Reliability Analysis of Gasifier Lock Bucket Valve System Based on DBN Method

Ming, Liu; Ma, Jiayue; Duo, Yili; Sun, Tiezhi

doi:10.1155/2021/8840545

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…Te primary goal of this part is to simulate the time-lag correlation between two time series I(i, t) and I(j, t)(i ≠ j) using dynamic Bayesian network (DBN) to realize epidemic STTP network structure learning. Bayesian network is a probabilistic graph model, which represents a group of random variables and their conditional dependencies through a directed acyclic graph (DAG) [25]. Formally, Bayesian networks use nodes to represent random variables (whose probability is Bayesian probability).…”

Section: Epidemic Spatial-temporal Transmission Path (Sttp)mentioning

confidence: 99%

Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path

Zhao,

Peng,

Zheng

et al. 2023

Complexity

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People in the epicenter suffer from emergency medical supplies shortage in the early stage of a public health emergency because of imbalanced supply-demand in different regions or areas, which is a key issue in a major infectious disease. In response to the severe insufficiency of supplies in the epicenter, this study proposed a strategy of distributing supplies from peripheral areas to the epicenter and gave a supply-side selection model considering the epidemic influence and supplies condition in the candidate supply-side areas. First of all, the epidemic spatial-temporal transmission path (STTP) network describing the geographic spread of disease is obtained using a first-order conditional dependence approximation algorithm in a dynamic Bayesian network (DBN). Then, the structural information of the STTP network and the supplies condition characteristic information are combined using the Bipartite network embedding (BiNE) method. Finally, a graph convolutional neural network (GCN) is conducted to select the supply-side areas for peripheral-epicenter supplies distribution based on information achieved from the bipartite graph. The results show that the highest supplies allocation accuracy reaches 87%. Validation and supremacy of the proposed methodology are provided by applying it to the case in Hubei province. This study considers crossed-areas supplies distribution strategy and contributes to select suitable supply-side areas considering the epidemic and supplies condition in the peripheral areas, which is helpful to both epicenter and peripheral areas.

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Section: Epidemic Spatial-temporal Transmission Path (Sttp)mentioning

confidence: 99%

Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path

Zhao,

Peng,

Zheng

et al. 2023

Complexity

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“…The training process was divided into pre-training and fine-tuning. The pre-training trains all RBMs in an unsupervised manner, whereas fine-tuning is used to fine-tune the weight and bias parameters of the pre-trained model through the implementation of the BP algorithm [31,32]. (…”

Section: Dbn Theorymentioning

confidence: 99%

A Novel Feature Identification Method of Pipeline In-Line Inspected Bending Strain Based on Optimized Deep Belief Network Model

Liu

Wang

Li³

et al. 2022

Energies

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Both long-distance oil and gas pipelines often pass through areas with unstable geological conditions or natural disasters. As a result, they are prone to bending, displacement, and deformation due to the action of an external environmental loading, which poses a threat to the safe operation of pipelines. The in-line inspection method that is based on the implementation of high-precision inertial measurement units (IMU) has become the main means of pipeline bending stress-strain detection technique. However, to address the problems of the inconsistent identification, low identification efficiency, and high misjudgment rate during the application of the traditional manual identification methods, a feature identification approach for the in-line inspected pipeline bending strain based on the employment of an optimized deep belief network (DBN) model is proposed in this work. In addition, our model can automatically learn features from the pipeline bending strain signals and complete classification and identification. On top of that, after the network model was trained and tested by using the actual pipeline bending strain inspection data, the extracted results showed that the model after the implementation of the training process could accurately identify and classify various pipeline features, with an identification accuracy and efficiency of 97.8% and 0.02 min/km, respectively. The high efficiency, elevated accuracy, and strong robustness of our method can effectively improve the in-line inspection procedure of pipelines during the enforcement of a bending strain load.

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“…The dynamic Bayesian network (DBN) structure model can effectively represent the structural relationship between node variables in the dynamic risk assessment system of the coal chemical industry and can also calculate the exact value of risk, so its use as the main research method for the dynamic risk assessment of the coal gasification process is suitable. Based on DBN, Liu [ 22 ] conducted a dynamic risk assessment of the changes in the reliability of a gasifier burner system during the operating cycle. The dynamic reliability of the system was inferred from prior data; it was found that the dynamic reliability of the system and its subsystems gradually decreased with an increase in operating time, and the weak links of the system were successfully identified.…”

Section: Introductionmentioning

confidence: 99%

Dynamic risk assessment of a coal slurry preparation system based on the structure-variable Dynamic Bayesian Network

Liu,

Wu,

Hou

2024

PLoS ONE

Self Cite

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In order to strengthen the safety management of coal slurry preparation systems, a dynamic risk assessment method was established by using the bow-tie (BT) model and the Structure-variable Dynamic Bayesian Network (SVDBN). First, the BT model was transformed into a static Bayesian network (BN) model of the failure of a coal slurry preparation system by using the bow-tie model and the structural similarity of the Bayesian cognitive science, based on the SVDBN recursive reasoning algorithm. The risk factors of the coal slurry preparation system were deduced using the Python language in two ways, and at the same time, preventive measures were put forward according to the weak links. In order to verify the accuracy and feasibility of this method, the simulation results were compared with those obtained using GeNIe software. The reasoning results of the two methods were very similar. Without considering maintenance factors, the failure rate of the coal slurry preparation system gradually increases with increasing time. When considering maintenance factors, the reliability of the coal slurry preparation system will gradually be maintained at a certain threshold, and the maintenance factors will increase the reliability of the system. The proposed method can provide a theoretical basis for the risk assessment and safety management of coal slurry preparation systems.

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Reliability Analysis of Gasifier Lock Bucket Valve System Based on DBN Method

Cited by 4 publications

References 39 publications

Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path

Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path

A Novel Feature Identification Method of Pipeline In-Line Inspected Bending Strain Based on Optimized Deep Belief Network Model

Dynamic risk assessment of a coal slurry preparation system based on the structure-variable Dynamic Bayesian Network

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