Novel methodology of fail-safe reliability-based topology optimization for large-scale marine structures

Cui, Yonghao; Yu, Yang; Huang, Shuling; Cheng, Siyuan; Wei, Mingxiu; Li, Zhenmian; Yu, Jianxing

doi:10.1007/s00158-023-03614-9

Cited by 4 publications

(1 citation statement)

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“…The Tennessee Eastman Process (TEP) is widely acknowledged as a formidable benchmark for assessing the efficacy of fault detection and false alarm monitoring strategies in complex industrial processes [1]. The precise identification of malfunctions and the mitigation of erroneous alerts are of utmost importance in guaranteeing optimal operational security and efficacy [2][3][4]. The primary objective of this scholarly research article is to elucidate the development of a cutting-edge soft computing-based approach known as the adaptive crow search algorithm (ACSA) aimed at the detection of faults and the mitigation of false alarms within the context of the TEP.…”

Section: Introductionmentioning

confidence: 99%

An adaptive metaheuristic optimization approach for Tennessee Eastman process for an industrial fault tolerant control system

Mustafa,

Ahmed,

Basit

et al. 2024

PLoS ONE

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The Tennessee Eastman Process (TEP) is widely recognized as a standard reference for assessing the effectiveness of fault detection and false alarm tracking methods in intricate industrial operations. This paper presents a novel methodology that employs the Adaptive Crow Search Algorithm (ACSA) to improve fault identification capabilities and mitigate the occurrence of false alarms in the TEP. The ACSA is an optimization approach that draws inspiration from the observed behavior of crows in their natural environment. This algorithm possesses the capability to adapt its search behavior in response to the changing dynamics of the optimization process. The primary objective of our research is to devise a monitoring strategy that is adaptable in nature, with the aim of efficiently identifying faults within the TEP while simultaneously minimizing the occurrence of false alarms. The ACSA is applied in order to enhance the optimization of monitoring variables, alarm thresholds, and decision criteria selection and configuration. When compared to traditional static approaches, the ACSA-based monitoring strategy is better at finding faults and reducing false alarms because it adapts well to changes in process dynamics and disturbances. In order to assess the efficacy of our suggested methodology, we have conducted comprehensive simulations on the TEP dataset. The findings suggest that the monitoring strategy based on ACSA demonstrates superior fault identification rates while concurrently mitigating the frequency of false alarms. In addition, the flexibility of ACSA allows it to efficiently manage process variations, disturbances, and uncertainties, thereby enhancing its robustness and reliability in practical scenarios. To validate the effectiveness of our proposed approach, extensive simulations were conducted on the TEP dataset. The results indicate that the ACSA-based monitoring strategy achieves higher fault detection rates while simultaneously reducing the occurrence of false alarms. Moreover, the adaptability of ACSA enables it to effectively handle process variations, disturbances, and uncertainties, making it robust and reliable for real-world applications. The contributions of this research extend beyond the TEP, as the adaptive monitoring strategy utilizing ACSA can be applied to other complex industrial processes. The findings of this study provide valuable insights into the development of advanced fault detection and false alarm monitoring techniques, offering significant benefits in terms of process safety, reliability, and operational efficiency.

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