基于贝叶斯网络的桥式起重机故障诊断系统开发
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摘要
贝叶斯网络模型合理性和先验概率准确性直接影响贝叶斯网络推理结果是否可靠。在对桥式起重机故障分解的基础上,加入故障表现节点,改进故障诊断模型,引入专家评价的先验概率,提高先验概率的量化赋值准确度。在此研究基础上开发一种基于贝叶斯网络的大型桥式起重机故障诊断软件用于进行故障诊断和故障预测等。研究结果表明,该故障诊断模型能够有效地提高故障诊断的准确度,诊断效率平均提高30%以上。
The rationalities and precision of prior probabilities of Bayesian network model directly influence whether the result of Bayesian network is correct or not. On the basis of the fault decomposition of bridge crane,we added the fault expression node to optimize the fault diagnosis model and introduced the prior probability of expert evaluation to improve the quantization precision of prior probability. Based on this research,bridge crane fault diagnosis software based on Bayesian network was developed to diagnose and predict the fault. The research results show that the fault diagnosis model can effectively improve the accuracy of fault diagnosis,and the average diagnosis efficiency of the fault diagnosis system is increased by more than 30%.
The rationalities and precision of prior probabilities of Bayesian network model directly influence whether the result of Bayesian network is correct or not. On the basis of the fault decomposition of bridge crane,we added the fault expression node to optimize the fault diagnosis model and introduced the prior probability of expert evaluation to improve the quantization precision of prior probability. Based on this research,bridge crane fault diagnosis software based on Bayesian network was developed to diagnose and predict the fault. The research results show that the fault diagnosis model can effectively improve the accuracy of fault diagnosis,and the average diagnosis efficiency of the fault diagnosis system is increased by more than 30%.
引文
[1]郭日红,董忠文,谢国锋.基于贝叶斯网络的机械设备故障诊断方法研究[J].机械设计与制造工程,2016(10):87-91.
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[2]段荣行,董德存,赵时星.采用动态故障树分析诊断系统故障的信息融合法[J].同济大学学报(自然科学版),2012,39(11):1699-1704.
[3]张建功.贝叶斯网络在起重机故障诊断中的应用研究[J].科技创新与生产力,2016(2):98-99.
[4]王国宏.先验概率和似然函数未知时的分布式检测融合[J].电光与控制,2004,11(1):10-14.
[5]孟晓风,季宏,王国华,等.计算故障先验概率的最大熵方法[J].北京航空航天大学学报,2006,32(11):1320-1323.
[6]陆静,王捷.基于超级贝叶斯方法的专家意见先验概率修正研究[J].统计与决策,2013(1):15-18.
[7]沐守宽,周伟.缺失数据处理的期望-极大化算法与马尔可夫蒙特卡洛方法[J].心理科学进展,2011,19(7):1083-1090.
[8]郭石开,李菲.基于分批估计算术平均的数据融合算法在循环流化床锅炉床温测量中的应用研究[J].陕西电力,2016,44(7):78-80.
[9]马春龙,张启英.基于高斯函数及分批估计融合理论的无线网络定位算法[J].长春工业大学学报,2012,33(1):59-63.
[10]廖惜春,丘敏,麦汉荣.基于参数估计的数据融合算法研究[J].传感器与微系统,2006,25(10):70-73.
[11]Petzold C.Programming Microsoft Windows with C#[M].America:Microsoft,2010:19-108.
[12]凌波,韩子渊,王玺.基于C#平台的桥式起重机主梁优化软件开发[J].机械工程与自动化,2016(6):98-99.