Submarine pipeline leakage accident risk assessment method based on fuzzy Bayesian network

A technology of Bayesian networks and submarine pipelines, applied in the field of risk assessment of submarine pipeline leakage accidents, can solve problems such as risk assessment of bottom pipeline leakage accidents, risk assessment of submarine pipeline leakage accidents, etc.

Inactive Publication Date: 2017-06-13
HARBIN UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the problem that the existing Reason model risk accident assessment method cannot carry out the risk assessment of the bottom pipeline leakage accident according to the uncertain factors of the ocean climate, and proposes a submarine pipeline based on fuzzy Bayesian network Risk Assessment Method for Spill Accident

Method used

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specific Embodiment approach 1

[0019] According to the fuzzy Bayesian network-based risk assessment method for submarine pipeline leakage accidents in this embodiment, the submarine pipeline leakage accident risk assessment method is implemented through the following steps:

[0020] Step 1. Establish a Bayesian network model according to the characteristics of the Reason model and the leakage accident data of the submarine pipeline, establish an expert system, and determine the method for determining the weight of experts;

[0021] Step 2, using triangular fuzzy numbers to quantify the expert weight determination method of the fuzzy language expression determined in step 1, and determine the logical relationship between events;

[0022] Step 3, defuzzifying the fuzzy number into a probability value;

[0023] Step 4: Define the logical relationship between events in GeNIe2.0 software, analyze the Bayesian network model, and obtain the probability of accidents of different degrees, so as to determine the risk...

specific Embodiment approach 2

[0025] The difference from the specific embodiment one is that in the fuzzy Bayesian network-based submarine pipeline leakage accident risk assessment method of this embodiment, the Bayesian network model is established according to the characteristics of the Reason model and the submarine pipeline leakage accident data described in step one. : , where: is the prior probability, is the posterior probability, is the likelihood ratio, A represents a n status a 1 , a 2 ,..., a n multi-state variables;

[0026] Then according to the full state formula: , when BN has multiple nodes, it can be expressed as: , where: X represents a node;

[0027] Joint distribution according to the chain method , where: for node parent collection. BN stands for Bayesian Network model.

specific Embodiment approach 3

[0029] The difference from the first or second specific implementation is that, in the fuzzy Bayesian network-based submarine pipeline leakage accident risk assessment method of this implementation, the process of establishing an expert system as described in step 1 and determining the weight of experts is specifically as follows: The tone values ​​are very high, high, high, medium, low, and very low, which are described as triangular fuzzy numbers in one-to-one correspondence: (0.9,1.0,1.0), (0.7,0.9,1.0), (0.5,0.7, 0.9), (0.3,0.5,0.7), (0.1,0.3,0.5), (0,0.1,0.3), (0,0,0.1).

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Abstract

Risk assessment method of submarine pipeline leakage accident based on fuzzy Bayesian network. The existing Reason model risk accident assessment method cannot carry out the risk assessment of bottom pipeline leakage accidents based on many uncertain factors of marine climate. The present invention is realized through the following steps: establish a Bayesian network model according to the characteristics of the Reason model and the leakage accident data of the submarine pipeline, establish an expert system, and determine the method for determining the expert weight; use triangular fuzzy numbers to determine the expert weight expressed in the fuzzy language determined in step 1 Quantify the method to determine the logical relationship between events; convert the fuzzy number into a probability value; define the logical relationship between events in GeNIe2.0 software, analyze the Bayesian network model, and obtain different degrees of accidents probability, so as to determine the risk level of accidents with different leakage degrees. The invention can more accurately evaluate the occurrence probability and level of the leakage accident risk of the submarine pipeline.

Description

[0001] Technical field: [0002] The invention relates to a method for assessing the risk of a submarine pipeline leakage accident based on a fuzzy Bayesian network. [0003] Background technique: [0004] With the rapid development of the offshore oil and gas industry, the risk of leakage accidents of submarine pipelines has gradually increased. Submarine pipelines have been exposed to the harsh marine environment for a long time, bearing complex working loads, environmental loads and unexpected risk loads, and have a high probability of failure. Marine environment, causing ecological disaster. With the increase of my country's demand for oil and gas, the offshore oil production industry has developed rapidly, and floating offshore oil production equipment has also become popular. According to the survey, the failure of offshore equipment has a huge economic impact and serious environmental pollution. This phenomenon makes people continuously analyze the causes of offshore ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q50/00
CPCG06Q50/00
Inventor 马德仲刘凯辛
Owner HARBIN UNIV OF SCI & TECH
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