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Machine Learning-Based Online Multi-Fault Diagnosis for IMs Using Optimization Techniques With Stator Electrical and Vibration Data

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2024年第4期39卷 2412-2424页

作者： Hsu, Shih-Hsien Lee, Chien-Hsing Wu, Wen-Fang Jiang, Joe-Air Feng Chia Univ Dept Elect Engn Taichung 407 Taiwan Natl Cheng Kung Univ Dept Syst & Naval Mech Engn Tainan 701 Taiwan Natl Taiwan Univ Dept BioMechatron Engn Taipei 106 Taiwan

Induction motors (IMs) have been commonly applied to industrial fields since the past decades;thus, developing advanced fault diagnosis methods becomes vital for IM applications. This study proposed an online fault diagnosis system for IMs based on the Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms to reduce the additional repair costs and prevent unexpected downtime. It focused on detecting healthy three-phase IMs and five common fault conditions of the IMs, involving broken rotor bars, rotor unbalance, and composite faults with short-circuited stator windings that combined two or three types of the faults, for practical purposes. The experimental results show that the model performance improved by 15% over the default model when train-test split ratios, feature selection, and hyperparameter optimization, notably in XGBoost, are considered. The proposed XGBoost model enables a high accuracy of 96.06% for RF to perform a motor fault diagnosis under six different motor conditions. Furthermore, the execution time required by the proposed fault diagnosis system is 57% less than the time required by existing motor fault diagnosis methods. These results successfully demonstrate the effectiveness of the methods proposed in this study for online motor diagnosis.

关键词： extreme gradient boosting algorithm machine learning motor failure diagnosis random forest algorithm

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Analysis and prediction of the influence of milling parameters on CFRP strength based on XGBoost algorithm

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2023年第7期45卷 1-14页

作者： Guo, Nan Chen, Yan Yan, Chaoren Meng, Xiangjun Qian, Ning Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China JITRI Insitute Precise Mfg Technol Nanjing 210016 Peoples R China

Carbon fiber reinforced plastic (CFRP) is widely used in aircraft fuselage, wings and tail etc. Milling is a necessary mechanical machining method after CFRP parts have been molded. The strength of CFRP materials is variable under different milling parameters and it's difficult to judge the variation tendency when changing the parameters. To explore the influence of the milling parameters on the mechanical strength, prediction models of tensile and compressive strength are established in this article, which is based on the extreme gradient boosting algorithm (XGBoost). In addition, the differences in prediction performance between the extreme gradient boosting algorithm model and the artificial neural network (ANN) model are compared. The results show that the milling parameters will not change the tensile strength of CFRP. The better prediction ability is obtained in the test dataset by XGBoost, and the maximum error of the model for the prediction of CFRP compressive strength among the milling parameters range is less than 7.56%. The XGBoost model also indicates that the spindle speed has more pronounced influence on the compressive strength of CFRP. It is proven by the fracture topography under different spindle speeds: the increase of spindle speed result in the heat damage of the machining surface and reduce the fiber-resin bonding strength and finally decrease the compressive strength of CFRP. The prediction model proposed in this article is expected to provide more guidance for milling CFRP materials to reduce the machining damage and obtain better mechanical strength of the parts after machining.

关键词： CFRP Milling Prediction of mechanical properties extreme gradient boosting algorithm

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Estimation of the Visibility in Seoul, South Korea, Based on Particulate Matter and Weather Data, Using Machine-learning algorithm

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AEROSOL AND AIR QUALITY RESEARCH 2022年第10期22卷 1-15页

作者： Kim, Bu-Yo Cha, Joo Wan Chang, Ki-Ho Lee, Chulkyu Natl Inst Meteorol Sci Res Applicat Dept Seogwipo 63568 Jeju South Korea Natl Inst Meteorol Sci Observat Res Dept Seogwipo 63568 Jeju South Korea

Visibility is an important indicator of air quality and of any consequent meteorological and climate change. Therefore, visibility in Seoul, which is the most polluted city in South Korea, was estimated using machine learning (ML) algorithms based on meteorological (temperature, relative humidity, and precipitation) and particulate matter (PM10 and PM2.5) data acquired from an automatic weather station, and the estimated visibility was compared with the observed visibility. Meteorological data, observed at 1-h intervals between 2018 and 2020, were used. Through learning and validation of each ML algorithm, the extreme gradient boosting (XGB) algorithm was found to be most suitable for visibility estimations (bias = 0 km, root mean square error (RMSE) = 0.08 km, and r = 1 for training data set). Among the meteorological and particulate matter data used for learning the XGB algorithm, the relative importance of PM2.5 and relative humidity variables were high (51% and 19%, respectively), whereas precipitation and wind speed had the low relative importance (approximately 1%). The estimation accuracy for the test dataset was good (bias = -0.11 km, RMSE = 2.08 km, and r = 0.94);the estimation accuracy was higher in the dry season (bias = -0.06 km, RMSE = 1.79 km, and r = 0.96) than in the rainy season (bias = -0.17 km, RMSE = 2.34 km, and r = 0.91). The results of this study indicated a higher correlation than the results of previous visibility estimation studies. The proposed method promotes accurate estimation of visibility in areas with poor visibility, and thus, it can be used to assess public health in areas with poor air quality.

关键词： Seoul Meteorological data PM10 PM2.5 Visibility estimation Machine learning extreme gradient boosting algorithm

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Decoding the synergy: unveiling gradient boosting regression model for multivariate quantitation of pioglitazone, alogliptin and glimepiride in pure and tablet dosage forms

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BMC CHEMISTRY 2024年第1期18卷 1-13页

作者： Elkhoudary, Mahmoud M. Marie, Aya A. Hammad, Sherin F. Salim, Mohamed M. Kamal, Amira H. Horus Univ Egypt Fac Pharm Dept Pharmaceut Chem New Damietta 34517 Egypt Tanta Univ Fac Pharm Dept Pharmaceut Analyt Chem Tanta 31527 Egypt Mansoura Univ Fac Pharm Dept Pharmaceut Analyt Chem Mansoura 35516 Egypt Tanta Univ Fac Pharm Med CampusElgeish St Tanta 31111 Egypt

This study represents a comparison among the performances of four multivariate procedures: partial least square (PLS) and artificial neural networks (ANN) in addition to support vector regression (SVR) and extreme gradient boosting (XG Boost) algorithm for the determination of the anti-diabetic mixture of pioglitazone (PIO), alogliptin (ALG) and glimepiride (GLM) in pharmaceutical formulations with aid of UV spectrometry. Key wavelengths were selected using knowledge-based variable selection and various preprocessing methods (e.g., mean centering, orthogonal scatter correction, and principal component analysis) to minimize noise and improve model precision. XG Boost effectively enhanced computing speed and accuracy by focusing on specific spectral features rather than the entire spectrum, demonstrating its advantages in resolving complex, overlapping spectral data. The independent test results of different models demonstrated that XG Boost outperformed other methods. XG Boost achieved the lowest root mean squared error of prediction (RMSEP) and standard deviation (SD) values across all compounds, indicating minimal prediction error and variability. For PIO, XG Boost recorded an RMSEP of 0.100 and SD of 0.369, significantly better than PLS and ANN. For ALG, XG Boost showed near-perfect performance with an RMSEP of 0.001 and SD of 0.005, outperforming SVR and PLS, which had higher error rates. In the case of GLM, XG Boost also excelled with an RMSEP of 0.001 and SD of 0.018, demonstrating superior precision compared to the much higher errors seen in PLS and ANN. These results highlight XG Boost's exceptional ability to handle complex, overlapping spectral data, making it the most reliable and accurate model in this study.

关键词： Partial least squares UV/Vis spectroscopy Artificial neural networks extreme gradient boosting algorithm Support vector regression

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An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2024年第1期12卷

作者： Nagar, Harsh Machavaram, Rajendra Ambuj, Peeyush Soni, Peeyush Saha, Subhajit Bose, T. Subhash Chandra Indian Inst Technol Kharagpur Agr & Food Engn Dept Kharagpur 721302 W Bengal India

Knowing tractor drawbar pull is crucial to ensure the tractor can handle the required workload efficiently and safely, preventing soil damage and optimising field productivity. The present study proposes a novel approach for tractor drawbar pull prediction by utilising the tractor's geometric parameters and forward speed to develop a cloud-infused, server-less, machine learning-based real-time generalised tractor drawbar pull prediction model for any tractor between the 6-58 kW power range. The drawbar pull prediction models from ANN and six ML algorithms were developed, and the data analysis with hyperparameter tuning concluded that the extreme gradient boosting (XGB) ML model outperformed the other ML models. A reasonable accuracy with R2 = 0.93 and MAPE = 6.77% was achieved using the XGB ML model for a separate validation dataset, which was not used for training. Furthermore, a cloud-based serverless Android App integrated with the XGB ML-based drawbar pull prediction model was developed for real-time tractor drawbar pull prediction and monitoring during tillage operations. The field validation demonstrated the XGB ML model's generalisation ability and effectiveness, with R2 = 0.90 and maximum MAPE of 9.86%. It can be used to simulate and optimize tractor performance, guiding manufacturers in selecting geometric parameters for tractor design.

关键词： Tractor drawbar pull prediction generalised model machine learning extreme gradient boosting algorithm cloud-based serverless android app

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Machine learning-aided risk stratification in Philadelphia chromosome-positive acute lymphoblastic leukemia

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BIOMARKER RESEARCH 2021年第1期9卷 13页

作者： Nishiwaki, Satoshi Sugiura, Isamu Koyama, Daisuke Ozawa, Yukiyasu Osaki, Masahide Ishikawa, Yuichi Kiyoi, Hitoshi Nagoya Univ Hosp Dept Adv Med Showa Ku 65 Tsurumai Cho Nagoya Aichi 4668560 Japan Toyohashi Municipal Hosp Div Hematol & Oncol Toyohashi Aichi Japan Japanese Red Cross Nagoya Daiichi Hosp Dept Hematol Nagoya Aichi Japan Nagoya Univ Grad Sch Med Dept Hematol & Oncol Nagoya Aichi Japan

We used the extreme gradient boosting algorithm, an optimized gradient boosting machine learning library, and established a model to predict events in Philadelphia chromosome-positive acute lymphoblastic leukemia using a machine learning-aided method. A model was constructed using a training set (80%) and prediction was tested using a test set (20%). According to the feature importance score, BCR-ABL lineage, polymerase chain reaction value, age, and white blood cell count were identified as important features. These features were also confirmed by the permutation feature importance for the prediction using the test set. Both event-free survival and overall survival were clearly stratified according to risk groups categorized using these features: 80 and 100% in low risk (two or less factors), 42 and 47% in intermediate risk (three factors), and 0 and 10% in high risk (four factors) at 4 years. Machine learning-aided analysis was able to identify clinically useful prognostic factors using data from a relatively small number of patients.

关键词： extreme gradient boosting algorithm Machine learning Philadelphia chromosome-positive acute lymphoblastic leukemia Prognostic factor Survival stratification

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Predicting the patch load resistance of stiffened plate girders using machine learning algorithms

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OCEAN ENGINEERING 2021年 240卷 109886-109886页

作者： Viet-Hung Truong Papazafeiropoulos, George Quang-Viet Vu Van-Trung Pham Kong, Zhengyi Thuyloi Univ Fac Civil Engn 175 Tay Son Hanoi Vietnam Natl Tech Univ Athens Dept Struct Engn Athens 15780 Greece Vietnam Maritime Univ Fac Civil Engn 484 Lach Tray St Hai Phong City Vietnam Anhui Univ Technol Sch Civil Engn & Architecture Maanshan 243032 Peoples R China

In the incremental launching method employed for steel bridge construction, the girder is subjected to patch loading which occurs at the piers' position. This loading significantly affects the girder resistance in the construction stage. Therefore, prediction of the girder resistance under this loading is important. This paper proposes a new approach for predicting the patch load resistance of stiffened plate girders using an extreme gradient boosting algorithm (XGBoost). A total of 170 experimental data on stiffened plate girders under patch loading collected from the literature serves as the training and testing data to build the predictive model. To demonstrate the efficiency of the proposed model, its predictions were compared with those obtained from other Machine Learning (ML) methods such as support vector machines (SVM), decision tree (DT), random forest (RF), adaptive boost (Adaboost), and deep learning (DL). The accuracy of the proposed model was validated against the existing equations taken from the design standards (EN-1993-1-5 and BS 5400) as well as existing formulae in the literature. The comparative results reveal that the proposed model provides better and more accurate predictions than the existing formulae.

关键词： Patch loading Steel plate girders Machine learning extreme gradient boosting algorithm Incremental launching

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A hybrid decomposition and Machine learning model for forecasting Chlorophyll-a and total nitrogen concentration in coastal waters

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JOURNAL OF HYDROLOGY 2023年 619卷

作者： Zhu, Xiaotong Guo, Hongwei Huang, Jinhui Jeanne Tian, Shang Zhang, Zijie Nankai Univ Coll Environm Sci & Engn Sino Canada Joint R&D Ctr Water & Environm Safety Tianjin 300071 Peoples R China

Information regarding Chlorophyll-a (ChlA) and total nitrogen (TN) is critical for early warning of algal blooms. However, reliable models for accurate forecasting of the ChlA and TN are still lacking due to the optical complexity of coastal waters. To address this issue, we proposed a novel hybrid model named the CEEMDAN-VMD-XGBOOST-SARIMA (CVXS) model to forecast ChlA and TN concentrations. The model performance was validated at three hydrological monitoring stations in Hong Kong, China. Four independent models including extreme gradient boosting (XGBoost), support vector regression (SVR), deep learning (DL), and Seasonal autoregressive integrated moving average (SARIMA), and three hybrid models including complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-XGBoost, CEEMDAN-SVR, and CEEMDAN-DL were developed to compare their performance with the CVXS model. In addition, the physical mechanisms of the CVXS model were further explored through correlation analysis between the decomposed time series of water quality parameters. The result indicated that (1) the CVXS model had the best accuracy among all models for forecasting ChlA and TN, and all the NSEs remained above 0.97 at three hydrological monitoring stations. For forecasting ChlA, the performance of the eight models is ranked as CVXS > CEEMDAN-XGBoost > CEEMDAN-DL > CEEMDAN-SVR > XGBoost > DL > SARIMA > SVR. For forecasting TN, the performance of the eight models is ranked as CVXS > CEEMDAN-XGBoost > CEEMDAN-DL > CEEMDAN-SVR > XGBoost > SVR > SARIMA > DL;(2) the optimal forecasting time horizons of the CVXS model were one to two months;and (3) the variability of ChlA and TN concentrations induced by hydrologic factors has been inherently embedded in the decomposed time series data, thus providing the theoretical basis for the CVXS model forecasting water quality parameters. The results of this study are promising with respect to forecasting algal blooms and coastal water resource manage

关键词： Water quality Machine learning Decomposition algorithm extreme gradient boosting algorithm Forecasting

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Dual particle swarm optimization based data-driven state of health estimation method for lithium-ion battery

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JOURNAL OF ENERGY STORAGE 2022年 56卷

作者： Liu, Xingtao Liu, Xiaojian Fang, Leichao Wu, Muyao Wu, Ji Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Peoples R China Anhui Intelligent Vehicle Engn Lab Hefei 230009 Peoples R China

Accurate estimation of Li-ion battery state of health (SOH) is essential to ensure battery safety and vehicle operation. Here, this paper proposes a dual particle swarm optimization algorithm-extreme gradient boosting algorithm (DP-X) with the battery's charging voltage and incremental capacity (IC) data. First, the features are extracted from the voltage curve and the IC curve of each charging cycle through curve compression and interpolation. Then, this paper utilizes the PSO-XGBoost (P-X) algorithm to optimize the selected features and reduce the dimensionality of the features. Finally, the P-X algorithm was applied to combine with the optimized features to adjust the model's hyperparameters and estimate the SOH. Experimental results show that the maximum SOH estimation error of the dual P-X algorithm is less than 2 %.

关键词： Lithium-ion battery State-of-health estimation Particle swarm optimization extreme gradient boosting algorithm

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Prediction of Ionospheric Scintillations Using Machine Learning Techniques during Solar Cycle 24 across the Equatorial Anomaly

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ATMOSPHERE 2024年第10期15卷 1213页

作者： Nasurudiin, Sebwato Yoshikawa, Akimasa Elsaid, Ahmed Mahrous, Ayman Egypt Japan Univ Sci & Technol E JUST Inst Basic & Appl Sci Dept Space Environm New Borg El Arab City 21934 Egypt Islamic Univ Uganda IUIU Fac Sci Dept Phys Kumi RdPOB 2555 Mbale Uganda Kyushu Univ Dept Earth & Planetary Sci Fukuoka 8190395 Japan Egypt Japan Univ Sci & Technol E JUST Inst Basic & Appl Sci Dept Appl & Computat Math New Borg El Arab City 21934 Egypt Mansoura Univ Fac Engn Dept Math & Engn Phys Mansoura 35516 Egypt Helwan Univ Fac Sci Dept Phys Cairo 11795 Egypt

Ionospheric scintillation is a pressing issue in space weather studies due to its diverse effects on positioning, navigation, and timing (PNT) systems. Developing an accurate and timely prediction model for this event is crucial. In this work, we developed two machine learning models for the prediction of ionospheric scintillation events at the equatorial anomaly during the maximum and minimum phases of solar cycle 24. The models developed in this study are the Random Forest (RF) algorithm and the extreme gradient boosting (XGBoost) algorithm. The models take inputs based on the solar wind parameters obtained from the OMNI Web database from the years 2010-2017 and Pc5 wave power obtained from the Bear Island (BJN) magnetometer station. We retrieved data from the Scintillation Network and Decision Aid (SCINDA) receiver in Egypt from which the S4 index was computed to quantify amplitude scintillations that were utilized as the target in the model development. Out-of-sample model testing was performed to evaluate the prediction accuracy of the models on unseen data after training. The similarity between the observed and predicted scintillation events, quantified by the R2 score, was 0.66 and 0.74 for the RF and XGBoost models, respectively. The corresponding Root Mean Square Errors (RMSEs) associated with the models were 0.01 and 0.01 for the RF and XGBoost models, respectively. The similarity in error shows that the XGBoost model is a good and preferred choice for the prediction of ionospheric scintillation events at the equatorial anomaly. With these results, we recommend the use of ensemble learning techniques for the study of the ionospheric scintillation phenomenon.

关键词： machine learning ensemble learning techniques ionospheric scintillation Random Forest algorithm extreme gradient boosting algorithm

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