A kernel canonical correlation analysis approach for removing environmental and operational variations for structural damage identification

Huang, Jinhai; Yuan, Sijie; Li, Dongsheng; Li, Hong‐Nan

doi:10.1016/j.jsv.2022.117516

Cited by 12 publications

(3 citation statements)

References 43 publications

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“…Residuals of canonical variables follow a multivariate normal distribution with zero mean, and the covariance matrix is given by Equation (25). Therefore, it is reasonable to utilize the following statistical data for detection purposes:…”

Section: Dynamic Canonical Correlation Analysismentioning

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: Dynamic Canonical Correlation Analysismentioning

confidence: 99%

“…Reference [12] first employed data-driven CCA techniques to achieve residual generation based on canonical correlation, yielding favorable fault detection results. Subsequently, CCA-based methods have been extensively researched and improved by numerous scholars [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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

Wu,

Zhang,

Chen

2023

Processes

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“…Therefore, the detection of abnormal structural dynamic characteristics needs to firstly eliminate the normal variation component caused by variable action. Hang et al proposed an abnormality detection method that integrates kernel canonical correlation analysis and cointegration theory to deal with the nonlinearity of monitoring data in practical engineering [20]. Wang et al used a method based on the Bayesian framework to achieve data component separation and structural state identification [21].…”

Section: Introductionmentioning

confidence: 99%

Study on Abnormal Pattern Detection Method for In-Service Bridge Based on Lasso Regression

Zhong,

Hu,

Hou

et al. 2024

Applied Sciences

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The real-time operational safety of in-service bridges has received wide attention in recent years. By fully utilizing the health monitoring data of bridges, a structural abnormal pattern detection method based on data mining can be established to effectively ensure the safety of in-service bridges. This paper takes a large-span arch bridge as the research object, analyzes the time-based variation of the main monitoring data of the structure, establishes Lasso regression models for load characteristic indicators and vertical bending fundamental frequency of the structure under different time scales, and uses the residuals of the Lasso model to indicate the structural state and identify abnormal patterns. Firstly, the monitoring data of bridge structural temperature, girder end displacement, and girder acceleration were analyzed, and the interrelationships were studied to extract characteristic parameters of structural load characteristics and structural frequency. Then, the time-varying patterns of structural response were analyzed, and Lasso regression models and their regression variables were discussed based on monitoring data under two different time scales: daily cycle and annual cycle. The abnormal pattern detection method for bridge structures was developed. Finally, the effectiveness of this method was verified by taking the bridge deck pavement replacement as the abnormal pattern. The research results indicate that the proposed bridge structure abnormal pattern detection method based on Lasso regression can effectively monitor changes in the state of the bridge, and the residual dispersion of the model established on the annual cycle scale is relatively smaller than that on the daily cycle scale, resulting in better abnormal detection performance.

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Voice Pathology Detection Based on Canonical Correlation Analysis Method Using Hilbert–Huang Transform and LSTM Features

Er,

İlhan

2024

Arab J Sci Eng

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A kernel canonical correlation analysis approach for removing environmental and operational variations for structural damage identification

Cited by 12 publications

References 43 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

Study on Abnormal Pattern Detection Method for In-Service Bridge Based on Lasso Regression

Voice Pathology Detection Based on Canonical Correlation Analysis Method Using Hilbert–Huang Transform and LSTM Features

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