A distance coefficient-multi objective information fusion algorithm for optimal sensor placement in structural health monitoring

Cao, Xiangyu; Chen, Jianyun; Xu, Qiang; Li, Jing

doi:10.1177/1369433220964375

Cited by 12 publications

(4 citation statements)

References 29 publications

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“…Te concrete damaged plasticity (CDP) model proposed by Lee and Fenves [53] is adopted for the dam body. Te sensor layout scheme is designed using the normal cloud mutation-shufed frog leaping algorithm (NCM-SFLA) proposed by Cao et al [54]. A sensor system consisting of 32 single-axis acceleration sensors (the test direction is along the river) is also integrated, as shown in Figure 5.…”

Section: Design Of Source and Target Domainsmentioning

confidence: 99%

Unsupervised Domain Adaptation Damage Identification Approach of High Arch Dams after Earthquakes

Cao

Liang

Jian-yun

et al. 2023

Structural Control and Health Monitoring

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In actual concrete arch dam engineering scenarios, the dynamic data obtained by the health monitoring system of an arch dam are incomplete. The data acquired typically depend on the state of the dam structure, that is, whether it is intact or incomplete. Besides, the future environmental loads of the structure are unpredictable. Thus, environmental noise is also uncertain. In practical engineering, the use of a damage identification model constructed based on incomplete information is problematic in scenarios with variable loads. Consequently, detecting the water level in actual arch dam projects after an earthquake and determining the impact of environmental uncertainty are necessary. Accordingly, this paper proposes a denoising contractive sparse deep autoencoder (DCS-DAE) model based on domain adaptation. The core idea of the proposed method is to constrain the data probability distribution of feature spaces in the source and target domains using maximum mean discrepancy. This fusion enables the DCS-DAE model to be capable of feature extraction. Moreover, it resolves the problem in which the objective function cannot be applied to other similar scenarios because of the lack of consistency constraints of feature spaces in the source and target domains. Four working conditions are designed to reproduce the uncertainty of structural modeling and the variability of water levels. The conditions are based on the postseismic water level detection requisites of dams in practical engineering. The results show that the proposed anomaly detection model enhances the generalization performance of the DCS-DAE in terms of feature design. Hence, the constructed model can “infer other things from one fact.” The results of this study are meaningful for the real-time cross-domain monitoring of structures under variable load conditions, providing a driving force to apply similar methods to practical arch dam projects.

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Section: Design Of Source and Target Domainsmentioning

confidence: 99%

Unsupervised Domain Adaptation Damage Identification Approach of High Arch Dams after Earthquakes

Cao

Liang

Jian-yun

et al. 2023

Structural Control and Health Monitoring

Self Cite

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“…where Q is an orthonormal matrix and Φ is the mode shape matrix. This metric has a few drawbacks: i) it might choose sensor locations with less energy content, just like KE, what can leave sensing incomplete and bring vulnerability in noisy conditions (Cao et al [2020]), ii) it presented poor results under conditions of uncertainty (Kammer [1992]).…”

Section: Modal Metrics In Structural Health Monitoringmentioning

confidence: 99%

“…The first using FIM and MSI at the same time, finding excellent results, iv) Cao et al [2020]: proposed a method named distance coefficient-multi-objective information fusion algorithm (D-MOIF) and two objective functions based on other metrics. The author was the first to study SPO in a more complex structure, v) Zhou et al [2021]: showed that the Multi-objective FA obtained better results than the NSGA-II, and vi) Colombo et al [2022]: proposed a statistical methodology that evaluated the positioning of sensors together with the damage identification.…”

Section: Introductionmentioning

confidence: 99%

Sensor Placement Optimization of a Helicopter Main Rotor Blade

Pereira¹,

Francisco²,

Jorge³

et al. 2022

Uncertainty Modeling: Fundamental Concepts and Models

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This chapter describes the importance of one of the major areas within Structural HealthMonitoring and Health and Usage Monitoring Systems: Sensor Placement Optimization (SPO). In this sense, the most important SPO metrics are presented and discussed with the reader. The methodological procedure of the optimization problem statement is also considered and discussed. he significance of rotary-wing aircraft, damages that can occur in the blade, works related to damage identification, modern methods and algorithms that have been used in this context, and a comprehensive SPO study for a helicopter main rotor blade will be presented. Finally, a new technique that considers the number of sensors as an objective associated with some of the main metrics used in SPO. The technique uses the Multiobjective Lichtenberg Algorithm and Feature Selection, an important area used in Data Mining.

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“…With the development of intelligent technology, the methods of intelligent optimization algorithm, multidimensional sensor parameter fusion algorithm and multi-optimization algorithm cross fusion have been used for sensor arrangement optimization. For example, the Euclidean distance derived from analytic geometry by Cao et al [33] based on the principle of information entropy, constructed an optimized objective function and proposed a distance coefficient-multi-objective information fusion algorithm. Niu et al [34] combined QR decomposition method and genetic algorithm to realize the optimal arrangement of hydraulic structure health monitoring sensors.…”

Section: Introductionmentioning

confidence: 99%

Sensor Layout of Hoisting Machinery Vibration Monitoring Based on Harmony Genetic Search Algorithm

Liu¹,

Ding²,

Jin³

et al. 2022

Structural Durability &Amp; Health Monitoring

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With the construction of automated docks, health monitoring technology as a parallel safety assurance technology for unmanned hoisting machinery has become a hot spot in the development of the industry. Hoisting machinery has a huge structure and numerous welded joints. The complexity and nonlinearity of the welded structure itself makes the structural failure parts random and difficult to arrange for monitoring sensors. In order to solve the problem of effectiveness and stability of the sensor arrangement method for monitoring the structure of hoisting machinery. Using the global and local search capabilities enhanced by the complementary search mechanism, a structural vibration monitoring sensor placement algorithm based on the harmony genetic algorithm is proposed. Firstly, the model is established for modal analysis to obtain the displacement matrix of each mode. Secondly, the optimal parameter combination is established through parameter comparison, and the random search mechanism is used to quickly search in the modal matrix to obtain the preliminary solution, and then the preliminary solution is genetically summed The mutation operation obtains the optimized solution, and the optimal solution is retained through repeated iterations to realize the decision of the vibration sensor layout of the crane structure monitoring. Combining the comparison test of harmony genetic algorithm, harmony search algorithm and genetic algorithm, the fitness of harmony genetic algorithm in X, Y and Z directions were 0.0045, 0.0084 and 0.0058, respectively, which were all optimal. And the average probability of deviating from the optimal path is 1.10%, 19.34%, and 54.43%, which are also optimal. Harmony genetic algorithm has the advantages of simplicity, fastness and strong global search ability, and can obtain better fitness value and better search stability.

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A distance coefficient-multi objective information fusion algorithm for optimal sensor placement in structural health monitoring

Cited by 12 publications

References 29 publications

Unsupervised Domain Adaptation Damage Identification Approach of High Arch Dams after Earthquakes

Unsupervised Domain Adaptation Damage Identification Approach of High Arch Dams after Earthquakes

Sensor Placement Optimization of a Helicopter Main Rotor Blade

Sensor Layout of Hoisting Machinery Vibration Monitoring Based on Harmony Genetic Search Algorithm

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