【作者】 孟静华；

【导师】 何世权；

【作者基本信息】 兰州理工大学 ， 安全技术及工程， 2009， 硕士

【摘要】 安全阀是承压设备的超压保护装置,它的可靠工作对现代企业的安全运行起着至关重要的作用。在坚持“安全第一,预防为主”生产方针的前提下,建立和完善安全法规、管理体系在高危行业中就显得尤为重要。安全评价是安全管理中的关键技术,科学、系统地开展安全评价工作,对减少事故发生,减轻人员伤亡和财产损失具有重要的意义。本文主要对安全阀失效进行评价,以降低其运行风险,开展的主要工作归纳如下。在实际使用过程中安全阀的失效涉及诸多因素且易受随机性因素的影响,具有模糊性和不确定性的特点,这些特点决定了系统状态的变化并不按照某一特定的规律或函数变化,整个系统是非线性动力学的。首先,本文将安全阀失效的相关影响因素作为因素集,把可能产生故障的元件作为评价集,采用模糊综合评价方法对安全阀的失效进行综合评价。然后,尝试着将模糊综合评价的分析思路应用到BP(Back Propagation)人工神经网络(Artificial Neural Network,ANN)模型中,利用MATLAB神经网络工具箱GUI,对样本数据进行仿真,接着采用训练好的网络对安全阀的现状进行评价。在两种方法的比较中发现,模糊综合评价法在确定权重时,采用层次分析法和专家评判相结合,将定性问题转化为定量问题,可较为准确的反映因素集中各因素的权重分布情况。但由于因素之间的相互关联较为复杂,两两比较出来的权重带有一定的主观色彩,因此还需寻找另一种更为简便、科学的方法,使评价的结果更为客观、公正、合理。BP神经网络的非线性动力学特性与安全阀失效的规律相耦合,克服了传统评价方法在解决非线性动力学问题上存在的缺陷,运用BP神经网络对安全阀的现状进行评价,得到的结果与实际检测结果一致,且快速、方便;通过对样本的不断训练,BP网络使权值收敛在一个稳定的范围,避免了主观因素的影响;同时BP人工神经网络还具有自适用性、自学习性、信息处理的并行性、结构的可塑性和良好的容错性等优点,因此将BP神经网络用于安全阀失效评价具有良好的评价效果和重要的实用价值。更多还原

【Abstract】 Safety valve is the protection against overpressure device of compressive equipment, its reliable operation plays an important role for the modern enterprise. It is of great important to establish and perfect safety laws and regulations as well as management systems, so as to make sure that the production can go on safely. Persisting in "Firstly safety, Mainly prevention" under the premise of production polity. As the key technology of the safety management, it is significance to carry out safety assessment scientifically and systematically in order to reduce accidents, and casualties and property losses. Secondly, this paper focuses on the failure evaluation of safety valves, in order to reduce the risk of operation. The main work was summarized as follows.The failure of safety valves is affected by many factors, and even many random factors easily, so there are characters of fuzziness and uncertainty. That determine the changes of system state do not according to a specific rules or function, but it is nonlinear dynamics. First, the model for the fuzzy comprehensive assessment of safety valve is proposed, in this model, the factors affecting the failure of safety valve is taken as the factor set; the elements of the safety valve, which are likely to fail, are taken as the assessment set. Secondly it was tried that analytical method of fuzzy comprehensive assessment is drawn into BP neural network model(NNS), this apply to failure analysis of safety valve. Based on fuzzy comprehensive assessment, BP neural network model of failure valuation was established. Then the sample data was emulated by using neural network toolbox in the MATLAB software-GUI. Lastly safety valve was valuated by using the trained network, its results are same as the practical results.In comparison of the two method, we found that in the process of fuzzy comprehensive assessment, the weight of the element are decided according to analytic hierarchy process and expert evaluation, this method could relatively accurate the failure of safety valve. But because of the relationship between the factors are complex, there are certain subjective color in weight by using pairwise comparison. Therefore it is need to find another more simple and scientific method, this method will make the evaluation results becomes more objective, fair and reasonable.BP Artificial neural network could compose new information processing system according to imitate human nervous system, its nonlinear dynamic is adapted to the rule of failure of safety valve, this overcomes the traditional evaluation method in solving nonlinear dynamics problems existing defects; Using BP neural network to evaluate the present status of safety valves, its results are consistent as the practical results, and overcome the subjective factors by; BP network makes weights converge to a stable range by using training sample; And there are the characters of the self-adaptability, self-study, concurrency of information processing, structural plasticity, good fault tolerance and so on. So application of ANN technique in safety evaluation has feasibility and strong adaptability.更多还原