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A bayesian network-based safety assessment method for solid propellant granule-casting molding process
DOI:10.1016/j.jlp.2023.105089.png)
摘要
En 中文
The safety of the solid propellant molding process is vital for the stable production of high-quality propellants. Failure events caused by abnormal parameters in the molding process may have catastrophic consequences. In this paper, a Bayesian network (BN) model is proposed to assess the safety of the solid propellant granule-casting molding process. Fault tree analysis (FTA) is developed to construct a causal link between process variables and process failures. Subsequently, expert experience and fuzzy set theory (FST) are used to obtain failure probabilities of the basic events (BEs). Based on the mapping rules, FTA provides BN with reliable prior knowledge and a network structure with interpretability. Finally, when new evidence is obtained, the probability is updated with the diagnostic reasoning capability of BN. The results of the sensitivity analysis and diagnostic inference were combined to identify key parameters in the granule-casting molding process, including curing temperature, vacuum degree, extrusion, calendering roll distance, length setting value, holding time, and polish time. The results of this paper can provide effective supporting information for managers to conduct process safety analysis.
Keyword:
Solid propellant
Granule -casting molding process
Bayesian network
Fault tree analysis
Safety analysis
期刊
IF:
4.2
论文数:
4.6K
被引数:
1.1W
机构
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ACTA ASTRONAUTICA
IF3.4

