Dynamic Analysis and Parameter Optimization of Pipelines with Multidimensional Vibration Isolation and Mitigation Device

Xu, Zhao‐Dong; Yang, Yang; Miao, An-Nan

doi:10.1061/(asce)ps.1949-1204.0000504

Cited by 50 publications

(15 citation statements)

References 24 publications

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“…Error indicator can be used to evaluate the performance of prediction model [55][56][57][58][59]. By counting the indicators used in the discussed models, this paper summarizes 18 indicators used to evaluate the prediction accuracy.…”

Section: Prediction Accuracymentioning

confidence: 99%

Residual Strength Assessment and Residual Life Prediction of Corroded Pipelines: A Decade Review

Huang

Zeng³

et al. 2022

Energies

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Prediction of residual strength and residual life of corrosion pipelines is the key to ensuring pipeline safety. Accurate assessment and prediction make it possible to prevent unnecessary accidents and casualties, and avoid the waste of resources caused by the large-scale replacement of pipelines. However, due to many factors affecting pipeline corrosion, it is difficult to achieve accurate predictions. This paper reviews the research on residual strength and residual life of pipelines in the past decade. Through careful reading, this paper compared several traditional evaluation methods horizontally, extracted 71 intelligent models, discussed the publishing time, the evaluation accuracy of traditional models, and the prediction accuracy of intelligent models, input variables, and output value. This paper’s main contributions and findings are as follows: (1) Comparing several traditional evaluation methods, PCORRC and DNV-RP-F101 perform well in evaluating low-strength pipelines, and DNV-RP-F101 has a better performance in evaluating medium–high strength pipelines. (2) In intelligent models, the most frequently used error indicators are mean square error, goodness of fit, mean absolute percentage error, root mean square error, and mean absolute error. Among them, mean absolute percentage error was in the range of 0.0123–0.1499. Goodness of fit was in the range of 0.619–0.999. (3) The size of the data set of different models and the data division ratio was counted. The proportion of the test data set was between 0.015 and 0.4. (4) The input variables and output value of predictions were summarized.

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Section: Prediction Accuracymentioning

confidence: 99%

Residual Strength Assessment and Residual Life Prediction of Corroded Pipelines: A Decade Review

Huang

Zeng³

et al. 2022

Energies

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“…In addition, YOLO [ 30 ] and SSD [ 31 ] considered the speed and accuracy of recognition. Compared with traditional methods, methods based on deep learning can autonomously learn the characteristics of data [ 32 , 33 , 34 , 35 , 36 ]. In the field of bolt loosening detection, Zhuang et al [ 37 ] combined the time reversal method with deep learning methods to classify the ultrasonic signals in the bolted connections of wood structures, thus realizing the prediction of residual preload on bolted connections.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Detection of Bolt Loosening Using YOLOv5

Sun

Dong

et al. 2022

Sensors

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Bolted connections have been widely applied in engineering structures, loosening will happen when bolted connections are subjected to continuous cyclic load, and a significant rotation between the nut and the bolt can be observed. Combining deep learning with machine vision, a bolt loosening detection method based on the fifth version of You Only Look Once (YOLOv5) is proposed, and the rotation of the nut is identified to detect the bolt loosening. Two different circular markers are added to the bolt and the nut separately, and then YOLOv5 is used to identify the circular markers, and the rotation angle of the nut against the bolt is calculated according to the center coordinate of each predicted box. A bolted connection structure is adopted to illustrate the effectiveness of the method. First, 200 images containing bolts and circular markers are collected to make the dataset, which is divided into a training set, verification set and test set. Second, YOLOv5 is used to train the model; the precision rate and recall rate are respectively 99.8% and 100%. Finally, the robustness of the proposed method in different shooting environments is verified by changing the shooting distance, shooting angle and light condition. When using this method to detect the bolt loosening angle, the minimum identifiable angle is 1°, and the maximum detection error is 5.91% when the camera is tilted 45°. The experimental results show that the proposed method can detect the loosening angle of the bolted connection with high accuracy; especially, the tiny angle of bolt loosening can be identified. Even under some difficult shooting conditions, the method still works. The early stage of bolt loosening can be detected by measuring the rotation angle of the nut against the bolt.

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“…Safety is essential for the normal operation of pipelines [1][2][3][4][5]. Among safety issues, wax deposition is a problem that cannot be ignored.…”

Section: Introductionmentioning

confidence: 99%

Safety Study on Wax Deposition in Crude Oil Pipeline

et al. 2021

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The Shunbei crude oil pipeline is prepared to use the unheated transportation process to transport waxy crudes. However, the wax formation in the pipeline is unknown. In order to predict the wax deposition of the pipeline, the physical property experiment of Shunbei crude oil was carried out through field sampling. The density, freezing point, hydrocarbon composition, and viscosity–temperature characteristics of crude oil are obtained. The cloud point and wax precipitation characteristics of the crude oil were obtained using the differential scanning calorimetry (DSC) thermal analysis method. Then, the wax deposition rate of the pipeline was predicted by two methods: OLGA software and wax deposition kinetic model. Finally, the optimal pigging cycle of the pipeline was calculated on this basis. The results show that: Shunbei crude oil is a light crude oil with low wax content, a low freezing point, and a high cloud point. Comparing the OLGA simulation results with the calculation results of the Huang Qiyu model, the development trend of wax deposition along the pipeline was the same under different working conditions. The relative error of the maximum wax layer thickness was 6%, proving that it is feasible for OLGA to simulate wax deposition in long-distance crude oil pipelines. Affected by the wax precipitation characteristics of Shunbei crude oil, there was a peak of wax precipitation between the pipeline section where crude oil temperature was 9.31–13.31 °C and the recommended pigging cycle at the lowest throughput was 34 days in winter and 51 days in spring and autumn.

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Dynamic Analysis and Parameter Optimization of Pipelines with Multidimensional Vibration Isolation and Mitigation Device

Cited by 50 publications

References 24 publications

Residual Strength Assessment and Residual Life Prediction of Corroded Pipelines: A Decade Review

Residual Strength Assessment and Residual Life Prediction of Corroded Pipelines: A Decade Review

Vision-Based Detection of Bolt Loosening Using YOLOv5

Safety Study on Wax Deposition in Crude Oil Pipeline

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