基于故障预测的武器装备预防性维修策略研究
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摘要
由于现代武器装备系统日趋复杂，使其表现出的故障规律也极其复杂，对装备的保障的要求也越来越高，因此，进行装备的预防性维修已经成为了部队保障战斗力的一个重要课题，制定精确合理的维修计划，掌握维修的主动权,对充分发挥装备的使用效能,提高战备完好率等方面具有重大意义。
本文在总结了国内外武器装备预防性维修研究与发展现状的基础上，结合我军装备预防性维修和管理的实际情况，主要从以下几个方面做了研究。
（1）武器装备预防性维修的故障率预测研究
准确的故障率预测是进行预防性维修的一个重要前提，目前比较成熟的故障率预测方法是基于可靠性为基础的估计方法，这种方法主要是根据现场数据或试验数据进行统计处理，并进行假设检验，从而确定其分布类型及参数，然后在此基础上计算其可靠度，根据可靠度函数来求解故障率，但参数的求解过程确是一个比较复杂的过程，针对这种情况，提出了利用威布尔分布来拟合和估计故障率。但是在很多时候统计数据的获得却是不容易的，这样就造成了对故障率预测的困难，为了解决在“少数据、贫信息”的情况对故障率的预测，提出了利用灰色理论对故障率进行预测，给出了典型的故障率曲线下基于离散GM（1，1）模型的故障率预测的方法和非典型故障率曲线下的基于灰色线性回归组合模型的故障率预测方法。
（2）武器装备预防性维修故障间隔期的确定及研究
故障间隔期的预测是进行装备预防性维修的另一个重要课题，目前典型的预防性维修周期的确定方法有两种，一种是以可用度最大为目标确定维修周期，另一种是以平均费用最低为目标确定维修周期，这两种方法在理论上虽然可行，但是要求的条件相对苛刻，不太适用于基层的预防性维修工作的开展，所以受到一定的限制。希望能寻找一些相对简单的模型去进行预测，于是我们提出了另外两种故障间隔期预测的方法，一种是建立灰色马尔可夫组合模型进行故障间隔期的预测，该模型对兼具趋势性和波动性的非平稳随机序列具有很好的拟合效果，能很好的表达其变化规律，另一种是基于有效度原理的灰色线性回归模型的故障间隔期预测，该组合模型能够综合线性和指数等多种信息，它是处理“小样本”、“贫信息”和“不确定性”问题的有效手段。同时又考虑到这种模型的对远期预测的趋弱性，对其提出利用新陈代谢的思想进行改进，使其整个预测模型一直处于更新和发展的过程中，结果表明，改进模型在预测精度和自适应性上都显著提高，这两种方法用于故障预测具有一定的实用性，对预防性维修有一定的指导意义。
（3）武器装备预防性维修备件的管理
武器装备的备件管理是进行预防性维修的基本保障，是预防性维修活动的重要组成部分，科学合理的备件管理才能使预防性维修任务完成得既经济又能保证进度。针对这种情况，我们利用层次分析法对装备维修备件进行了分类，并且利用重要度原理对维修备件的储备定额进行了研究。在充分了解国内外常用的订货间隔期确定的方法的基础上，针对随机波动需求的订货间隔期预测的难点，提出了利用GERT随机网络模型进行随机波动需求的订货间隔期预测。另外对备件的需求量预测的问题，也提出了基于灰色马尔可夫模型的维修备件需求预测方法，该模型综合体现了灰色预测和马尔柯夫预测的优点，是一种准确、实用的方法，为非平稳随机备件需求预测提供了一种新的途径和方法。
（4）基于排队论的装备维修参数的确定及资源优化
在武器装备维修系统中，除了经典排队模型求出的几个指标以为，可能我们还关心其他指标，比如说装备全部故障的期望时间、停留在各个状态的稳态概率、各个状态的停留时间等等，为了解决这些问题，我们在排队维修系统中引入GERT随机网络模型来求解和排队维修系统有关的其他参数，这些参数的求得一方面是对维修排队系统理论的一个补充，也为武器装备的维修提供指导性的意见。最后根据武器装备维修的实际情况，对武器装备维修服务小组的设置问题进行了优化。
Due to the increasing complexity of modern weapons and equipment systems, so themalfunction rule showed extremely complex and the demand of equipment support becomeincreasingly high. Therefore, the preventive maintenance of equipment has become animportant issue in order to safeguard the combat effectiveness of the troops. Developing anaccurate and reasonable maintenance plan can help us to grasp the initiative in maintenanceand has important significance on giving full play to the efficiency of weapons and improvingoperational readiness rate.
The work is based on summarizing of the research and development in weaponryprecautionary maintenance at home and abroad. Combined with the reality of our army’sequipment preventive and management, the research has been done as the follow.
(1)Prediction research on failure rate of weapon preventive maintenance
Accurate prediction of failure rate is an important premise to preventive maintenance.Now, the estimation methods based on reliability is a relatively mature prediction method topredict failure rate. The method is based on the field data or experimental data for statisticalprocessing and carrying out hypothesis testing in order to determine the distribution type andparameters. Then, on the basis calculating the reliability and according to the reliabilityfunction to solve the failure rate. However, the solution of parameters is a complicatedprocess. Aimed at this situation, using Weibull distribution to fit and estimate the failure ratewas raised. But in many cases, it is not easy to obtain the statistical data, so it become difficultto predict the failure rate. In order to solve the difficulty of predicting failure rate in the caseof poor data and information, the Grey Theory was raised to predict the failure rate. Theprediction method based on discrete GM(1,1) model under the typical failure rate curve andthat based on combination gray linear regression model under the untypical failure rate curvewere given.
(2) Prediction research on fault interval of weapon preventive maintenance
The prediction of fault interval is another important topic for equipment preventivemaintenance. There are two ways to determine the typical preventive maintenance cycle. Oneis the availability as the goal to determine the maintenance cycle, and another based on theaverage cost of a minimum of targeting maintenance cycle. Both methods are theoreticallyfeasible, but requires relatively harsh conditions. Not applicable to primary preventivemaintenance work, so it has certain restrictive. Hoping to find some relatively simple modelto predict, we propose two fault interval prediction methods. One way is through theestablishment of between failures (MTBF) of the forecast combination of gray Markov model.The model has a good fit on both trend and volatility of the non-stationary random sequence.It is a good expression of variation between failures. Another prediction method is based onthe principle of effective degrees of gray linear regression model. This combined model canbe integrated linear and index information. It is dealing with an effective means of "sample","poor" and "uncertainty". Taking into account the long-term prediction of this model weaker, we improve this model through the idea of the metabolism, This makes the entire predictionmodel has been in the process of updating and development. The two methods for faultprediction have certain practicability, and have certain directive significance to the preventiverepair.
(3)Preventive repair spare parts management of weapons and equipment
Spare parts management of weapon equipment is the basic guarantee of preventive repairand an important part of preventive repair activities. Scientific and reasonable spare partsmanagement can make the preventive repair task completion economical and timely. Aimed atthis situation, we had equipment maintenance spare parts are classified by the analytichierarchy process (AHP), and studied the maintenance spare part reserve quota by importanceprinciple. In order to fully understand the basis of interval commonly used at home andabroad, aiming at the difficulties of forecasting random fluctuations in demand of orderinginterval, predicted by the GERT random network model is proposed for random fluctuationsof demand order interval. In addition to forecast demand for spare parts problem, forecastingmethod of Grey Markov model is proposed based on the repair of spare parts demand. Thismodel shows the advantages of grey prediction and the prediction of Markov. It is a kind ofmethod is accurate, practical, and provides a new way and method for the non-stationarystochastic spare parts demand forecasting.
(4) Determination of parameters of equipment maintenance and resource optimization Basedon the queuing theory
In the weapon equipment repair system, we also care about the other indexes except forseveral indicators of the classic queuing model. For example, all the equipment fault expectedtime, stay in each state of the steady-state probability, each state residence time etc.In order tosolve these problems, we introduce GERT random network model in queuing repair system tosolve the other parameters related to line up maintenance system.These parameters areobtained as a supplementary system to repair the queuing theory, but also provide guidancefor weapon repair.Finally, according to the actual situation of weapon equipment repair, weoptimized the weapon equipment repair team.
本文在总结了国内外武器装备预防性维修研究与发展现状的基础上，结合我军装备预防性维修和管理的实际情况，主要从以下几个方面做了研究。
（1）武器装备预防性维修的故障率预测研究
准确的故障率预测是进行预防性维修的一个重要前提，目前比较成熟的故障率预测方法是基于可靠性为基础的估计方法，这种方法主要是根据现场数据或试验数据进行统计处理，并进行假设检验，从而确定其分布类型及参数，然后在此基础上计算其可靠度，根据可靠度函数来求解故障率，但参数的求解过程确是一个比较复杂的过程，针对这种情况，提出了利用威布尔分布来拟合和估计故障率。但是在很多时候统计数据的获得却是不容易的，这样就造成了对故障率预测的困难，为了解决在“少数据、贫信息”的情况对故障率的预测，提出了利用灰色理论对故障率进行预测，给出了典型的故障率曲线下基于离散GM（1，1）模型的故障率预测的方法和非典型故障率曲线下的基于灰色线性回归组合模型的故障率预测方法。
（2）武器装备预防性维修故障间隔期的确定及研究
故障间隔期的预测是进行装备预防性维修的另一个重要课题，目前典型的预防性维修周期的确定方法有两种，一种是以可用度最大为目标确定维修周期，另一种是以平均费用最低为目标确定维修周期，这两种方法在理论上虽然可行，但是要求的条件相对苛刻，不太适用于基层的预防性维修工作的开展，所以受到一定的限制。希望能寻找一些相对简单的模型去进行预测，于是我们提出了另外两种故障间隔期预测的方法，一种是建立灰色马尔可夫组合模型进行故障间隔期的预测，该模型对兼具趋势性和波动性的非平稳随机序列具有很好的拟合效果，能很好的表达其变化规律，另一种是基于有效度原理的灰色线性回归模型的故障间隔期预测，该组合模型能够综合线性和指数等多种信息，它是处理“小样本”、“贫信息”和“不确定性”问题的有效手段。同时又考虑到这种模型的对远期预测的趋弱性，对其提出利用新陈代谢的思想进行改进，使其整个预测模型一直处于更新和发展的过程中，结果表明，改进模型在预测精度和自适应性上都显著提高，这两种方法用于故障预测具有一定的实用性，对预防性维修有一定的指导意义。
（3）武器装备预防性维修备件的管理
武器装备的备件管理是进行预防性维修的基本保障，是预防性维修活动的重要组成部分，科学合理的备件管理才能使预防性维修任务完成得既经济又能保证进度。针对这种情况，我们利用层次分析法对装备维修备件进行了分类，并且利用重要度原理对维修备件的储备定额进行了研究。在充分了解国内外常用的订货间隔期确定的方法的基础上，针对随机波动需求的订货间隔期预测的难点，提出了利用GERT随机网络模型进行随机波动需求的订货间隔期预测。另外对备件的需求量预测的问题，也提出了基于灰色马尔可夫模型的维修备件需求预测方法，该模型综合体现了灰色预测和马尔柯夫预测的优点，是一种准确、实用的方法，为非平稳随机备件需求预测提供了一种新的途径和方法。
（4）基于排队论的装备维修参数的确定及资源优化
在武器装备维修系统中，除了经典排队模型求出的几个指标以为，可能我们还关心其他指标，比如说装备全部故障的期望时间、停留在各个状态的稳态概率、各个状态的停留时间等等，为了解决这些问题，我们在排队维修系统中引入GERT随机网络模型来求解和排队维修系统有关的其他参数，这些参数的求得一方面是对维修排队系统理论的一个补充，也为武器装备的维修提供指导性的意见。最后根据武器装备维修的实际情况，对武器装备维修服务小组的设置问题进行了优化。
Due to the increasing complexity of modern weapons and equipment systems, so themalfunction rule showed extremely complex and the demand of equipment support becomeincreasingly high. Therefore, the preventive maintenance of equipment has become animportant issue in order to safeguard the combat effectiveness of the troops. Developing anaccurate and reasonable maintenance plan can help us to grasp the initiative in maintenanceand has important significance on giving full play to the efficiency of weapons and improvingoperational readiness rate.
The work is based on summarizing of the research and development in weaponryprecautionary maintenance at home and abroad. Combined with the reality of our army’sequipment preventive and management, the research has been done as the follow.
(1)Prediction research on failure rate of weapon preventive maintenance
Accurate prediction of failure rate is an important premise to preventive maintenance.Now, the estimation methods based on reliability is a relatively mature prediction method topredict failure rate. The method is based on the field data or experimental data for statisticalprocessing and carrying out hypothesis testing in order to determine the distribution type andparameters. Then, on the basis calculating the reliability and according to the reliabilityfunction to solve the failure rate. However, the solution of parameters is a complicatedprocess. Aimed at this situation, using Weibull distribution to fit and estimate the failure ratewas raised. But in many cases, it is not easy to obtain the statistical data, so it become difficultto predict the failure rate. In order to solve the difficulty of predicting failure rate in the caseof poor data and information, the Grey Theory was raised to predict the failure rate. Theprediction method based on discrete GM(1,1) model under the typical failure rate curve andthat based on combination gray linear regression model under the untypical failure rate curvewere given.
(2) Prediction research on fault interval of weapon preventive maintenance
The prediction of fault interval is another important topic for equipment preventivemaintenance. There are two ways to determine the typical preventive maintenance cycle. Oneis the availability as the goal to determine the maintenance cycle, and another based on theaverage cost of a minimum of targeting maintenance cycle. Both methods are theoreticallyfeasible, but requires relatively harsh conditions. Not applicable to primary preventivemaintenance work, so it has certain restrictive. Hoping to find some relatively simple modelto predict, we propose two fault interval prediction methods. One way is through theestablishment of between failures (MTBF) of the forecast combination of gray Markov model.The model has a good fit on both trend and volatility of the non-stationary random sequence.It is a good expression of variation between failures. Another prediction method is based onthe principle of effective degrees of gray linear regression model. This combined model canbe integrated linear and index information. It is dealing with an effective means of "sample","poor" and "uncertainty". Taking into account the long-term prediction of this model weaker, we improve this model through the idea of the metabolism, This makes the entire predictionmodel has been in the process of updating and development. The two methods for faultprediction have certain practicability, and have certain directive significance to the preventiverepair.
(3)Preventive repair spare parts management of weapons and equipment
Spare parts management of weapon equipment is the basic guarantee of preventive repairand an important part of preventive repair activities. Scientific and reasonable spare partsmanagement can make the preventive repair task completion economical and timely. Aimed atthis situation, we had equipment maintenance spare parts are classified by the analytichierarchy process (AHP), and studied the maintenance spare part reserve quota by importanceprinciple. In order to fully understand the basis of interval commonly used at home andabroad, aiming at the difficulties of forecasting random fluctuations in demand of orderinginterval, predicted by the GERT random network model is proposed for random fluctuationsof demand order interval. In addition to forecast demand for spare parts problem, forecastingmethod of Grey Markov model is proposed based on the repair of spare parts demand. Thismodel shows the advantages of grey prediction and the prediction of Markov. It is a kind ofmethod is accurate, practical, and provides a new way and method for the non-stationarystochastic spare parts demand forecasting.
(4) Determination of parameters of equipment maintenance and resource optimization Basedon the queuing theory
In the weapon equipment repair system, we also care about the other indexes except forseveral indicators of the classic queuing model. For example, all the equipment fault expectedtime, stay in each state of the steady-state probability, each state residence time etc.In order tosolve these problems, we introduce GERT random network model in queuing repair system tosolve the other parameters related to line up maintenance system.These parameters areobtained as a supplementary system to repair the queuing theory, but also provide guidancefor weapon repair.Finally, according to the actual situation of weapon equipment repair, weoptimized the weapon equipment repair team.
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