Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults

Li, Xiaochuan; Mba, David; Delpha, Claude

doi:10.3390/en12040726

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…Building upon the research by Li et al, this research conducted a comparison of fault identification capabilities among the traditional Q contribution method, the T 2 contribution method, and the contribution method based on residuals of canonical variables [32]. In this section, the samples are consolidated into a dataset Y, rather than being partitioned into input and output categories.…”

Section: Contribution-based Fault Identificationmentioning

confidence: 99%

“…Additionally, in accordance with [32], the computation of variable contribution based on the T 2 statistical indicator can be expressed as:…”

Section: Q-based Contributionmentioning

confidence: 99%

“…Apart from the two contribution calculation methods mentioned above, [32] also introduced a contribution calculation approach based on canonical variable residuals (CVR). The central idea is to detect minor changes by examining the deviations between future and past canonical variables.…”

Section: Q-based Contributionmentioning

confidence: 99%

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MK-DCCA-Based Fault Diagnosis for Incipient Faults in Nonlinear Dynamic Processes

Wu,

Zhang,

Chen

2023

Processes

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Incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of mixed kernel principal components analysis and dynamic canonical correlation analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient faults in nonlinear and dynamic systems. Meanwhile, fault identification experiments were conducted, validating the high accuracy of the fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance.

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Section: Contribution-based Fault Identificationmentioning

confidence: 99%

“…Additionally, in accordance with [32], the computation of variable contribution based on the T 2 statistical indicator can be expressed as:…”

Section: Q-based Contributionmentioning

confidence: 99%

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MK-DCCA-Based Fault Diagnosis for Incipient Faults in Nonlinear Dynamic Processes

Wu,

Zhang,

Chen

2023

Processes

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“…Linear-based observation techniques and nonlinear-based observation algorithms are the main techniques used to design observation systems [22][23][24][25]. Linear observation systems such as proportional integral (PI) controllers have been applied in several systems for control and fault diagnosis, but their effectiveness in the presence of uncertainties is the main restriction of these algorithms [26].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy-Based Fault-Tolerant Control of a Continuum Robotic System for Maxillary Sinus Surgery

Piltan

Kim

2019

Applied Sciences

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Continuum robots represent a class of highly sensitive, multiple-degrees-of-freedom robots that are biologically inspired. Because of their flexibility and accuracy, these robots can be used in maxillary sinus surgery. The design of an effective procedure with high accuracy, reliability, robust fault diagnosis, and fault-tolerant control for a surgical robot for the sinus is necessary to maintain the high performance and safety necessary for surgery on the maxillary sinus. Thus, a robust adaptive hybrid observation method using an adaptive, fuzzy auto regressive with exogenous input (ARX) Laguerre Takagi–Sugeno (T–S) fuzzy robust feedback linearization observer for a surgical robot is presented. To address the issues of system modeling, the fuzzy ARX-Laguerre technique is represented. In addition, a T–S fuzzy robust feedback linearization observer is applied to a fuzzy ARX-Laguerre to improve the accuracy of fault estimation, reliability, and robustness for the surgical robot in the presence of uncertainties. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy observation-based feedback linearization technique is presented. The effectiveness of the proposed algorithm is tested with simulations. Experimental results show that the proposed method reduces the average position error from 35 mm to 2.45 mm in the presence of faults.

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MK-DCCA Based Fault Diagnosis for Incipient Fault in Nonlinear Dynamic Processes

Wu,

Zhang,

Gao

et al. 2023

Lecture Notes in Electrical Engineering

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Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults

Cited by 9 publications

References 28 publications

MK-DCCA-Based Fault Diagnosis for Incipient Faults in Nonlinear Dynamic Processes

MK-DCCA-Based Fault Diagnosis for Incipient Faults in Nonlinear Dynamic Processes

Adaptive Fuzzy-Based Fault-Tolerant Control of a Continuum Robotic System for Maxillary Sinus Surgery

MK-DCCA Based Fault Diagnosis for Incipient Fault in Nonlinear Dynamic Processes

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