Fault monitoring using novel adaptive kernel principal component analysis integrating grey relational analysis

Han, Yongming; Song, Guangliang; Liu, Fenfen; Geng, Zhiqiang; Ma, Bo; Xu, Wei

doi:10.1016/j.psep.2021.11.029

Cited by 45 publications

(9 citation statements)

References 34 publications

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“…While the (TGK) formula worked out a cumulative contribution ratio of (0.86321) with a number of (4) compounds. As noted when (q = 8, 15, 20, 30) and (n = 50), the (MGK) formula was also the best in its contribution ratios cumulative as it accomplished (0.86020, 0.89926, 0.88143, 0.89096) and with higher component numbers also (8,9,10,14) respectively according to dimensions (q) and when (q = 15, 20, 30) and (n = 200) we note that (MGK) was also the best in its cumulative contribution ratios, as it achieved (0.89292, 0.89066, 0.89447) and with relatively close numbers of components (10,20,21), respectively, according to dimensions (q), but in the case of the matrix (𝐻 2 ), the results showed the formula (MGK) was not good, but there are cases in which results did not appear, as in the case of (q = 15, n = 20), (q = 30, n = 50), (q = 30, n = 100). From The above results, it can be considered that the formula (MGK) is good in the case of the matrix (𝐻 1 ) estimated by the ROT method, but it cannot be generalized to the rest of the methods.…”

Section: Analysis For Kpc By Rotmentioning

confidence: 68%

Identification of Variables Affecting Levels of Salt Concentrations in Shatt Al-Arab Water Using Modified Kernel Principal Component Analysis

Husham Mohammed Albasri,

Abdul Hameed Ashour

2024

J. Phys.: Conf. Ser.

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This study paper is an attempt to bring to light a new approach in the treatment of the Gaussian function. The Gaussian function is considered the basis for building the elements of the kernel matrix within the methodology of the kernel principal components that aims to reduce the dimensions and then determine the most influential variables. Besides, it works on reducing the mathematical complexity that can arise because of multidimensionality, especially if it is the data suffers from a non-linear problem in describing the relationships. This research paper has included processing the introductory parameter matrix (H) by adopting two types of matrices, namely (H 1 diagonal, and H 2 hybrid diagonal). For achieving the benefit of this paper, it has been applied to the phenomenon of salt concentrations in Shatt al-Arab water in Basra Governorate through a number of climatic variables for identification of the most influential variables. The modified Gaussian function (MGK) was used and compared with the traditional method (TGK) by adopting two methods of estimating the introductory parameter matrix H. The simulation results brought to light that the (MGK) could not achieve better results than the (TGK) for any type of matrices (H 1 & H 2), estimated by the two methods (NS-R, ROT). Despite this fact, the (MGK) and (TGK) were consistent in determining the climatic variables most affected by the rise in salt concentrations when adopting the (NS-R) method, which are (air temperature, minimum temperature, maximum temperature, and solar brightness). While when adopting the (ROT), the (MGK) determined other variables, while the traditional method identified the same variables mentioned above.

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Section: Analysis For Kpc By Rotmentioning

confidence: 68%

Identification of Variables Affecting Levels of Salt Concentrations in Shatt Al-Arab Water Using Modified Kernel Principal Component Analysis

Husham Mohammed Albasri,

Abdul Hameed Ashour

2024

J. Phys.: Conf. Ser.

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“…Fault monitoring using novel adaptive kernel principal component analysis integrating grey relational analysis [33] Adaptive kernel principal component analysis integrating grey relational analysis (AKPCA-GRA)…”

Section: Iron Sulfide Oxidationmentioning

confidence: 99%

Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects

et al. 2023

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In the chemical process, abnormal situations are precursor events of incidents and accidents. Abnormal situation management (ASM) can effectively identify abnormalities and prevent them from evolving into incidents or accidents, ensuring the safe and smooth operation of chemical plants. In recent years, ASM has attracted extensive attention from the process industry and from academia, and a lot of research work has been conducted. However, the intelligence level of ASM in actual chemical plants is still relatively low, and industrial applications still face many difficulties and challenges. This review first summarizes the concepts and involved in the contents of ASM. Then, the latest research progress in various aspects of ASM is systematically reviewed. Finally, the challenges and future research directions of ASM are analyzed based on the perspective of industrial application. This review aims to provide the most cutting-edge reference for follow-up research on ASM, and to promote the intelligent development and practical industrial application of ASM in the chemical process.

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“…For the evaluation problem of the collision responses of honeycomb structures, researchers have proposed some common assessment indexes, such as maximum collision depth δ max and maximum absorption energy E max [12,14]. Grey relational analysis (GRA) is an attractive tool for addressing multi-index evaluation problems, successfully used in various engineering fields [37,38]. In this paper, GRA is used to evaluate the anti-impact performance of the honeycomb structure.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Intelligent Prediction Approach for Collision Responses of Honeycomb Reinforced Pipe Pile of the Offshore Platform

Yang

Lin

Han

et al. 2023

JMSE

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The potential collision between the ship and the pipe piles of the jacket structure brings huge risks to the safety of an offshore platform. Due to their high energy-absorbing capacity, honeycomb structures have been widely used as impact protectors in various engineering applications. This paper proposes a data-driven intelligent approach for the prediction of the collision response of honeycomb-reinforced structures under ship collision. In the proposed model, the artificial neural network (ANN) is combined with the dynamic particle swarm optimization (DPSO) algorithm to predict the collision responses of honeycomb reinforced pipe piles, including the maximum collision depth (δmax) and maximum absorption energy (Emax). Furthermore, a data-driven evaluation method, known as grey relational analysis (GRA), is proposed to evaluate the collision responses of the honeycomb-reinforced pipe piles of offshore platforms. Results of the case study demonstrate the accuracy of the DPSO-BP-ANN model, with measured mean-square-error (MSE) of 5.06 × 10−4 and 4.35 × 10−3 and R2 of 0.9906 and 0.9963 for δmax and Emax, respectively. It is shown that the GRA method can provide a comprehensive evaluation of the performance of a honeycomb structure under impact loads. The proposed model provides a robust and efficient assessment tool for the safe design of offshore platforms under ship collisions.

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Fault monitoring using novel adaptive kernel principal component analysis integrating grey relational analysis

Cited by 45 publications

References 34 publications

Identification of Variables Affecting Levels of Salt Concentrations in Shatt Al-Arab Water Using Modified Kernel Principal Component Analysis

Identification of Variables Affecting Levels of Salt Concentrations in Shatt Al-Arab Water Using Modified Kernel Principal Component Analysis

Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects

A Data-Driven Intelligent Prediction Approach for Collision Responses of Honeycomb Reinforced Pipe Pile of the Offshore Platform

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