矿井提升系统关键设备危险源辨识、评价及监控研究
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摘要
矿井提升系统担负着提升煤炭、矸石,下放材料,升降人员和设备的任务,其工作状态直接关系到整个矿井生产和工作人员的安全,因此,为了避免矿井提升系统事故的发生,有必要对提升系统的危险源进行辨识,并进行安全评价,对于影响度比较大的因素进行重点监控。因此,本文从建立提升系统关键设备的故障树入手,首先通过提升机制动系统、滑动事故和断绳等事故的故障树,简单辨识出了引起提升系统运行事故的危险源。
引起提升系统运行事故的各危险源就成为对提升系统进行安全评价的重要指标,首先应用Delphi法对故障树分析出的危险源即安全评价指标进行了量化,针对这些量化指标,运用危险源辨识及安全评价理论与方法中的模糊故障树分析评价法,对矿井提升制动系统和滑动事故进行了安全评价,进而评价出各指标在引起提升机重大事故中的重要度。
由于各危险因素引起提升系统的危险等级是非线性的,因此,本文采用了神经网络的方法对提升机运行进行了安全评价。在总结了制动失效、断绳事故和滑动事故的底事件基础上,将其作为神经网络的输入,以提升系统的安全等级作为输出,对提升系统进行了安全评价,通过反推权值计算出了各危险源对提升系统事故的重要程度。由于神经网络算法易于陷入局部最小点,因此应用了遗传算法进行优化。
根据对危险源的分析,首先设计了提升设备的监控执行机构,并对防滑装置制动系统进行了仿真。对提升监控设备进行了硬件的选型和设计,同时设计了监控系统上位机和下位机的软件。矿井提升危险源监控装置在矿井中得到了实际应用,对提升安全运行起到了重要作用,取得了较好的社会经济效益。
Mine hoist system shoulders important responsibilities that include coal and gangue hoisting, material drawing, staffs and equipments transport. Its work status is directly related to the coal mine production and safety of workers. Therefore, in order to avoid hoisting system accidents, it is necessary to build such system which includes hazard identification and safety evaluation to monitor key factors. Thence, this paper establishes system of the fault tree of key equipments, first of all, with the fault tree of hoisting brake system, the sliding accident and rope breaking and so on. It clearly identifies the hazard of the hoisting system.
The hazard which causes the accident of hoisting system becomes the important indexes for safety assessment. The first, the method of Delphi is used to quantize the safety evaluation index, the hazard analyzed by the fault tree. And then, aiming at these indicators, by the evaluation method of fuzzy fault tree analysis in the hazard identification and safety evaluation theory and method, the safety evaluation for the hoisting brake system and sliding accident is carried out, and then the index weight is determined between the indexes of major accident to the hoist.
Because relation of system dangerous levels and risk factors is nonlinear, this paper adopts the method of neural network to the safety evaluation of hoisting system. Based on brake failure, the rope breaking and sliding accident events, we used them as the inputs of neural network and took the system security levels as outputs, and then we evaluated the safety of hoisting system, and calculated the value degree of the hazard of hoisting system accidents through the anti-values. Because the neural network algorithm is easy to fall into local minima point, the genetic algorithm is used for the optimization.
According to the hazard analysis, firstly, monitor enforcement agency is designed and anti-skid braking system device is simulated. And then monitoring devices of hardware are selected and designed, at the same time, software is designed for lower computer and upper one. Hazard of mine hoisting monitoring devices are applied actually, it has played an important role on safety operation and achieved good social and economic benefits.
引起提升系统运行事故的各危险源就成为对提升系统进行安全评价的重要指标,首先应用Delphi法对故障树分析出的危险源即安全评价指标进行了量化,针对这些量化指标,运用危险源辨识及安全评价理论与方法中的模糊故障树分析评价法,对矿井提升制动系统和滑动事故进行了安全评价,进而评价出各指标在引起提升机重大事故中的重要度。
由于各危险因素引起提升系统的危险等级是非线性的,因此,本文采用了神经网络的方法对提升机运行进行了安全评价。在总结了制动失效、断绳事故和滑动事故的底事件基础上,将其作为神经网络的输入,以提升系统的安全等级作为输出,对提升系统进行了安全评价,通过反推权值计算出了各危险源对提升系统事故的重要程度。由于神经网络算法易于陷入局部最小点,因此应用了遗传算法进行优化。
根据对危险源的分析,首先设计了提升设备的监控执行机构,并对防滑装置制动系统进行了仿真。对提升监控设备进行了硬件的选型和设计,同时设计了监控系统上位机和下位机的软件。矿井提升危险源监控装置在矿井中得到了实际应用,对提升安全运行起到了重要作用,取得了较好的社会经济效益。
Mine hoist system shoulders important responsibilities that include coal and gangue hoisting, material drawing, staffs and equipments transport. Its work status is directly related to the coal mine production and safety of workers. Therefore, in order to avoid hoisting system accidents, it is necessary to build such system which includes hazard identification and safety evaluation to monitor key factors. Thence, this paper establishes system of the fault tree of key equipments, first of all, with the fault tree of hoisting brake system, the sliding accident and rope breaking and so on. It clearly identifies the hazard of the hoisting system.
The hazard which causes the accident of hoisting system becomes the important indexes for safety assessment. The first, the method of Delphi is used to quantize the safety evaluation index, the hazard analyzed by the fault tree. And then, aiming at these indicators, by the evaluation method of fuzzy fault tree analysis in the hazard identification and safety evaluation theory and method, the safety evaluation for the hoisting brake system and sliding accident is carried out, and then the index weight is determined between the indexes of major accident to the hoist.
Because relation of system dangerous levels and risk factors is nonlinear, this paper adopts the method of neural network to the safety evaluation of hoisting system. Based on brake failure, the rope breaking and sliding accident events, we used them as the inputs of neural network and took the system security levels as outputs, and then we evaluated the safety of hoisting system, and calculated the value degree of the hazard of hoisting system accidents through the anti-values. Because the neural network algorithm is easy to fall into local minima point, the genetic algorithm is used for the optimization.
According to the hazard analysis, firstly, monitor enforcement agency is designed and anti-skid braking system device is simulated. And then monitoring devices of hardware are selected and designed, at the same time, software is designed for lower computer and upper one. Hazard of mine hoisting monitoring devices are applied actually, it has played an important role on safety operation and achieved good social and economic benefits.
引文
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