Inverse fuzzy fault model for fault detection and isolation with least angle regression for variable selection

Márquez-Vera, Marco Antonio; Ramos-Velasco, Luis Enrique; López-Ortega, Omar; Zúñiga-Peña, Nadia Samantha; Ramos-Fernández, Julio C.; Ortega-Mendoza, Rosa María

doi:10.1016/j.cie.2021.107499

Cited by 14 publications

(3 citation statements)

References 51 publications

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“…The idea of the LARS model to solve Equation ( 13) is to initialize all variable values of X to zero first and make predictions about y by finding the independent variable x i ∈ X with the highest correlation with y [41,42]. The LARS model is then solved by finding the independent variable xi with the highest correlation with the current residuals.…”

Section: Least Angle Regression For Adaptive Feature Selectionmentioning

confidence: 99%

Enhancing Underwater Image Quality Assessment with Influential Perceptual Features

Liu,

Huang,

Xie

et al. 2023

Electronics

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In the multifaceted field of oceanic engineering, the quality of underwater images is paramount for a range of applications, from marine biology to robotic exploration. This paper presents a novel approach in underwater image quality assessment (UIQA) that addresses the current limitations by effectively combining low-level image properties with high-level semantic features. Traditional UIQA methods predominantly focus on either low-level attributes such as brightness and contrast or high-level semantic content, but rarely both, which leads to a gap in achieving a comprehensive assessment of image quality. Our proposed methodology bridges this gap by integrating these two critical aspects of underwater imaging. We employ the least-angle regression technique for balanced feature selection, particularly in high-level semantics, to ensure that the extensive feature dimensions of high-level content do not overshadow the fundamental low-level properties. The experimental results of our method demonstrate a remarkable improvement over existing UIQA techniques, establishing a new benchmark in both accuracy and reliability for underwater image assessment.

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Section: Least Angle Regression For Adaptive Feature Selectionmentioning

confidence: 99%

Enhancing Underwater Image Quality Assessment with Influential Perceptual Features

Liu,

Huang,

Xie

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Yang et al [13] employed a CNN to evaluate the state of HVCBs, thereby avoiding manual feature extraction and improving the assessment accuracy. In addition, M.A.Márquez-Vera et al [14] proposed an inverse fuzzy fault model to detect and isolate faults which has only four fuzzy rules and shows a smaller isolation time than that required using the fuzzy classifier. Zhang et al [15] proposed multi-sensor information fusion which can achieve more accurate identification of mechanical faults for high-voltage circuit breakers with higher speed.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Fault Diagnosis of High-Voltage Circuit Breakers with Dynamic Multi-Attention Graph Convolutional Networks Based on Adaptive Graph Construction

Sui,

Yan,

et al. 2024

Applied Sciences

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With the rapid development of deep learning, its powerful capabilities make it possible to perform mechanical fault diagnosis of high-voltage circuit breakers (HVCBs). Among deep learning approaches, the convolutional neural network is widely used. However, while it can extract features effectively, it also has some limitations. Specifically, it depends on a large number of training data and only takes data information into account without considering structural information. These shortcomings lead to unused information and unsatisfactory model results. To address these shortcomings, this paper proposes AKNN-DMGCN, a novel dynamic multi-attention graph convolutional network based on an adaptively constructed graph, which can achieve high accuracy and robust mechanical fault diagnosis of HVCBs. First, a novel adaptive k-nearest neighbor (AKNN) graph construction method is proposed to construct informative graphs. The AKNN method can mine the relationship between the original data samples and utilize the data and label information. Thus, it has high fault tolerance to noise signals and can construct a structure graph with rich and accurate information, which can improve the overall model performance. Then, a dynamic multi-attention graph convolutional network (DMGCN) is applied for mechanical fault diagnosis of HVCBs. DMGCN fully utilizes structural and numerical information representing HVCB signals to perform classification. DMGCN has a dynamic multi-attention mechanism with strong expressive ability, which allows it to achieve high diagnostic accuracy. The experimental results indicate that the accuracy of AKNN-DMGCN reaches 97.22% on a balanced dataset and 95.01% on an imbalanced dataset, which demonstrates that the proposed method is effective for both balanced and imbalanced samples.

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“…A fault is an undesired behabior of a system [1], being the fault detection the capability in recognizing an anomalous behavior, and the fault isolation is to know what fault is affecting the system [2]. There are some approaches used for fault detection and isolation (FDI) like principal component analysis [1], artificial neural networks [3], fuzzy systems [4]. In this work is shown the use of recurrent neural networks (RNN) which are simplets than deep learning and they can use past information to recognize the signals evolution early in time.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and isolation in the DAMADICS system using recurrent neural networks

Avila-Diaz,

Márquez-Vera

2023

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Inverse fuzzy fault model for fault detection and isolation with least angle regression for variable selection

Cited by 14 publications

References 51 publications

Enhancing Underwater Image Quality Assessment with Influential Perceptual Features

Enhancing Underwater Image Quality Assessment with Influential Perceptual Features

Mechanical Fault Diagnosis of High-Voltage Circuit Breakers with Dynamic Multi-Attention Graph Convolutional Networks Based on Adaptive Graph Construction

Fault detection and isolation in the DAMADICS system using recurrent neural networks

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