Gas turbine shaft unbalance fault detection by using vibration data and neural networks

Tajik, Mostafa; Movasagh, Shirin; Shoorehdeli, Mahdi Aliyari; Yousefi, Iman

doi:10.1109/icrom.2015.7367802

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…Undersampling of major classes and oversampling of minor classes form the basis of the solution commonly proposed for the problem. References (Tajik et al, 2015;Zhang et al, 2015;Duan et al, 2016) discuss fault detection models with an imbalanced training dataset in the industrial and manufacturing sector. Resampling methods (oversampling, undersampling, and hybrid sampling) (Chawla et al, 2002;Han et al, 2005;Cateni et al, 2014;Sun et al, 2015;Nekooeimehr et al, 2016), feature selection and extraction, cost-sensitive learning, and ensemble methods (Krawczyk, 2016;Guo et al, 2017) are other approaches to deal with the class-imbalance problem.…”

Section: Class Imbalancementioning

confidence: 99%

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

Tripathi

Muhr

Manuel

et al. 2021

Front. Artif. Intell.

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The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely accepted framework in production and manufacturing. This data-driven knowledge discovery framework provides an orderly partition of the often complex data mining processes to ensure a practical implementation of data analytics and machine learning models. However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data- and model development-related issues. These issues need to be carefully addressed by allowing a flexible, customized and industry-specific knowledge discovery framework. For this reason, extensions of CRISP-DM are needed. In this paper, we provide a detailed review of CRISP-DM and summarize extensions of this model into a novel framework we call Generalized Cross-Industry Standard Process for Data Science (GCRISP-DS). This framework is designed to allow dynamic interactions between different phases to adequately address data- and model-related issues for achieving robustness. Furthermore, it emphasizes also the need for a detailed business understanding and the interdependencies with the developed models and data quality for fulfilling higher business objectives. Overall, such a customizable GCRISP-DS framework provides an enhancement for model improvements and reusability by minimizing robustness-issues.

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Section: Class Imbalancementioning

confidence: 99%

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

Tripathi

Muhr

Manuel

et al. 2021

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…Undersampling of major classes and oversampling of minor classes form the basis of the solution commonly proposed for the problem. References [105,17,126] discuss fault detection models with an imbalanced training dataset in the industrial and manufacturing sector. Resampling methods (oversampling, undersampling, and hybrid sampling) [13,32,77,102,10], feature selection and extraction, cost-sensitive learning, and ensemble methods [30,55] are other approaches to deal with the class-imbalance problem.…”

Section: Class Imbalancementioning

confidence: 99%

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

Tripathi¹,

Muhr²,

Manuel³

et al. 2020

Preprint

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The implementation of robust, stable, and user-centered data analytics and machine learning models is confronted by numerous challenges in production and manufacturing. Therefore, a systematic approach is required to develop, evaluate, and deploy such models. The data-driven knowledge discovery framework provides an orderly partition of the data-mining processes to ensure the practical implementation of data analytics and machine learning models. However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data-and model-development-related issues. These issues should be carefully addressed by allowing a flexible, customized, and industry-specific knowledge discovery framework; in our case, this takes the form of the cross-industry standard process for data mining (CRISP-DM). This framework is designed to ensure active cooperation between different phases to adequately address data-and model-related issues. In this paper, we review several extensions of CRISP-DM models and various data-robustness-and model-robustness-related problems in machine learning, which currently lacks proper cooperation between data experts and business experts because of the limitations of data-driven knowledge discovery models.

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“…Finally, the imbalance fault is detected by the computation of statistics indices in the principal and residual subspaces of principal component analysis (PCA) under different fixed flow conditions. In [33][34][35][36], the proposed method is used to denoise based on empirical mode decomposition (EMD). The methods mentioned above are applied to detect the blade imbalance fault of an MCT, such as EMD and wavelet transform (WT).…”

Section: Introductionmentioning

confidence: 99%

Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

Wei

Xie

Shi

et al. 2021

JMSE

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Marine current energy as a kind of renewable energy has gradually attracted more and more attention from many countries. However, the blade imbalance fault of marine current turbines (MCTs) will have an effect on the power production efficiency and cause damage to the MCT system. It is hard to classify the severity of an MCT blade imbalance fault under the condition of the current instability and seafloor noise. This paper proposes a fault classification method based on the combination of variational mode decomposition denoising (VMD denoising) and screening linear discriminant analysis (S-LDA). The proposed method consists of three parts. Firstly, phase demodulation of the collected stator current signal is performed by the Hilbert transform (HT) method. Then, the obtained demodulation signal is denoised by variational mode decomposition denoising (VMD denoising), and the denoised signal is analyzed by power spectral density (PSD). Finally, S-LDA is employed on the power signal to determine the severities of fault classification. The effectiveness of the proposed method is verified by experimental results under different severities of blade imbalance fault. The stator current signatures of experiments with different severities of blade imbalance fault are used to validate the effectiveness of the proposed method. The fault classification accuracy is 92.04% based on the proposed method. Moreover, the experimental results verify that the influence of velocity fluctuation on fault classification can be eliminated.

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Gas turbine shaft unbalance fault detection by using vibration data and neural networks

Cited by 10 publications

References 17 publications

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

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