Early detection of faults in HVAC systems using an XGBoost model with a dynamic threshold

Chakraborty, Debaditya; Elzarka, Hazem

doi:10.1016/j.enbuild.2018.12.032

Cited by 122 publications

(40 citation statements)

References 16 publications

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“…Hence, the new regression trees are tending to a maximum correlated to the negative of the gradient of the loss function, which not only improves the flexibility of the algorithm, but also converges on the loss function. The gradient boosting can be expressed as [47]:ŷ…”

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

“…The XGBoost algorithm possesses low computational complexity, high accuracy, and fast running speed for any input data set size due to utilization of the central processing unit (CPU) with multi-threaded parallel computing. These methods are effectively used in various fields, such as prediction of environmental condition, detection of medical symptoms, and diagnosis of machine faults [47][48][49].…”

Section: Introductionmentioning

confidence: 99%

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Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

Toma

Kim

2020

Applied Sciences

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Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motors (IMs) utilizing the motor current signal that uses the discrete wavelet transform (DWT) for feature extraction. Three wavelets (db4, sym4, and Haar) are used to decompose the current signal, and several features are extracted from the decomposed coefficients. In the pre-processing stage, notch filtering is used to remove the line frequency component to improve classification performance. Finally, the two ensemble machine learning (ML) classifiers random forest (RF) and extreme gradient boosting (XGBoost) are trained and tested using the extracted feature set to classify the bearing fault condition. Both classifier models demonstrate very promising results in terms of accuracy and other accepted performance indicators. Our proposed method achieves an accuracy slightly greater than 99%, which is better than other models examined for the same dataset.

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Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

Toma

Kim

2020

Applied Sciences

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show abstract

“…The results indicated that the proposed approach was robust in various wind turbine models, including offshore ones, under different working conditions. Chakraborty and Elzarka [41] developed an XGBoost model with a dynamic threshold for early detection of faults in Heating Ventilation and Air Conditioning (HVAC) systems. Zhang et al [42] applied the XGboost algorithm to the fault diagnosis of rolling bearings, and the results showed that the XGboost algorithm was superior to other tree algorithms in accuracy and time.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Lei

Jiang

et al. 2019

Processes

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A novel fault diagnosis method is proposed, depending on a cloud service, for the typical faults in the hydraulic directional valve. The method, based on the Machine Learning Service (MLS) HUAWEI CLOUD, achieves accurate diagnosis of hydraulic valve faults by combining both the advantages of Principal Component Analysis (PCA) in dimensionality reduction and the eXtreme Gradient Boosting (XGBoost) algorithm. First, to obtain the principal component feature set of the pressure signal, PCA was utilized to reduce the dimension of the measured inlet and outlet pressure signals of the hydraulic directional valve. Second, a machine learning sample was constructed by replacing the original fault set with the principal component feature set. Third, the MLS was employed to create an XGBoost model to diagnose valve faults. Lastly, based on model evaluation indicators such as precision, the recall rate, and the F1 score, a test set was used to compare the XGBoost model with the Classification And Regression Trees (CART) model and the Random Forests (RFs) model, respectively. The research results indicate that the proposed method can effectively identify valve faults in the hydraulic directional valve and have higher fault diagnosis accuracy.

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“…XGBoost, as a strong classification model in machine learning, has been widely applied in fault diagnosis [34]. Moreover, it has been reported that this approach can successfully detect faults in industrial fields [35]. Therefore, XGBoost was also applied to detect faults for comparison.…”

Section: Resultsmentioning

confidence: 99%

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

et al. 2020

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It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM method for wind turbine fault detections. To this end, the realization of feature selection for fault detection is firstly achieved by utilizing the maximum information coefficient to analyze the correlation among features in supervisory control and data acquisition (SCADA) of wind turbines. After that, a performance evaluation criterion is proposed for the improved LightGBM model to support fault detections. In this scheme, by embedding the confusion matrix as a performance indicator, an improved LightGBM fault detection approach is then developed. Based on the adaptive LightGBM fault detection model, a fault detection strategy for wind turbine gearboxes is investigated. To demonstrate the applications of the proposed algorithms and methods, a case study with a three-year SCADA dataset obtained from a wind farm sited in Southern China is conducted. Results indicate that the proposed approaches established a fault detection framework of wind turbine systems with either lower false alarm rate or lower missing detection rate.

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Early detection of faults in HVAC systems using an XGBoost model with a dynamic threshold

Cited by 122 publications

References 16 publications

Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

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