Mining equipment reliability assessment models based on genetic algorithms and theoretical probability distributions

基于遗传算法和理论概率分布的矿山设备可靠性评估模型

• 批准号: 155573-2008
• 负责人: Vayenas, Nick
• 金额: $ 1.31万
• 依托单位: Laurentian University of Sudbury
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=522646
• 关键词: Mining; equipment; reliability; assessment; models

This proposal is a continuation of an NSERC grant which attempts to develop and test a mining equipment reliability assessment methodology based on Genetic Algorithms (GAs) with theoretical probability distributions as the fitness functions for the GAs models. GAs are stochastic search techniques based on the principles of natural selection, heredity and genetics. The reason for selecting GAs is the fact that the reliability of mining equipment changes over time due to its dependence upon several covariates/factors (e.g. the operating environment, quality of repair). These factors combine to create a complex impact on a piece of equipment's reliability function. This impact encapsulates and inherits to some degree the individual characteristics of the factors as they evolve over time. The overall idea is to investigate whether novel reliability assessment models based on GAs can produce satisfactory solutions compared to classical reliability analysis. So far, an Exponential distributions based GAs model and a Lognormal distributions based model have been developed and tested using collected data in the form of times between failures from underground mines. These two probability distributions have been found to fit historical failure data. However, the data indicated that the criteria of convergence does not suffice and it is imperative to develop models based on other probability distr. for achieving higher predictability. This proposal will first focus on the development and testing of new models and their comparison with the existing GAs models. Our previous research, on the combination of mine production simulation studies with GAs based reliability assessment models for analyzing the impact of mobile equipment failures on the productivity of mines, resulted in the development of a prototype mine simulator. The next phase in the development of this simulator will require the implementation of higher functionality to better represent real life mine case studies.

该建议是NSERC赠款的延续，该赠款试图开发和测试基于遗传算法（GAs）的采矿设备可靠性评估方法，并将理论概率分布作为GAs模型的适应度函数。遗传算法是基于自然选择、遗传和遗传学原理的随机搜索技术。选择GA的原因是，由于采矿设备的可靠性取决于多个协变量/因素（例如操作环境、维修质量），因此其可靠性会随着时间的推移而变化。这些因素联合收割机对设备的可靠性功能产生复杂的影响。这种影响在一定程度上概括和继承了这些因素随着时间的推移而演变的个别特征。 总体思路是调查是否新的可靠性评估模型的基础上，遗传算法可以产生令人满意的解决方案相比，经典的可靠性分析。到目前为止，一个指数分布为基础的遗传算法模型和对数正态分布为基础的模型已经开发和测试使用收集的数据的形式之间的时间从地下矿山的故障。这两个概率分布已被发现，以适应历史故障数据。然而，数据表明，收敛的标准是不够的，必须开发基于其他概率分布的模型。以实现更高的可预测性。本提案将首先侧重于新模型的开发和测试及其与现有一般事务助理模型的比较。我们以前的研究，结合矿山生产模拟研究与遗传算法为基础的可靠性评估模型，用于分析移动的设备故障对矿山生产率的影响，导致了原型矿山模拟器的开发。 该模拟器开发的下一阶段将需要实现更高的功能，以更好地代表真实的矿山案例研究。