Wear Fault Diagnosis of Aeroengines Based on Broad Learning System and Ensemble Learning

Wang, Mengmeng; Ge, Quanbo; Jiang, Haoyu; Yao, Gang

doi:10.3390/en12244750

Cited by 23 publications

(9 citation statements)

References 29 publications

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“…where SS res is residual sum of squares and SS tot represents total sum of squares. 2) Bagging-BLS: The Bagging-BLS is a combination model of Bagging and BLS [72]. Figure 3 shows the structure of Bagging-BLS.…”

Section: B Experimental Results By Classical Methodsmentioning

confidence: 99%

Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic

Zhan

Zheng

Zhang

et al. 2021

IEEE Internet Things J.

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The rapid geographic spread of COVID-19, to which various factors may have contributed, has caused a global health crisis. Recently, the analysis and forecast of the COVID-19 pandemic have attracted worldwide attention. In this work, a large COVID-19 dataset consisting of COVID-19 pandemic, COVID-19 testing capacity, economic level, demographic information, and geographic location data in 184 countries and 1,241 areas from Dec 18, 2019, to Sep 30, 2020, were developed from public reports released by national health authorities and bureau of statistics. We proposed a machine learning model for COVID-19 prediction based on the Broad Learning System (BLS). Here, we leveraged Random Forest to screen out the key features. Then, we combine the bagging strategy and Broad Learning System to develop a Random-forest-Bagging Broad Learning System (RF-Bagging-BLS) approach to forecast the trend of the COVID-19 pandemic. In addition, we compared the forecasting results with Linear Regression (LR) model, K-Nearest Neighbors (KNN), Decision Tree (DT), Adaptive Boosting (Ada), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Regression (SVR), Extra Trees (ET) regressor, CatBoost (CAT), LightGBM (LGB), XGBoost (XGB), and Broad Learning System (BLS).The RF-Bagging BLS model showed better forecasting performance in terms of relative mean square error (RMSE), coefficient of determination (R 2 ), adjusted coefficient of determination (R 2 adj ), median absolute error (MAD), and mean absolute percentage error (MAPE) than other models. Hence, the proposed model demonstrates superior predictive power over other benchmark models.

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Section: B Experimental Results By Classical Methodsmentioning

confidence: 99%

Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic

Zhan

Zheng

Zhang

et al. 2021

IEEE Internet Things J.

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“…By training multiple independent weak learners and combining their learning results, better performance, stability, and generalization ability can get better. Classical ensemble learning includes boosting, bagging and stacking methods 8,9 .…”

Section: Ensemble Kernel-based Broad Learning Systemmentioning

confidence: 99%

Ensemble kernel-based broad learning system for fast gas recognition in electronic nose systems

Zhao

Wang

2023

Third International Conference on Sensors and Information Technology (ICSI 2023)

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The rapid recognition of flammable and toxic gases is an essential and challenging task for electronic noses (E-nose) in various fields. The traditional recognition method is limited by the limited features of the gas ultra-time response curve, and has the problems of unsatisfactory classification results and long recognition time. We propose a noval gas recognition algorithm that combines the ensemble learning method with kernel-base learning system learning (KBLS), thereby improving the accuracy of gas recognition, with faster recognition time, and a more stable model. We used four volatile combustible gases as gas test datasets and evaluated the algorithm proposed in this study over a range of response curves from 0.5s to 4s. According to our extensive comparative experiments, it is found that the accuracy is higher than that of other algorithms.

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“…Tuerxun et al [23] used the improved Pelican algorithm to optimize the hyperparameters such as the number of feature nodes and enhanced nodes of BLS, and achieved good results in wind turbine fault diagnosis. Wang et al [24] trained multiple sub BLS models, and then integrated them using ensemble learning method. Based on the real oil analysis data, high-precision wear fault diagnosis of aero-engine has been realized.…”

Section: Introductionmentioning

confidence: 99%

An imbalanced sample intelligent fault diagnosis method using data enhancement and improved broad learning system

Cui

2023

Meas. Sci. Technol.

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Broad learning system (BLS) has been widely applied in the field of fault diagnosis because of its high computational efficiency, simple structure, and strong interpretability. However, traditional BLS cannot extract deep level fault features. Meanwhile, some fault samples are difficult to obtain, which leads to the imbalance of samples and further affects the diagnostic results of BLS. To solve these problems, an improved BLS fault diagnosis method based on data enhancement and multi-domain feature fusion is proposed. First, to solve the problem of sample imbalance, some false samples are generated through Deep Convolutional Generative Adversarial Networks (DCGAN). Second, to solve the problem of poor feature extraction ability of BLS, the multi-domain feature extraction and feature optimization based on ReliefF algorithm are carried out for the enhanced samples. Compared with traditional BLS, the improved BLS effectively solves the problem of sample imbalance and greatly improves the diagnostic accuracy. The proposed method is then testified on the rolling bearing fault simulation test bench. The results show that, samples generated by the proposed method are highly similar to the real samples. In addition, the diagnostic accuracy of the BLS after multi-domain feature extraction and optimization is improved by about 19.67%, which proves the effectiveness of the method. This method provides a new perspective in fault diagnosis and could further expand the application of BLS in fault diagnosis.

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Wear Fault Diagnosis of Aeroengines Based on Broad Learning System and Ensemble Learning

Cited by 23 publications

References 29 publications

Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic

Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic

Ensemble kernel-based broad learning system for fast gas recognition in electronic nose systems

An imbalanced sample intelligent fault diagnosis method using data enhancement and improved broad learning system

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