Enhancing pipeline integrity: a comprehensive review of deep learning-enabled finite element analysis for stress corrosion cracking prediction

Sarwar, Umair; Mokhtar, Ainul Akmar; Muhammad, Masdi; Wassan, Rano Khan; Soomro, Afzal Ahmed; Wassan, Majid Ali; Kaka, Shuaib

doi:10.1080/19942060.2024.2302906

Cited by 2 publications

(3 citation statements)

References 108 publications

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“…The degree to which observation data are affected by environmental interference can be calculated by equations ( 5) and ( 6), as shown in figure 8. Then, the initial values of the attribute reliability can be calculated by equation (7) as 0.1911 and 0.6933, respectively. This corresponds to the observation data of the spacecraft flywheel system shown in figure 7.…”

Section: Model Constructionmentioning

confidence: 99%

“…Obviously, the global method based on the tolerance range for calculating attribute reliability is not able to identify sudden perturbations in the system efficiently compared with the local method based on the distances proposed in this paper. However, there can still be some inaccuracy in the attribute reliability calculated by equations ( 5)- (7) because the observed data are affected by the environment. Thus, the interpretability constraints are set by expert knowledge and then further calibrated by the optimization algorithm.…”

Section: Nomentioning

confidence: 99%

“…In addition, they are able to learn and adapt to new data, i.e. by analyzing new observation data, they dynamically adjust their intrinsic parameters to continuously improve their performance [7]. Wang et al predicted the spatiotemporal distribution characteristics of O3 and its precursors using a convolutional neural network model [8].…”

Section: Introductionmentioning

confidence: 99%

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An interpretable spacecraft flywheel system health status assessment method under perturbation

Zhang,

He,

et al. 2024

Meas. Sci. Technol.

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Health status assessment is an important measure for maintaining the safety of spacecraft flywheel systems. The influence of noise, sensor quality, and other disturbance factors can lead to a decrease in the reliability of the collected information. This can affect the model accuracy. Moreover, a loss of belief in the model is frequently caused by the opaque nature of the procedure and the incomprehensibility of the outcomes, particularly in fields such as aerospace. It is urgent to maintain the interpretability of the model and successfully identify the unreliability of the observed data. Therefore, this paper proposes a spacecraft flywheel system health status assessment method under perturbation based on interpretable belief rule base with attribute reliability (IBRB-r). First, the attribute reliability is calculated based on the average distance method, and a new fusion method of attribute reliability is proposed to reduce the interference of unreliable information. Then, a new interpretable constraint strategy is proposed to improve the rationality and interpretability of the parameters. Finally, the proposed method is validated by a case study of the health status assessment of a spacecraft flywheel system. Experiments show that the IBRB-r maintains high accuracy and interpretability under unreliable observation data.

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Section: Model Constructionmentioning

confidence: 99%

Section: Nomentioning

confidence: 99%

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An interpretable spacecraft flywheel system health status assessment method under perturbation

Zhang,

He,

et al. 2024

Meas. Sci. Technol.

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A mechanistic deterioration point assignment model for water pipe condition assessment

Zhu,

Wang,

Feng

et al. 2024

AQUA — Water Infrastructure, Ecosystems and Society

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A pipe condition assessment model is required to implement effective and economical planned maintenance of the water distribution system. The application of such a model requires sufficient accuracy, which, however, is limited by the complexity of the pipe deterioration process and storage capacity of the water utility. The majority of previous studies have focused on the improvement of assessment algorithms for data mining. In this study, a mechanistic deterioration point assignment (MDPA) model is developed to make advancements in the modes of data input and result output to enhance the model's accuracy and application scope for cast iron and steel pipes. In this MDPA model, (1) indicators/sub-indicators on external corrosion, external load, internal corrosion, and internal load are constructed and can be obtained by data estimation or techniques and (2) assessment results include both pipe overall condition and detailed conditions on pipe corrosion and load, offering evidence for primary maintenance measures. The weights of the indicators/sub-indicators are estimated using the Bayesian statistics theory. The modelling results of pipe samples demonstrate that this MDPA model is an effective tool for pipe condition assessment.

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Enhancing pipeline integrity: a comprehensive review of deep learning-enabled finite element analysis for stress corrosion cracking prediction

Cited by 2 publications

References 108 publications

An interpretable spacecraft flywheel system health status assessment method under perturbation

An interpretable spacecraft flywheel system health status assessment method under perturbation

A mechanistic deterioration point assignment model for water pipe condition assessment

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