Multi-mode fusion BP neural network model with vibration and acoustic emission signals for process pipeline crack location

Zhou, Lei; Wang, Puzhe; Zhang, Chuanjie; Qu, Xianqiang; Gao, Chengang; Xie, Yaoguo

doi:10.1016/j.oceaneng.2022.112384

Cited by 18 publications

(12 citation statements)

References 16 publications

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“…Te crack is made by cutting the pipeline. In order to ensure the consistency of the damage degree, the cracks on the main pipe and branch pipe were cut along the circumference of 1/3, as shown in Figures 6(a)-6(c) [21], and the locations were selected at the typical locations of fatigue cracks: (1) the bend of the main pipe, (2) the joint of the mass block pipe and large pipe, and (3) the connection between the vertical bend and the big pipe. For the lossless pipeline models included, a total of 4 test models were obtained, as shown in Table 1.…”

Section: Real-scale Pipe Prefabrication Crackmentioning

confidence: 99%

“…Te installation method was magnetic seat, and the contact surface should be smooth and clean during installation. Te installation position of the sensor is selected in the place where vibration is obvious, and these positions are obtained through the fnite element simulation and preliminary experiment, as shown in Figure 8 [21]. Te vibration characteristic test of the pipeline system model is simulated by force hammer knocking.…”

Section: Modal Tests and Numerical Simulations Of The Pipeline Modelmentioning

confidence: 99%

“…Each measurement model has 8 channels: 1 force hammer signal channel and 7 acceleration signal channels. Table 2 [21] shows the frst ten modal frequencies measured in the modal test of no. 1-4 pipeline systems after modal parameter identifcation.…”

Section: Modal Tests and Numerical Simulations Of The Pipeline Modelmentioning

confidence: 99%

“…ANSYS Workbench 2020 R1 was selected for fnite element software. Te material parameters are shown in Table 3 [21].…”

Section: Finite Element Numerical Simulationmentioning

confidence: 99%

See 3 more Smart Citations

Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network

Xie

Gao

Wang

et al. 2023

Shock and Vibration

Self Cite

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To study vibration fatigue damage localization of offshore oil and gas pipelines, aiming at the location error caused by uncertainty of the initial parameters in backpropagation (BP) neural network training, an improved BP neural network based on the genetic algorithm (GA) is proposed to locate pipeline damage. This approach was verified by experiments and simulations. First, a BP neural network for structural damage location was constructed, and a method to optimize the BP neural network parameters based on the GA was established. Then, a finite element model was established based on the measured data from modal tests of a physical pipeline model, and a large number of vibration neural network training samples were obtained using the finite element model. Finally, to show that the improved BP neural network based on the GA had a better damage location accuracy, the location results of the original BP and GA BP methods were compared for cases with no noise and with 5% noise. The results show that the average error of the BP neural network based on the GA was less than 3%, which was 11.6% lower than that of the original BP neural network.

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Section: Real-scale Pipe Prefabrication Crackmentioning

confidence: 99%

Section: Modal Tests and Numerical Simulations Of The Pipeline Modelmentioning

confidence: 99%

Section: Modal Tests and Numerical Simulations Of The Pipeline Modelmentioning

confidence: 99%

“…ANSYS Workbench 2020 R1 was selected for fnite element software. Te material parameters are shown in Table 3 [21].…”

Section: Finite Element Numerical Simulationmentioning

confidence: 99%

See 2 more Smart Citations

Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network

Xie

Gao

Wang

et al. 2023

Shock and Vibration

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ANN model was inspired by the group of biological neurons that was efficiently modeled and fires according to the goal of the whole neuron system. Due to that reason, ANNs have gained attention from various researchers from different backgrounds to solve various potential optimization problems [1][2][3][4][5]. The high demand of the ANN is due to the nature of ANNs that can improve the solution through specific iterations which can be run easily by computer program.…”

Section: Introductionmentioning

confidence: 99%

Random Maximum 2 Satisfiability Logic in Discrete Hopfield Neural Network Incorporating Improved Election Algorithm

et al. 2022

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Real life logical rule is not always satisfiable in nature due to the redundant variable that represents the logical formulation. Thus, the intelligence system must be optimally governed to ensure the system can behave according to non-satisfiable structure that finds practical applications particularly in knowledge discovery tasks. In this paper, we a propose non-satisfiability logical rule that combines two sub-logical rules, namely Maximum 2 Satisfiability and Random 2 Satisfiability, that play a vital role in creating explainable artificial intelligence. Interestingly, the combination will result in the negative logical outcome where the cost function of the proposed logic is always more than zero. The proposed logical rule is implemented into Discrete Hopfield Neural Network by computing the cost function associated with each variable in Random 2 Satisfiability. Since the proposed logical rule is difficult to be optimized during training phase of DHNN, Election Algorithm is implemented to find consistent interpretation that minimizes the cost function of the proposed logical rule. Election Algorithm has become the most popular optimization metaheuristic technique for resolving constraint optimization problems. The fundamental concepts of Election Algorithm are taken from socio-political phenomena which use new and efficient processes to produce the best outcome. The behavior of Random Maximum 2 Satisfiability in Discrete Hopfield Neural Network is investigated based on several performance metrics. The performance is compared between existing conventional methods with Genetic Algorithm and Election Algorithm. The results demonstrate that the proposed Random Maximum 2 Satisfiability can become the symbolic instruction in Discrete Hopfield Neural Network where Election Algorithm has performed as an effective training process of Discrete Hopfield Neural Network compared to Genetic Algorithm and Exhaustive Search.

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Experimental research on vortex-induced vibration suppression of pipeline intelligent plugging operation

Wu,

Miao,

Zhao

et al. 2024

Engineering Failure Analysis

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Multi-mode fusion BP neural network model with vibration and acoustic emission signals for process pipeline crack location

Cited by 18 publications

References 16 publications

Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network

Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network

Random Maximum 2 Satisfiability Logic in Discrete Hopfield Neural Network Incorporating Improved Election Algorithm

Experimental research on vortex-induced vibration suppression of pipeline intelligent plugging operation

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