Detection of nonlinearity effects in structural integrity monitoring methods for offshore jacket-type structures based on principal component analysis

Mojtahedi, Alireza; Lotfollahi-Yaghin, Mohammad Ali; Ettefagh, Mir Mohammad; Hassanzadeh, Yousef; Fujikubo, Masahiko

doi:10.1016/j.marstruc.2013.04.007

Cited by 22 publications

(11 citation statements)

References 19 publications

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“…They introduced more differentiable damage features based on transformed FRF by PCA. Mojtahedi et al 35 used PCA for the detection of nonlinearity in the recorded data of an instrumented model of offshore jacket‐type structure. Ha and Golinval 36 investigated the sensitivity of PCs of FRF data of a beam model.…”

Section: Introductionmentioning

confidence: 99%

Structural damage detection using principal component analysis of frequency response function data

Esfandiari

Nabiyan

Rofooei

2020

Struct Control Health Monit

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Summary In this paper, a new sensitivity‐based model updating method is presented based on the changes of principal components (PCs) of frequency response function (FRF). Structural damage estimation, identification of damage location and severity, is conducted by an innovative sensitivity relation. The sensitivity relation is derived by incorporating PC analysis (PCA) data obtained from the incomplete measured structural responses in a mathematical formulation and is then solved by the least square method. In order to demonstrate the performance of the proposed method, it is applied to a truss and a frame model. The results prove the ability of the method as a robust damage detection algorithm in the presence of measurement and mass modeling errors. The comparative studies prove that the results obtained by the proposed sensitivity relation are more accurate than the results based on using FRF data directly.

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Section: Introductionmentioning

confidence: 99%

Structural damage detection using principal component analysis of frequency response function data

Esfandiari

Nabiyan

Rofooei

2020

Struct Control Health Monit

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“…With the orthogonal decomposition technique, PCA is proper to establish the baseline statistical model (pattern) using the measured data of structures under the healthy condition, and then the damaged condition of structure could be detected by utilizing the pattern recognition techniques [ 27 , 28 ]. Using this idea, PCA has been applied to detect the nonlinearity effects of structural integrity of an offshore structure [ 29 ], diagnose the abnormal condition of composite structures [ 30 ], and monitor the stress conditions of cylindrical specimens using PZTs [ 31 ]. Additionally, PCA only needs the simple calculation process, so it also has been adopted for detecting the damage of structures under the environmental temperature changes.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection of Bridges under Environmental Temperature Changes Using a Hybrid Method

Wang¹,

Gao

Liu

2020

Sensors

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Principal component analysis (PCA)-based method is popular for detecting the damage of bridges under varying environmental temperatures. However, this method deletes some information when the damage features are projected in the direction of nonprincipal components; thus, the effectiveness of PCA-based methods will decrease if the deleted information is related to bridge damage. To address this issue, a hybrid method is proposed to detect the damage of bridges under environmental temperature changes. On one side, the PCA-based method is applied to deal with the nonprincipal components; on the other side, the Gaussian mixture method (GMM) is used to classify all the principal components into different clusters, and then the novel detection method is implemented to detect bridge damage for each cluster. In this way, all the damage feature information is saved and used to detect bridge damage. The numerical example and example of an actual bridge show that the proposed hybrid method is effective in detecting bridge damage under environmental temperature changes. The GMM is effective for classifying the natural monitoring frequency data of actual bridges, and the relationship between the natural frequencies of actual bridges and the environmental temperature is not always linear.

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“…6,7 During the vibration testing, various parameters are collected by sensors, and then the data are processed with an intelligent algorithm, which could determine the status of offshore platform and give the appropriate measures. [8][9][10] The information of sensor is generally transmitted by wires. However, the wire acquisition system for offshore platforms utilizes numerous wires and makes the cabling very difficult.…”

Section: Introductionmentioning

confidence: 99%

Design and validation of a fast wireless low-frequency vibration inspection system for offshore platform structures

Zhou

Wang

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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Structural health monitoring has become increasingly important in the effective evaluation of structural health conditions and the maintenance service of structures. The validity and convenience of obtaining sensor data are critical for data mining, feature extraction, and condition assessment. A fast wireless low-frequency vibration inspection system (FWLVIS) based on wireless sensor networks for offshore platform structural vibration inspection is presented in this paper. The designed system consists of intelligent acquisition equipment and eight wireless nodes with low-frequency acceleration sensors, while the entire system has 64 collection channels. The wireless nodes integrated with a vibration sensing unit, an embedded low-power micro-processing unit, a wireless transceiver unit, and a large-capacity power unit perform functions, which could perform data collection, initial analysis, data storage, and wireless transmission. The intelligent acquisition equipment integrated with a high-performance computation unit, a wireless transceiver unit, a mobile power source, and the embedded data analysis software could completely control the multi-wireless nodes, receive and analyze the data, and implement the parameter identification. Experiments are performed on a single pendulum, and then on an offshore platform model is constructed to verify the FWLVIS. The experimental results show that the system has the following characteristics: fast arrangement, high sampling rate, high resolution, and capacity for low-frequency inspection. Thus, the system has good application prospects and practical value in the field of structural health monitoring and non-destructive inspection.

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Detection of nonlinearity effects in structural integrity monitoring methods for offshore jacket-type structures based on principal component analysis

Cited by 22 publications

References 19 publications

Structural damage detection using principal component analysis of frequency response function data

Structural damage detection using principal component analysis of frequency response function data

Damage Detection of Bridges under Environmental Temperature Changes Using a Hybrid Method

Design and validation of a fast wireless low-frequency vibration inspection system for offshore platform structures

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