Digital twin based condition monitoring of a knuckle boom crane: An experimental study

Moi, Torbjørn; Cibicik, Andrej; Rølvåg, Terje

doi:10.1016/j.engfailanal.2020.104517

Cited by 78 publications

(29 citation statements)

References 30 publications

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“…DT has been researched in the SHM of spacecraft structures by Shangguan et al [8], small-scale knuckle boom crane by Moi et al [21], and collapse fragility assessment of long span bridges under strong earthquakes by Lin et al [22].…”

Section: Advancements In Structural Health Monitoring (Shm) Methodsmentioning

confidence: 99%

Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure

et al. 2022

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Over the life cycle of a civil infrastructure (a bridge as an example), 0.4–2% of the construction cost is spent annually on its maintenance. Utilising new technologies including the internet of things (IoT) and digital twin (DT) can significantly reduce the infrastructure maintenance costs. An infrastructure DT involves its digital replica and must include data on geometric, geospatial reference, performance, attributes (material, environment etc.) and management. Then, the acquired data need to be analysed and visualised to inform maintenance decision making. To develop this DT, the first step is the study of the infrastructure life cycle to design DT architecture. Using data semantics, this paper presents a novel DT architecture design for an intelligent infrastructure maintenance system. Semantic modelling is used as a powerful tool to structure and organize data. This approach provides an industry context through capturing knowledge about infrastructures in the structure of semantic model graph. Using new technologies, DT approach derives and presents meaningful data on infrastructure real-time performance and maintenance requirements, and in a more expressible and interpretable manner. The data semantic model will guide when and what data to collect for feeding into the infrastructure DT. The proposed DT concept was applied on one of the conveyors of Dalrymple Bay Coal Terminal in Queensland Australia to monitor the structural performance in real-time, which enables predictive maintenance to avoid breakdowns and disruptions in operation and consequential financial impacts.

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Section: Advancements In Structural Health Monitoring (Shm) Methodsmentioning

confidence: 99%

Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure

et al. 2022

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“…A comparison between traditional and DT-driven assembly is also presented. The authors in [177] created a DT model of a small-scale knuckle boom crane for condition monitoring. Nonlinear Finite Element (FE) analysis was performed with input as payload weight.…”

Section: Figure 10 Dt Reference Modelmentioning

confidence: 99%

Digital Twin Perspective of Fourth Industrial and Healthcare Revolution

2022

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Digital Twin (DT) is bringing revolution to our lives by a digital representation of the physical system. DT is the creation of the joint usage of various technologies like Cyber-Physical System (CPS), Internet of Things (IoT), Big Data, Edge Computing (EC), Artificial Intelligence (AI), and Machine Learning (ML), etc. DTs are established to optimize a wide range of applications of industry, healthcare, smart cities, smart homes, etc. It is still in its early development stages. This paper fills the gaps by combining the extensive information on technologies utilized in the creation of DT in industry and healthcare. The paper focuses on studying the characteristics of DT, communication technologies and tools utilized in the creation of DT models, reference models, standards, and the researcher's recent work in smart manufacturing and healthcare. Challenges and open issues that need attention are also discussed.

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“…(5) In method 5 (MW-KPCA), time series data with close time interval have high similarity and correlation, and the MW strategy based on local model can highlight the similarity between normal data and the difference between abnormal data, thus improving the sensitivity of the monitoring model. (6) In method 6 (MW-KECA), the monitoring strategy based on MW-KECA method can well reflect the dynamic change characteristics of the metallic structure and contain good anti-disturbance ability and stability, which can accurately monitor the working condition of crane. However, this method needs to be further optimized due to the confusion of monitoring different working conditions caused by insufficient clustering.…”

Section: Case 1: Experimental Data From Laboratorymentioning

confidence: 99%

“…balance beam, A-frame, tie-rod, fly-jib and cargo boom) are prone to localized damage, which causes downtime of the entire large hoisting machinery system. [5][6][7][8] Hence, to reduce the maintenance costs and extend the remaining useful life (RUL) and ensure the safety and stability of a hoisting machinery system, more and more literatures and investigations are mainly focused on the condition monitoring of hoisting machinery system. [9][10][11][12][13] Therefore, it is valuable and significant to develop a novel technique with outstanding generalizability and high accuracy to conduct the condition monitoring of cranes under different working conditions.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission and moving window-improved kernel entropy component analysis for structural condition monitoring of hoisting machinery under various working conditions

Yang

2021

Structural Health Monitoring

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Acoustic emission (AE) has been widely used to the nondestructive evaluation (NDE) and structural health monitoring (SHM) of hoisting machinery recently. Kernel entropy component analysis (KECA) is generally applied to extract the AE features based on its excellent nonlinear ability. However, traditional KECA specifically requires a considerable number of components (e.g. eigenvalues and eigenvectors) to excellently describe the original data, which leads to a reduction in the effect of approximate dimensionality reduction of high-dimensional data, thus causing readily unacceptable condition monitoring result. To overcome this weakness, a novel method named moving window-improved kernel entropy component analysis (MW-IKECA) is proposed in this study for structural condition monitoring of hoisting machinery, which is aimed at extracting more AE feature information and improving the condition identification accuracy. Firstly, a twiddle factor is introduced in the KECA model for the purpose of breaking the restriction that the projection axes originate only from the feature vectors and maximizing the independence between the components. Meanwhile, the moving window local strategy is incorporated into the proposed IKECA to extract more rich and effectiveness AE feature information at different scales. Finally, the Cauchy–Schwarz (CS) statistic is utilized to calculate the similarity between probability density functions and maintain the angular structure of the MW-IKECA feature space for the task of improving the monitoring accuracy and shortening the monitoring time-delay of MW-IKECA. Results of the experimental and practical engineering application validate the effectiveness and superiority of the proposed method in AE-based crane SHM under different working conditions compared with the traditional KECA and some combinatorial methods.

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Digital twin based condition monitoring of a knuckle boom crane: An experimental study

Cited by 78 publications

References 30 publications

Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure

Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure

Digital Twin Perspective of Fourth Industrial and Healthcare Revolution

Acoustic emission and moving window-improved kernel entropy component analysis for structural condition monitoring of hoisting machinery under various working conditions

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