Weighted Linear Local Tangent Space Alignment via Geometrically Inspired Weighted PCA for Fault Detection

Shah, Muhammad Zohaib Hassan; Hu, Li-Sheng; Ahmed, Zahoor

doi:10.1109/tii.2022.3166784

Cited by 19 publications

(3 citation statements)

References 31 publications

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“…By utilizing the ACSA, the proposed approach takes advantage of its ability to dynamically adapt the search parameters, striking a balance between exploration and exploitation. This adaptive nature allows our method to effectively handle the complexities and variations present in real-world industrial systems, leading to improved fault detection capabilities as compared to other statistical approaches presented in [ 24 , 50 , 51 ].…”

Section: Introductionmentioning

confidence: 99%

“…Through the ACSA-based optimization process, our method finds an optimal balance between sensitivity and specificity. This robust optimization enables a significant reduction in false alarm occurrences, contributing to a more reliable and efficient operation of industrial systems [ 50 , 51 ].…”

Section: Introductionmentioning

confidence: 99%

“…This adaptive modification ensures that the chosen attributes are in line with the changing traits of the TEP, hence improving the overall resilience of our approach. Comparative assessments comparing statistical approaches in [ 24 , 50 , 51 , 56 ] procedures with ACSA demonstrate the shortcomings of traditional methods in adjusting to dynamic industrial processes, hence emphasizing the suitability of ACSA in our specific situation.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Linear local tangent space alignment with autoencoder

Ran

Wang

Fang

2023

Complex Intell. Syst.

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Linear local tangent space alignment (LLTSA) is a classical dimensionality reduction method based on manifold. However, LLTSA and all its variants only consider the one-way mapping from high-dimensional space to low-dimensional space. The projected low-dimensional data may not accurately and effectively “represent” the original samples. This paper proposes a novel LLTSA method based on the linear autoencoder called LLTSA-AE (LLTSA with Autoencoder). The proposed LLTSA-AE is divided into two stages. The conventional process of LLTSA is viewed as the encoding stage, and the additional and important decoding stage is used to reconstruct the original data. Thus, LLTSA-AE makes the low-dimensional embedding data “represent” the original data more accurately and effectively. LLTSA-AE gets the recognition rates of 85.10, 67.45, 75.40 and 86.67% on handwritten Alphadigits, FERET, Georgia Tech. and Yale datasets, which are 9.4, 14.03, 7.35 and 12.39% higher than that of the original LLTSA respectively. Compared with some improved methods of LLTSA, it also obtains better performance. For example, on Handwritten Alphadigits dataset, compared with ALLTSA, OLLTSA, PLLTSA and WLLTSA, the recognition rates of LLTSA-AE are improved by 4.77, 3.96, 7.8 and 8.6% respectively. It shows that LLTSA-AE is an effective dimensionality reduction method.

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Multi-scale adaptive multivariate state estimation fault detection enhancement for time-varying industrial system based on multi-output Gaussian process autoregression

Liu,

Xu,

Zhao

et al. 2023

Computers & Industrial Engineering

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Weighted Linear Local Tangent Space Alignment via Geometrically Inspired Weighted PCA for Fault Detection

Cited by 19 publications

References 31 publications

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

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

Linear local tangent space alignment with autoencoder

Multi-scale adaptive multivariate state estimation fault detection enhancement for time-varying industrial system based on multi-output Gaussian process autoregression

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