu²,

Cao³

et al. 2022

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…Then, based on the extracted signal features, supervised and unsupervised learning methods are often performed to diagnose and prognose rotor systems [6]. For example, Liu [7] extracted the time-and frequency-domain features of shaft vibration signals, such as standard deviation, shape factor, root mean square frequency, and standard deviation frequency, and combined them with a non-fuzzy solution-weighted back-propagation-AdaBoost to realize a multi-fault diagnosis. Nath [8] combined distinctive frequency components, such as rotating frequency and its harmonic frequencies, in the vibration spectrum with a developed long short-term memory to diagnose the structural rotor fault.…”

Section: Introductionmentioning

confidence: 99%

On-line condition monitoring for rotor systems based on nonlinear data-driven modelling and model frequency analysis

Zhao,

Liu,

Zhang

et al. 2024

Nonlinear Dyn

Self Cite

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This paper proposes a novel on-line rotor system condition monitoring approach using nonlinear data-driven modelling and model frequency analysis. First, the dynamic process model of the vibration transmission path between the vibration measurement points of two fulcrum structures is established by utilizing nonlinear data-driven modelling. Then, the unique frequency properties are extracted from the established model to reveal, in real time, the health condition of the rotor system. Finally, using the frequency properties as features, the unsupervised learning technology is applied to the on-line monitoring of the rotor system. Compared to conventional condition monitoring methods, the proposed approach can output an early warning 26 min before a shaft fracture occurs, without generating false alarms. Consequently, this approach can greatly enhance diagnostic accuracy, demonstrating its potential to contribute to the advancement of rotor system condition monitoring techniques.

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“…As an efficient nonlinear algorithm in the Boosting learning family, BP-Adaboost modelling strategy holds the high modelling effects and had been widely used in traffic flow prediction, image classification, fingerprint recognition and other fields [34][35][36]. However, the BP-Adaboost may also cause the hyperparameter optimization issues when dealing with the highly complex problems.…”

Section: Introductionmentioning

confidence: 99%

Fatigue reliability analysis of aeroengine blade-disc systems using physics-informed ensemble learning

Li,

Song,

Choy

et al. 2023

Phil. Trans. R. Soc. A.

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For the fatigue reliability analysis of aeroengine blade-disc systems, the traditional direct integral modelling methods or separate independent modelling methods will lead to low computational efficiency or accuracy. In this work, a physics-informed ensemble learning (PIEL) method is proposed, i.e. firstly, based on the physical characteristics of blade-disc systems, the complex multi-component reliability analysis is split into a series of single-component reliability analyses; moreover, the PIEL model is established by introducing the mapping of multiple constitutive responses and the multi-material physical characteristics into the ensemble learning; finally, the PIEL-based system reliability framework is established by quantifying the failure correlation with the Copula function. The reliability analysis of a typical aeroengine high-pressure turbine blade-disc system is regarded as an example to verify the effectiveness of the proposed method. Compared with the direct Monte Carlo, support vector regression, neural network, ensemble learning and physics-informed neural network, the proposed method exhibits the highest computing accuracy and efficiency, and is validated to be an efficient method for the reliability analysis of blade-disc systems. The current work can provide a novel insight for physics-informed modelling and fatigue reliability analyses. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

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A rotor fault diagnosis method based on BP-Adaboost weighted by non-fuzzy solution coefficients

Cited by 17 publications

References 21 publications

Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions

Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions

On-line condition monitoring for rotor systems based on nonlinear data-driven modelling and model frequency analysis

Fatigue reliability analysis of aeroengine blade-disc systems using physics-informed ensemble learning

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