Bayesian Optimization-Based Gradient Boosting Method of Fault Detection in Oil-Immersed Transformer and Reactors

Paul, Devaprasad; Goswami, Arup Kumar; Chetri, Rahul Lamichane; Roy, Rajesh Cherian; Sen, Pritin

doi:10.1109/tia.2021.3134140

Cited by 19 publications

(6 citation statements)

References 46 publications

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“…, 2020). The result of CBN was imported as the causal structure to XGBoost and analyzed with SHAP To solve this issue (Paul et al. , 2021; Yang et al.…”

Section: Cbn Model Inference and Resultsmentioning

confidence: 99%

“…It was used for reliability evaluation of tunnels (Liu et al, 2020), and complexity management in 2020 (Luo et al, 2020). The combination of BNs with XGBoost and machine learning methods emerged in 2021 in oil and gas projects (Borgheipour et al, 2021;Hao et al, 2021;Paul et al, 2021). In 2022, the BNs have been used for construction risk management (Arabi et al, 2022;Ji et al, 2022;Khanh et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, the problem with machine-learning models is that they cannot determine the Performance optimization of PPP projects causality of their outputs and are known as "black boxes" (Mannering et al, 2020). The result of CBN was imported as the causal structure to XGBoost and analyzed with SHAP To solve this issue (Paul et al, 2021;Yang et al, 2021). First, the model was run to measure the effect of each KPI on the model outcome.…”

Section: Xgboost and Shap Value Analysismentioning

confidence: 99%

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Evaluating and optimizing performance of public–private partnership projects using copula Bayesian network

Ghorbany

Yousefi

Noorzai

2022

ECAM

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PurposeBeing an efficient mechanism for the value of money, public–private partnership (PPP) is one of the most prominent approaches for infrastructure construction. Hence, many controversies about the performance effectiveness of these delivery systems have been debated. This research aims to develop a novel performance management perspective by revealing the causal effect of key performance indicators (KPIs) on PPP infrastructures.Design/methodology/approachThe literature review was used in this study to extract the PPPs KPIs. Experts’ judgment and interviews, as well as questionnaires, were designed to obtain data. Copula Bayesian network (CBN) has been selected to achieve the research purpose. CBN is one of the most potent tools in statistics for analyzing the causal relationship of different elements and considering their quantitive impact on each other. By utilizing this technique and using Python as one of the best programming languages, this research used machine learning methods, SHAP and XGBoost, to optimize the network.FindingsThe sensitivity analysis of the KPIs verified the causation importance in PPPs performance management. This study determined the causal structure of KPIs in PPP projects, assessed each indicator’s priority to performance, and found 7 of them as a critical cluster to optimize the network. These KPIs include innovation for financing, feasibility study, macro-environment impact, appropriate financing option, risk identification, allocation, sharing, and transfer, finance infrastructure, and compliance with the legal and regulatory framework.Practical implicationsIdentifying the most scenic indicators helps the private sector to allocate the limited resources more rationally and concentrate on the most influential parts of the project. It also provides the KPIs’ critical cluster that should be controlled and monitored closely by PPP project managers. Additionally, the public sector can evaluate the performance of the private sector more accurately. Finally, this research provides a comprehensive causal insight into the PPPs’ performance management that can be used to develop management systems in future research.Originality/valueFor the first time, this research proposes a model to determine the causal structure of KPIs in PPPs and indicate the importance of this insight. The developed innovative model identifies the KPIs’ behavior and takes a non-linear approach based on CBN and machine learning methods while providing valuable information for construction and performance managers to allocate resources more efficiently.

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“…, 2020). The result of CBN was imported as the causal structure to XGBoost and analyzed with SHAP To solve this issue (Paul et al. , 2021; Yang et al.…”

Section: Cbn Model Inference and Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Xgboost and Shap Value Analysismentioning

confidence: 99%

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Evaluating and optimizing performance of public–private partnership projects using copula Bayesian network

Ghorbany

Yousefi

Noorzai

2022

ECAM

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“…However, the Bayesian network realizes fault diagnosis through conditional probability inference. Reliable prior probabilities are required to ensure the accuracy of diagnosis results, which are considerably difficult to obtain in the event of complex faults in shipboard MVAC power systems [7,8]. The core idea of analytic models-based fault diagnosis is to construct a mathematical model that can express the logical relationship among electrical equipment, protection, and circuit breakers' actions.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Shipboard Medium‐Voltage Alternating Current Power System with Fault Recording Data‐Driven SE‐ResNet18‐1 Model

Peng,

Mu,

Fang

2023

IEEJ Transactions Elec Engng

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The electrification trend of marine vessels has led to the rapid development of all‐electric ships. Yet the unique topology and harsh working environment of shipboard power system have posed significant challenges to its fault diagnosis. This paper addresses the research gap in fault diagnosis for shipboard medium‐voltage alternating current (MVAC) power systems and proposes a machine learning‐based fault diagnosis scheme. Signals from fault recorders before relay triggering are used as input for the neural network model. By combining the residual neural network (ResNet) with Squeeze‐and‐Excitation (SE) Block, the feature extraction capability of the network model is maximized. Utilizing the fault transient information, the proposed scheme can realize real‐time diagnosis of any large disturbance to the power system. The data set for scheme verification is established through simulation experiments on PSCAD/EMTDC platform, while model training is completed under the PyTorch framework. The experimental results show that the proposed fault diagnosis scheme can effectively discriminate all types of faults from non‐fault conditions in shipboard MVAC power system. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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“…Bayesian optimization is an approach technique for searching for the optimum value of a function by using the probabilistic of the overall search and evaluating the function [24,25], Bayesian will use the theory of Bayesian probability for an iterative model so that it can have the advantage of updating initial knowledge [26,27]. This research can help in improving the model that ignores text or information that has important value in producing a summary.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Text Summarization with a T5 Model and Bayesian Optimization

Lubis,

Safitri,

Irvan

et al. 2023

RIA

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At present the habits and interests of individuals in obtaining information by reading large amounts of information have changed at the stage of reading information more concisely, but these changes have challenges such as the nature of the data which is still unstructured making it difficult to summarize text. This study applies a data cleaning process with text processing and manually annotates to divide the data into summary data and text data so that it can be used for the process of implementing the T5 model and Bayesian optimization. In the implementation of Bayesian optimization using the prior distribution and likelihood parameters. In implementing the T5 model there will be several stages such as processing training and test data then Decodification and Post-Processing processes. The results of this study were obtained using the ROUGE evaluation technique which resulted in an increased evaluation value. The T5 model produces a ROUGE 1 value with an average value of 0.42, ROUGE-2 has a value of 0.55 and ROUGE-L has a value of 0.46 while applying Bayesian optimization produces a ROUGE-1 evaluation with an average value of 0.53 ROUGE-2 has a value of 0.55 and ROUGE-L has a value of 0.59.

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Bayesian Optimization-Based Gradient Boosting Method of Fault Detection in Oil-Immersed Transformer and Reactors

Cited by 19 publications

References 46 publications

Evaluating and optimizing performance of public–private partnership projects using copula Bayesian network

Evaluating and optimizing performance of public–private partnership projects using copula Bayesian network

Fault Diagnosis of Shipboard Medium‐Voltage Alternating Current Power System with Fault Recording Data‐Driven SE‐ResNet18‐1 Model

Enhancing Text Summarization with a T5 Model and Bayesian Optimization

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