An Extreme Gradient Boosting Aided Fault Diagnosis Approach: A Case Study of Fuse Test Bench

Alfarizi, Muhammad Gibran; Vatn, Jrn; Yin, Shen

doi:10.1109/tai.2022.3165137

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…According to preliminary analyses conducted for this study, tree-based ML structures like Random Forests (RF) and eXtreme Gradient Boosting can perform better than identical models [37]. Deep neural networks and ANNs in general also showed encouraging outcomes with increased computing load.…”

Section: Proposed Algorithmmentioning

confidence: 78%

“…In a different research [32], an XGB method were introduced to increase fault ID precision, combining an improved genetic algorithm (IGA) with the XGBoost to create a hybrid diagnostic network. Gibran et al [37] also proposed an integrated fault diagnosis system using the extreme gradient boosting algorithm for a fuse test bench line. The article concludes that the proposed method achieves high classification accuracy, fast diagnosis time, and interpretable root cause analysis.…”

Section: Decision Trees (Dt)mentioning

confidence: 99%

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Intelligent Fault Diagnosis of Manufacturing Processes Using Extra Tree Classification Algorithm and Feature Selection Strategies

Yousefi,

Yin,

Alfarizi

2023

IEEE Open J. Ind. Electron. Soc.

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Fault diagnosis is integral to maintenance practices, ensuring optimal machinery functionality. While traditional methods relied on human expertise, Intelligent Fault Diagnosis (IFD) techniques, propelled by Machine Learning (ML) advancements, now offer automated fault identification. Despite their efficiency, a research gap exists, emphasizing the need for methods providing not just reliable fault identification but also in-depth causal factor analysis. This research introduces a novel approach using an Extra Tree classification algorithm and feature selection to identify fault importance in manufacturing processes. Compared with SVM, neural networks, and tree-based ML, the method enhances training and computational efficiency, achieving over 99% classification accuracy on PHM 2021 dataset. Importantly, the algorithm enables researchers to analyze individual fault causes, addressing a critical research gap. The study provides guidelines for further research, aiming to refine the proposed strategy. This work contributes to advancing fault diagnosis methodologies, combining automation with comprehensive causal analysis, crucial for both academic and industrial applications.

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Section: Proposed Algorithmmentioning

confidence: 78%

Section: Decision Trees (Dt)mentioning

confidence: 99%

Intelligent Fault Diagnosis of Manufacturing Processes Using Extra Tree Classification Algorithm and Feature Selection Strategies

Yousefi,

Yin,

Alfarizi

2023

IEEE Open J. Ind. Electron. Soc.

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“…The data-driven diagnostic method requires high professional knowledge and experience, and requires a lot of manpower and time [8,9]. With the continuous evolution of modern industrialization, fault monitoring equipment has begun to gather large and diverse data sets, covering various types of information, which poses unprecedented challenges to traditional fault diagnosis methods [10].…”

Section: Introductionmentioning

confidence: 99%

A new dual-channel convolutional neural network and its application in rolling bearing fault diagnosis

Hu,

Liu,

Zhao

et al. 2024

Meas. Sci. Technol.

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Recently, deep learning has received widespread attention in the field of bearing fault diagnosis due to its powerful feature learning capability. However, when the actual working conditions are complex and variable, the fault information in a single domain is limited, making it difficult to achieve high accuracy. To overcome these challenges, this paper proposes a bearing fault diagnosis method based on the Markov transition field, continuous wavelet transform (CWT), and dual-channel convolutional neural network (CNN). The method combines the descriptive ability of the Markov model for state transfer, the time-frequency analysis ability of CWT for signal, and the excellent performance of CNN with attention mechanism in feature extraction and classification. Specifically, we first propose a multi-channel Markov transition field method, which is combined with CWT to obtain two different representations of two-dimensional (2D) images. To comprehensively mine fault information, we further propose a dual-channel CNN with an attention mechanism. The design of this network structure aims to extract multi-level features from two types of 2D images. At the same time, we designed and embedded an attention mechanism to enable the network to focus more on extracting effective features, thereby improving the performance and accuracy of the network. To verify the effectiveness of the proposed method, three datasets were used for empirical research. The results show that this method exhibits superior performance in bearing fault diagnosis and has higher accuracy compared to traditional methods.

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Transfer and supplement AdaBoost for extracting region proposals of CNN in transfer-learning application

Li,

Liu

2023

Multimed Tools Appl

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An Extreme Gradient Boosting Aided Fault Diagnosis Approach: A Case Study of Fuse Test Bench

Cited by 12 publications

References 32 publications

Intelligent Fault Diagnosis of Manufacturing Processes Using Extra Tree Classification Algorithm and Feature Selection Strategies

Intelligent Fault Diagnosis of Manufacturing Processes Using Extra Tree Classification Algorithm and Feature Selection Strategies

A new dual-channel convolutional neural network and its application in rolling bearing fault diagnosis

Transfer and supplement AdaBoost for extracting region proposals of CNN in transfer-learning application

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