Online Condition Monitoring of Gripper Cylinder in TBM Based on EMD Method

Li, Lin; Tao, Jianfeng; Yu, Hai; Huang, Yi

doi:10.1007/s10033-017-0187-0

Cited by 10 publications

(4 citation statements)

References 26 publications

(25 reference statements)

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“…In construction process field, 20 out of 61 observations were made regarding the construction of buildings, dams, roads, and tunnels. Within this field, the 60 out of 202 observations covered topics such as construction delays [22], crane, drilling and excavation tasks [14,18,24,39,48,70,74]; geological conditions [54], scaffolding collapse [68]; transport delays [31]; tunnelling [28,36,37,41,43,55,67]; workers and machinery location [34,40]. According to Erzaij et al [22], project suspensions are among the most persistent tasks facing the construction sector, due to the difficulty of the industry and the essential interdependence between the bases of delay risk.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

Pireddu,

Bedini,

Lombardi

et al. 2024

Preprint

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Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programmes of the National Institute for Occupational Accident Insurance (Inail). Objectives: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate to be applied to certain investigation objectives, types, and sources of data, as defined by the authors. Methods: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies, were based on the Preferred Reporting Items for Systematic Review and meta-analysis (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we use principal component analysis (PCA) and meta-analysis. Results: The search strategy based on the PRISMA eligibility criteria, provided us with 61 out of 2,234 potential articles, 202 observation, 91 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource type. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: "supervised methods, institutional dataset, and predictive and classificatory purposes" (correlation 0.97÷8.18E-01; p-value 7.67E-55÷1.28E-22) and the second, Dim2 "not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment" (corr. 0.84÷0.47; p-value 5.79E-25÷3.59E-06). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. Conclusions: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53÷0.96) compared to not-supervised methods.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

Pireddu,

Bedini,

Lombardi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…e Hilbert-Huang Transform (HHT) includes the empirical mode decomposition (EMD) and the Hilbert time spectrum [31,32]. Firstly, the signal is decomposed into several inherent mode functions (IMF) by the EMD method.…”

Section: Hilbert-huang Transform and Svmmentioning

confidence: 99%

Data‐Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader

Liu

Dai

et al. 2021

Shock and Vibration

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Incipient fault detection and identification (IFDI) of cutting arms is a crucial guarantee for the smooth operation of a roadheader. However, the shortage of fault samples restricts the application of the fault diagnosis technique, and the data analysis tools should be optimized efficiently. In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. The commonly measured current and vibration data cutting arms are used in the IFDI. The experimental results show that the support vector machines based on dynamic cuckoo outperform the other methods. Besides, the performance of the four methods under different operating conditions is compared. The fault cause of cutting arms of the roadheader is analyzed and the design improvement scheme for cutting arms is provided. This study provides a reference for improving the fault diagnosis of the roadheader.

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“…The current researches focus mainly on shield machine or shield type TBM, which make the position and posture rectification by adjusting the distribution of thrust pressure in corresponding thrust cylinders working zone. However, the mechanical structure of open type hard rock TBM is different from shield machines [9][10][11]. Gripper carrier assembly is used for deviation rectification of open type TBM rather than thrust system in shield machine.…”

Section: Introductionmentioning

confidence: 99%

Indirect Adaptive Robust Trajectory Tracking Control of Hard Rock TBM with Load Variation of Tunneling Face

Shao

Liao

Liu

et al. 2019

Chin. J. Mech. Eng.

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Posture adjustment of open-type hard rock tunnel boring machine (TBM) can be achieved by properly adjusting the hydraulic pressure of gripper cylinder and torque cylinders. However, the time-varying inhomogeneous load acting on tunneling face of TBM and complex stratum working condition can cause the trajectory deviation. In this paper, the position and posture rectification kinematics and dynamics models of TBM have been established in order to track the trajectory. Moreover, there are uncertain parameters and uncertain loads from complex working conditions in the dynamic model. An indirect adaptive robust control strategy is applied to achieve precise position and posture trajectory tracking control. Simulation results show when the position deviation only occurs in Y-axis and the current orientation is parallel with the designed axis, the deviation can be corrected by controlling the pressure of gripper cylinder and the actual trajectory meets the designed axis when TBM is pushed forward 0.14 m in X-axis. If the deviation only occurs in Z-axis, then the deviation can be corrected by controlling torque cylinders. If the position deviation occurs both in Y-axis and Z-axis at the same time, the pressure of gripper cylinder and torque cylinders should be controlled at the same time to rectify the deviation. Simulation results are shown to illustrate the effectiveness and robustness of the proposed controller. This research proposes an indirect adaptive robust controller that can track the planned tracking trajectory smoothly and rapidly. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Online Condition Monitoring of Gripper Cylinder in TBM Based on EMD Method

Cited by 10 publications

References 26 publications

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

Data‐Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader

Indirect Adaptive Robust Trajectory Tracking Control of Hard Rock TBM with Load Variation of Tunneling Face

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