A research on fatigue crack growth monitoring based on multi-sensor and data fusion

Chang, Qi; Weixi, Yang; Liu, Jun; Gao, Heming; Ye, Meng

doi:10.1177/1475921719865727

Cited by 13 publications

(10 citation statements)

References 29 publications

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“…The Random Forest model has had previous SHM applications in predicting natural frequencies, 84 damage detection 68,85,86 and crack detection. 87,88…”

Section: Methodsmentioning

confidence: 99%

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A Feature Extraction & Selection Benchmark for Structural Health Monitoring

Buckley

Ghosh

Pakrashi

2022

Structural Health Monitoring

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There are a large number of time domain, frequency domain and time-frequency signal processing methods available for univariate feature extraction. However, there is no consensus in SHM on which feature, or feature sets, are best suited for the identification, localisation and prognosis of damage. This paper attempts to address this problem by providing a comprehensive benchmark of feature selection & reduction methods applied to an extensive set of univariate features. These univariate features are extracted using multiple statistical, temporal and spectral methods from the benchmark S101 and Z24 bridge datasets. These datasets contain labelled accelerometer recordings from full scale bridges as they are progressively subjected to multiple damage scenarios. To identify the minimal set of features that best distinguishes between the multiple damage states, a supervised machine learning approach is used in combination with multiple feature selection methods. The ability of these reduced feature sets to distinguish between damage states is benchmarked using the prediction performance of the classification models, with the training and test sets obtained through stratified k-fold cross validation. The results obtained show that reduced sets of univariate features, extracted from a single accelerometer sensor, are capable of accurately distinguishing between multiple classes of healthy and damaged states. This work provides a benchmark for SHM practitioners and researchers alike for the choice, comparison and validation of feature extraction and feature selection methods across a wide range of systems.

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“…The Random Forest model has had previous SHM applications in predicting natural frequencies, 84 damage detection 68,85,86 and crack detection. 87,88…”

Section: Methodsmentioning

confidence: 99%

“…The Random Forest model has had previous SHM applications in predicting natural frequencies, 84 damage detection 68,85,86 and crack detection. 87,88 k-Nearest Neighbour. k-nearest neighbour (kNN) is an effective nonparametric method for classification which is regularly used in SHM applications.…”

Section: Classificationmentioning

confidence: 99%

A Feature Extraction & Selection Benchmark for Structural Health Monitoring

Buckley

Ghosh

Pakrashi

2022

Structural Health Monitoring

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“…During the experiment, the main contrastive algorithms used are BP [ 28 ], RF [ 29 ], SVM [ 30 ], and CNN [ 31 ]. Both SVM and FSVM use a radial basis kernel function, and the kernel function parameter is set to 0.001.…”

Section: Methodsmentioning

confidence: 99%

A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

Liu

Gao

et al. 2021

Journal of Healthcare Engineering

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Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

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“…Currently, structural health monitoring (SHM) systems are deployed on large civil structures and typically contain various types of sensors to measure diferent types of responses of structures. Numerous studies relating to data fusion have investigated SHM in time and space [32][33][34][35][36][37][38][39][40]. Wu and Jahanshahi [41] reviewed data fusion methods for SHM and system identifcation.…”

Section: Introductionmentioning

confidence: 99%

“…Jin et al [39] adopted the traditional, multifdelity data fusion framework based on Gaussian regression to investigate the complementary data fusion of point strain sensors and distributed sensors and obtained an accurate strain distribution by combining their advantages. Qi et al [40] proposed a model-based, inverse fnite element algorithm by using two kinds of sensors to identify the crack length and carried out experimental verifcation. However, according to the investigation, there is no precedent for applying data fusion technology to SIF calculation.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter-Based Multitype Measurement Data Fusion for Stress Intensity Factor Evaluation in Marine Structures

Dai

Jia

Ren

et al. 2023

Structural Control and Health Monitoring

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Fracture is the main form of marine structural failure due to the action of waves and oceans. It is necessary to monitor the initiation and extension of cracks in a timely and accurate manner, and the stress intensity factor (SIF) is an important indicator for analysing the propagation of cracks. Based on existing research, this paper combines the calculation method of the SIF based on a single strain gauge (SSG) and maximum crack opening displacement (CMOD) and proposes a method based on the Kalman filter (KF). Numerical simulation and experimental verifications of this method have been carried out. The results show that the method can be used for a variety of configurations of precracked specimens. The KF-based method obtains more accurate results than the SSG-based and CMOD-based methods, and the relative error (RE) is not greater than 1.2%.

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A research on fatigue crack growth monitoring based on multi-sensor and data fusion

Cited by 13 publications

References 29 publications

A Feature Extraction & Selection Benchmark for Structural Health Monitoring

A Feature Extraction & Selection Benchmark for Structural Health Monitoring

A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

Kalman Filter-Based Multitype Measurement Data Fusion for Stress Intensity Factor Evaluation in Marine Structures

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