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Sensor fault detection, diagnosis, and data reconstruction strategy for chiller based on an improved tree-based pipeline optimization tool framework

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INTERNATIONAL JOURNAL OF REFRIGERATION 2025年 174卷 60-75页

作者： Wu, Pinguo Hu, Yunpeng Li, Guannan Liu, Qi Xiong, Chenglong Deng, Jiahui Chen, Shiao Wuhan Univ Sci & Technol Sch Urban Construct Wuhan 430065 Peoples R China Wuhan Business Univ Dept Bldg Environm & Energy Engn Wuhan 430065 Peoples R China Xian Univ Architecture & Technol State Key Lab Green Bldg Western China Xian 710055 Peoples R China Wuhan Univ Sci & Technol Hubei Prov Engn Res Ctr Urban Regenerat Wuhan 430081 Peoples R China China Railway SIYUAN Consulting & Engn Grp Co Ltd Wuhan 430063 Peoples R China

Machine learning is widely applied to fault detection, diagnosis, and data reconstruction for chiller sensors but often requires domain expertise and manual intervention. tree-based pipeline optimization tool (TPOT), an automated machine learning framework, shows promise in fault detection, diagnosis, and reconstruction by automating model optimization and parameter tuning. Although the TPOT framework includes automated data preprocessing functions, it lacks the ability to automatically handle outliers. Outliers in sensor data can adversely affect the quality of the modeling process. By leveraging TPOT's capability for automated modeling, an ensemble fault diagnosis model can be developed. However, this model is prone to misdiagnosis when the sensor variables exhibit high correlations. Therefore, this study proposes an improved TPOT framework by incorporating a sliding window strategy to enhance TPOT's ability to handle outliers. The ensemble fault diagnosis model based on TPOT incorporates a Euclidean distance strategy, which identifies faulty sensors by quantifying the difference between the input data and the predicted results. Results show that the improved TPOT framework enhances fault detection, diagnosis, and data reconstruction. In the detection of sensor bias, drift, and precision degradation faults, the fault detection rates increased by a mean of 3.11 %, 4.64 %, and 8.62 %, respectively. The diagnostic strategy incorporating Euclidean distance reduced the number of misdiagnoses by one in the diagnosis of nine different sensor faults. In sensor data reconstruction, the RMSE was reduced by a mean of 68.26 %.

关键词： Chiller sensor Automated machine learning tree-based pipeline optimization tool Fault detection and diagnosis Data reconstruction

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Creating optimized machine learning pipelines for PV power generation forecasting using the grid search and tree-based pipeline optimization tool

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COGENT ENGINEERING 2024年第1期11卷

作者： Khasawneh, Hussam J. Ghazal, Zaid A. Al-Khatib, Waseem M. Al-Hadi, Ahmad M. Arabiyat, Zaid M. Univ Jordan Dept Mechatron Engn Amman Jordan Al Hussein Tech Univ Dept Elect Engn Amman Jordan

Demand for electric power, especially amidst limited fossil fuel-based generation capacity, has elevated renewable energy sources to a forefront solution for the growing energy needs. Solar energy, a key renewable source through photovoltaic (PV) panels, faces challenges such as intermittency and non-dispatchability. Thus, recent research has focused on developing programs to predict near-future solar energy generation, with machine learning being a pivotal approach. This article details the creation of an effective machine-learning pipeline for predicting future hourly power generation based on weather data (e.g. temperature, humidity, irradiance). The pipeline, aimed at a scheduling system in a farm equipped with a Solar Power System (SPS) in Al-Salt, Jordan, was optimized using Genetic Algorithm and Grid Search methods. The objective of this article is to create an optimal pipeline with minimal loss. The evaluation shows that ensemble regressors, especially Gradient Boosting Regressors, are effective. This is evidenced in the grid search pipeline, which outperformed the TPOT optimization pipeline-derived pipeline, the latter including stacked ensemble regressors and sequential preprocessors.

关键词： Solar energy forecasting renewable energy systems energy scheduling optimization demand response machine learning genetic algorithm grid search tree-based pipeline optimization tool Ai Qingsong, Senior Editor, Wuhan University of Technology, China optimizationRenewable EnergyMachine LearningSolar energy

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TPOT with SVM hybrid machine learning model for lung cancer classification using CT image

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 104卷

作者： Murthy, Nayana N. Thippeswamy, K. Visvesvaraya Technol Univ Dept Comp Sci & Engn Belagavi 590018 Karnataka India VTU PG Ctr Dept Comp Sci Engn Mysuru India Visvesvaraya Technol Univ Belagavi 590018 Karnataka India

Lung cancer arises when lung cells proliferate uncontrollably, creating tumors that may disrupt the normal functioning of the lungs. Accurate classification of lung cancer leads to earlier detection, which significantly improves treatment outcomes and survival rates. Diagnosing early-stage lung cancer is challenging due to its lack of symptoms and the high costs. Moreover, the previous methods struggle to fully understand and interpret the complex patterns in medical images. Therefore, this work develops the tree-based pipeline optimization tool with Support Vector Machine (TPOT_SVM) to classify lung cancer. Here, the Computed Tomography (CT) image is considered as input, and then Adaptive Median Filter (AMF) is employed for pre-processing. After this, the lung lobe is segmented by employing M-SegNet. Following this, the features, namely statistical features, Convolutional Neural Network (CNN) features and textural features are mined. After this, the features are subjected to lung cancer classification, which is executed using the proposed TPOT_SVM. Here, TPOT_SVM is produced by integrating a tree-based pipeline optimization tool (TPOT) and Support Vector Machine (SVM). The TPOT_SVM attained an accuracy of 91.77%, a True Positive Rate (TPR) of 94.79% and a False Positive Rate (FPR) of 11.24%. The accuracy of the TPOT_SVM is 9.81%, 6.64%, 4.36%, 4.12%, 3.27%, 2.18%, 1.64% and 1.09% higher than the existing methods, such as Convolutional Neural Network (CNN), Kernel Attribute Selected Classifier (KASC), Cat Swarm optimization-based Computer-Aided Diagnosis for lung cancer classification (CHO-CADLCC), SelfUpgraded Cat Mouse Optimizer with Machine Learning Driven Lung Cancer Classification (SCMO-MLL2C), Lung-EffNet, Gradient Boosting Classifier (GBC), TPOT and SVM.

关键词： Adaptive median filter Support vector machine M-SegNet tree-based pipeline optimization tool Machine learning

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The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms

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MULTIMEDIA toolS AND APPLICATIONS 2024年第30期83卷 74349-74364页

作者： Rimal, Yagyanath Sharma, Navneet Alsadoon, Abeer IIS Deemed Be Univ Jaipur India Charles Sturt Univ CSU Sch Comp Math & Engn Sch Comp Data & Math Sci Bathurst Australia Western Sydney Univ WSU Sydney Australia Asia Pacific Int Coll APIC Sydney Australia

Hyperparameters play a critical role in analyzing predictive performance in machine learning models. They serve to strike a balance between overfitting and underfitting of research-independent features to prevent extremes. Manual tuning and automated techniques are employed to identify the optimal combination and permutation to achieve the best model performance. This study explores the pursuit of the best fit through various hyperparameters. Following Logistic Regression analysis, this research compared Random Forest, Randomized search, Grid search, Genetic, Bayesian, and Optuna machine learning model tuning for the best accuracy of prediction the student The model accuracy was further assessed using confusion matrices and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) curves for student grade classification. The genetic algorithm's recommended hyperparameter tuning yielded the highest accuracy (82.5%) and AUC-ROC score (90%) for student result classification. Manual tuning with an estimator of 300, criterion entropy, max features of sqrt, and a minimum sample leaf of 10 achieved an accuracy of 81.1%, which closely resembled the performance randomized search cross-validation algorithm. The default random forest model scored the least accuracy (78%). However, this manual tuning process took a lesser time (3.66 s) to fit the model while grid search CV tuned 941.5 s. Hence, this research made significant contributions to optimizing various machine learning models using a range of hyperparameters for grade classification.

关键词： Random forest Educational data mining Bayesian Randomized search cross validation Automate hyperparameter tuning tree-based pipeline optimization tool Characteristic area under the curve

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Radiomics-based Machine Learning to Predict Recurrence in Glioma Patients Using Magnetic Resonance Imaging

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JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY 2023年第1期47卷 129-135页

作者： Hu, Guanjie Hu, Xinhua Yang, Kun Yu, Yun Jiang, Zijuan Liu, Yong Liu, Dongming Hu, Xiao Xiao, Hong Zou, Yuanjie You, Yongping Liu, Hongyi Chen, Jiu Nanjing Med Univ Affiliated Brain Hosp Dept Neurosurg Nanjing Peoples R China Nanjing Med Univ Inst Brain Funct Imaging Nanjing Peoples R China Nanjing Med Univ Affiliated Hosp 1 Dept Neurosurg Nanjing Peoples R China Nanjing Med Univ Sch Biomed Engn & Informat Nanjing Peoples R China Nanjing Med Univ Affiliated Brain Hosp Dept Radiol Nanjing Jiangsu Peoples R China Nanjing Med Univ Affiliated Brain Hosp Inst Neuropsychiat Nanjing Jiangsu Peoples R China Nanjing Med Univ Affiliated Brain Hosp Inst Neuropsychiat Inst Brain Funct Imaging 264 Guangzhou Rd Nanjing 210029 Jiangsu Peoples R China Nanjing Med Univ Affiliated Brain Hosp Dept Neurosurg 264 Guangzhou Rd Nanjing 210029 Jiangsu Peoples R China

ObjectiveRecurrence is a major factor in the poor prognosis of patients with glioma. The aim of this study was to predict glioma recurrence using machine learning based on radiomic *** recruited 77 glioma patients, consisting of 57 newly diagnosed patients and 20 patients with recurrence. After extracting the radiomic features from T2-weighted images, the data set was randomly divided into training (58 patients) and testing (19 patients) cohorts. An automated machine learning method (the tree-based pipeline optimization tool) was applied to generate 10 independent recurrence prediction models. The final model was determined based on the area under the curve (AUC) and average specificity. Moreover, an independent validation set of 20 patients with glioma was used to verify the model *** in glioma patients was successfully predicting by machine learning using radiomic features. Among the 10 recurrence prediction models, the best model achieved an accuracy of 0.81, an AUC value of 0.85, and a specificity of 0.69 in the testing cohort, but an accuracy of 0.75 and an AUC value of 0.87 in the independent validation *** algorithm that is generated by machine learning exhibits promising power and may predict recurrence noninvasively, thereby offering potential value for the early development of interventions to delay or prevent recurrence in glioma patients.

关键词： glioma machine learning recurrence tree-based pipeline optimization tool

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tree-based machine learning models assisted fluorescent sensor array for detection of metal ions based on silver nanocluster probe

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023年第1期297卷 122738页

作者： Chen, Xihang Xu, Jinming Zhou, Huangmei Zhao, Yu Wu, Ying Zhang, Jie Zhang, Sanjun East China Normal Univ State Key Lab Precis Spect 500 Dongchuan Rd Shanghai 200241 Peoples R China East China Normal Univ State Key Lab Estuarine & Coastal Res 500 Dongchuan Rd Shanghai 200241 Peoples R China East China Normal Univ Sch Phys & Elect Sci Shanghai Key Lab Magnet Resonance Shanghai 200241 Peoples R China Shanxi Univ Collaborat Innovat Ctr Extreme Opt Taiyuan 030006 Shanxi Peoples R China NYU Shanghai NYU ECNU Inst Phys 3663 North Zhongshan Rd Shanghai 200062 Peoples R China

The growing concern over heavy metal pollution and its impact on the environment and human health has led to a proliferation of research on the detection and differentiation of heavy metal ions. A novel fluorescent sensor array utilizing only one single Ag-nanoclusters (Ag NCs) was developed for the efficient detection of six metal ions. The Ag NCs probe was prepared by using poly(methyl vinyl ether-alt-maleic acid) (PMVEM) as the ligand and has different fluorescence properties in water and dimethyl sulfoxide (DMSO). The interaction between metal ions and Ag NCs resulted in a characteristic fluorescence variation pattern which was subsequently analyzed using various tree-based machine learning models. We have compared different combinations of classification models and pre-processing methods of which the K-Nearest Neighbors Classifier with the first five linear discriminants has the highest accuracy. Through the integration of concentration models within a treebased pipeline optimization framework, six unique concentration regression models were selected for each metal ion. In addition, the developed sensor array could identify metal ions in binary mixtures. And it still kept high accuracy for the classification of six target metal ions in river water. In conclusion, the proposed framework was found to be effective in the detection of heavy metal ions in environmental samples, thus providing a promising approach for addressing heavy metal pollution.

关键词： Heavy metal ion detection Silver nanocluster Machine learning Fluorescent sensor array tree-based pipeline optimization tool

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Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain

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Neuro-oncology 2020年第3期22卷 393-401页

作者： Xiaorui Su Ni Chen Huaiqiang Sun Yanhui Liu Xibiao Yang Weina Wang Simin Zhang Qiaoyue Tan Jingkai Su Qiyong Gong Qiang Yue Huaxi MR Research Center Department of Radiology West China Hospital of Sichuan University Chengdu China. Huaxi Glioma Center West China Hospital of Sichuan University Chengdu China. Department of Pathology West China Hospital of Sichuan University Chengdu China. Department of Neurosurgery West China Hospital of Sichuan University Chengdu China. Department of Radiology West China Hospital of Sichuan University Chengdu China.

BACKGROUND:Conventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (autoML) approach to the MR radiomics features of patients with midline gliomas. METHODS:This single-institution retrospective study included 100 patients with midline gliomas, including 40 patients with H3 K27M mutations and 60 wild-type patients. Radiomics features were extracted from fluid-attenuated inversion recovery images. Prior to autoML analysis, the dataset was randomly stratified into separate 75% training and 25% testing cohorts. The tree-based pipeline optimization tool (TPOT) was applied to optimize the machine learning pipeline and select important radiomics features. We compared the performance of 10 independent TPOT-generated models based on training and testing cohorts using the area under the curve (AUC) and average precision to obtain the final model. An independent cohort of 22 patients was used to validate the best model. RESULTS:Ten prediction models were generated by TPOT, and the accuracy obtained with the best pipeline ranged from 0.788 to 0.867 for the training cohort and from 0.60 to 0.84 for the testing cohort. After comparison, the AUC value and average precision of the final model were 0.903 and 0.911 in the testing cohort, respectively. In the validation set, the AUC was 0.85, and the average precision was 0.855 for the best model. CONCLUSIONS:The autoML classifier using radiomics features of conventional MR images provides high discriminatory accuracy in predicting the H3 K27M mutation status of midline glioma.

关键词： H3 K27M mutation automated machine learning TPOT tree-based pipeline optimization tool autoML midline glioma

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