(n,N) type maintenance policy for multi-component systems with failure interactions

This paper studies maintenance policies for multi-component systems in which failure interactions and opportunistic maintenance (OM) involve. This maintenance problem can be formulated as a Markov decision process (MDP). However, since an action set and state space in MDP exponentially expand as the number of components increase, traditional approaches are computationally intractable. To deal with curse of dimensionality, we decompose such a multi-component system into mutually influential single-component systems. Each single-component system is formulated as an MDP with the objective of minimising its long-run average maintenance cost. Under some reasonable assumptions, we prove the existence of the optimal (n, N) type policy for a single-component system. An algorithm to obtain the optimal (n, N) type policy is also proposed. Based on the proposed algorithm, we develop an iterative approximation algorithm to obtain an acceptable maintenance policy for a multi-component system. Numerical examples find that failure interactions and OM pose significant effects on a maintenance policy.     

Optimal maintenance policy for multi-component systems under Markovian environment changes

In this paper, we study multi-component systems, which environmental conditions and opportunistic maintenance (OM) involve. Environmental conditions will exert an influence on deterioration processes of the components in the system. For each component, the worse the environmental conditions are, the faster its deterioration speed is. We want to determine when to preventively maintain each component under such environmental influence. Our purpose is to minimize its long-run average maintenance cost. We decompose such a multi-component system into mutually influential single-component systems, and formulate the maintenance problem of each component as a Markov decision process (MDP). Under some reasonable assumptions, we prove the existence of the optimal (n_r, N_r) type policy for each component. A policy iteration method is used to calculate its optimal maintenance policy. Based on the method, we develop an iterative approximation algorithm to obtain an acceptable maintenance policy for a multi-component system. Numerical examples find that environmental conditions and OM pose significant effects on a maintenance policy.     

基于拆卸序列的多部件设备机会维护策略研究

针对多部件设备进行维护时未考虑需要拆卸的问题,建立了一种基于拆卸序列的多部件设备机会维护策略模型.首先,以拆卸混合图表示部件间的拆卸序列,进而得出各部件的拆卸时间和拆卸成本;然后针对各部件的衰退过程,结合役龄递减因子和故障率递增因子的混合故障率来描述各部件的衰退特性;其次,在此基础上,以设备的机会维护时间窗为决策变量,在设备满足一定可用度的情况下,以设备在有限运行时间内的最低维护成本为优化目标,求解设备的最优预防维护计划;最后,通过数值仿真分析,验证所提出的多部件设备机会维护策略的有效性.     

Preventive maintenance scheduling for a multi-component system with non-negligible replacement time

When maintenance models are developed for complex continuous operating systems, with large production losses, it is important to take into account the maintenance duration, in order to provide an effective maintenance program. In this work, a preventive maintenance model is presented in order to coordinate the component replacements in a multi-component system. The model is based on the partial periodic renewal policy. At each partial renewal, a group of components to be replaced is defined. When all components are replaced simultaneously, we consider that the system undergoes an overhaul which resets it to the new state. Moreover, the model takes into account for non-negligible replacement times, where different assumptions are considered. The relevant effect of maintenance times on the optimal policy is clearly shown by the numerical results, provided by the application in an oil refinery.     

基于性能合同的多部件系统维修策略模型优化

针对多部件串联系统维修效率低下与供应商所获利润少的问题,考虑各部件间的经济相关性,提出基于性能合同的多部件系统的维修策略模型.首先,采用韦伯(Weibull)分布描述系统各部件的使用寿命规律,并通过判断各部件使用度与预防性维修阈值和机会维修阈值的关系来实施不同的维修策略;其次,计算单位更新周期内的各维修活动概率和对应维...     

Calendar maintenance of arbitrarily structured systems

A semi-Markov model is constructed that describes an arbitrarily structured multicomponent technological system and takes into account its calendar maintenance. Using an aggregation algorithm, approximate values of stationary reliability characteristics and an optimal term of maintenance work are determined for the system. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 69–86, March–April 2006.     

Scheduling and control of failure prone systems with demand uncertainty and job overlaps

Given that the overlapping of jobs is permitted, the paper studies the scheduling and control of failure prone production systems,i, e.so-called settings with demand uncertainty and job overlaps. Because a variable demand resource is revolved in the production and corrective maintenance control problems of the system, which switched randomly between zero and a maximum level, it is difficult to obtain the analytical solutions of the optimal single hedging point policy. An asymptotic optimal scheduling policy is presented and a double hedging point policy is offered to control simultaneously the production rate and the corrective maintenance rate of the system. The corresponding analytical solutions and approximate solutions are obtained. Considering the relationship of production, corrective maintenance and demand variable, an approximate optimal single hedging point control policy is proposed. Numerical results are presented.     

Optimal maintenance policy incorporating system level and unit level for mechanical systems

The study works on a multi-level maintenance policy combining system level and unit level under soft and hard failure modes. The system experiences system-level preventive maintenance (SLPM) when the conditional reliability of entire system exceeds SLPM threshold, and also undergoes a two-level maintenance for each single unit, which is initiated when a single unit exceeds its preventive maintenance (PM) threshold, and the other is performed simultaneously the moment when any unit is going for maintenance. The units experience both periodic inspections and aperiodic inspections provided by failures of hard-type units. To model the practical situations, two types of economic dependence have been taken into account, which are set-up cost dependence and maintenance expertise dependence due to the same technology and tool/equipment can be utilised. The optimisation problem is formulated and solved in a semi-Markov decision process framework. The objective is to find the optimal system-level threshold and unit-level thresholds by minimising the long-run expected average cost per unit time. A formula for the mean residual life is derived for the proposed multi-level maintenance policy. The method is illustrated by a real case study of feed subsystem from a boring machine, and a comparison with other policies demonstrates the effectiveness of our approach.     

The control of a two-level Markov decision process by time aggregation

The solution of Markov Decision Processes (MDPs) often relies on special properties of the processes. For two-level MDPs, the difference in the rates of state changes of the upper and lower levels has led to limiting or approximate solutions of such problems. In this paper, we solve a two-level MDP without making any assumption on the rates of state changes of the two levels. We first show that such a two-level MDP is a non-standard one where the optimal actions of different states can be related to each other. Then we give assumptions (conditions) under which such a specially constrained MDP can be solved by policy iteration. We further show that the computational effort can be reduced by decomposing the MDP. A two-level MDP with M upper-level states can be decomposed into one MDP for the upper level and M to M(M-1) MDPs for the lower level, depending on the structure of the two-level MDP. The upper-level MDP is solved by time aggregation, a technique introduced in a recent paper [Cao, X.-R., Ren, Z. Y., Bhatnagar, S., Fu, M., & Marcus, S. (2002). A time aggregation approach to Markov decision processes. Automatica, 38(6), 929-943.], and the lower-level MDPs are solved by embedded Markov chains.     

Decentralized adaptive iterative learning control for interconnected systems with uncertainties

In many applications,the system dynamics allows the decomposition into lower dimensional subsystems with interconnections among them.This decomposition is motivated by the ease and flexibility of the controller design for each subsystem.In this paper,a decentralized model reference adaptive iterative learning control scheme is developed for interconnected systems with model uncertainties.The interconnections in the dynamic equations of each subsystem are considered with unknown boundaries.The proposed controller of each subsystem depends only on local state variables without any information exchange with other subsystems.The adaptive parameters are updated along iteration axis to compensate the interconnections among subsystems.It is shown that by using the proposed decentralized controller,the states of the subsystems can track the desired reference model states iteratively.Simulation results demonstrate that,utilizing the proposed adaptive controller,the tracking error for each subsystem converges along the iteration axis.     

含聚集物化视图的增量维护方法

针对含聚集物化视图的增量维护问题,提出了一种基于逐步更新思想的增量维护模型和方法.该方法建立了聚集函数计算前查询结果与基表记录之间的映射关系表,并根据映射关系表的增量变化更新物化视图的聚集函数.针对之前方法对于MAX/MIN聚集函数增量更新的不足,在仅利用增量数据不能更新聚集函数值时,采用仅重新计算映射关系表中相关行的MAX/MIN函数值的方法,极大地减小了更新MAX/MIN聚集函数的计算量.在OSCAR数据库的应用表明了该方法的有效性和高效性.     

透射光谱线性空间核学习建模求解多组分浓度

本文对求解多组分体系浓度的分光光度计同时测定法提供了一种建模方法.通过对多组分体系的透射光谱建立光谱线性空间,证明了多组分体系的分子势函数矩阵V_M可以转化为对角矩阵,对角矩阵的对角元是单组分势函数的块矩阵J_i.多组分体系透射光谱空间是单组分分子透射光谱函数为基函数、分子数量占比为坐标的线性组合.利用多核学习法确定各单组分体系透射波的占比权重系数,提出了用单组分浓度光量子隧穿软测量模型测定多组分浓度的测量方法.实例验证表明此方法稳定可靠,且能降低计算的复杂性.     

Degradation prediction and rolling predictive maintenance policy for multi-sensor systems based on two-dimensional self-attention

Traditional preventive maintenance policy gradually failed to guarantee the security and economy of current mechanical systems. This paper proposed a highly efficient rolling predictive maintenance (RPdM) policy for multi-sensor system, to make maintenance decisions. In this policy, to cope with the uncertainty of remaining useful life (RUL) prediction, the degradation process of the system is first divided into four intervals according to the inspection interval and spare parts lead time. Then, the two-dimensional self-attention (TDSA) method, which extract time dimensional and feature dimensional features by parallel computation, is developed to predict the probabilities of system RUL in the four intervals instead of the point of RUL. In addition, the output probabilities of the TDSA method are utilized to calculate the maintenance cost rate of the current inspection point and future point. The maintenance decision including spare parts ordering time and maintenance time is determined by comparing the maintenance cost rate of each inspection point, and the decision is updated at the next inspection point. To verify the effectiveness of the proposed RPdM policy, the C-MAPSS dataset provided by NASA is employed to implement degradation prediction and maintenance decision. Experiment results show that the cost rate of RPdM policy is lower than preventive maintenance policy, and only 27.7% higher than ideal maintenance policy which is impossible in real engineering. Moreover, the impact of different out-of-stock costs and corrective costs are explored and shows the good robustness of the RPdM policy.     

Control of manufacturing systems with a two-value, production-dependent failure rate

In this paper we consider a failure prone, single machine, single part-type, limited inventory, manufacturing system subject to a non-homogeneous Markov failure/repair process with the failure rate depending on the production rate through a two-value function. The problem is to minimize a cost function which includes a penalty for waiting customers. We derive and prove the optimality of a policy which depends on a threshold but is not the so-called hedging point policy, optimal for the Markov homogeneous case.     

Failure analysis of engineering systems with preventive maintenance and failure interactions

Optimal operation and maintenance of engineering systems heavily rely on the accurate prediction of their failures. Most engineering systems, especially mechanical systems, are susceptible to failure interactions. These failure interactions can be estimated for repairable engineering systems when determining optimal maintenance strategies for these systems. An extended Split System Approach is developed in this paper. The technique is based on the Split System Approach and a model for interactive failures. The approach was applied to simulated data. The results indicate that failure interactions will increase the hazard of newly repaired components. The intervals of preventive maintenance actions of a system with failure interactions, will become shorter compared with scenarios where failure interactions do not exist.     

执行器故障多率采样间歇过程的鲁棒 耗散迭代学习容错控制

针对一类具有干扰和执行器故障的多率采样间歇过程,提出一种具有鲁棒耗散性能的迭代学习容错控制算法.通过提升技术将多采样率过程用慢速率采样的状态空间模型来描述,并基于二维系统理论,把迭代学习控制过程转化为等价2D Roesser故障系统,再沿时间和迭代方向设计具有耗散性能的反馈容错控制器,并以线性矩阵不等式形式给出容错控制器存在的充分条件,同时确保多率采样间歇过程在正常和故障条件下的耗散性能.注塑过程的注射速度控制仿真验证了方法的有效性和可行性.     

离散时间Itˆo 型跳变系统Lyapunov 方程的有限次迭代求解算法

针对离散时间Itˆo 型马尔科夫跳变系统Lyapunov 方程的求解给出一种迭代算法. 经证明, 在误差允许的范围内, 该算法可以在确定的有限次数内收敛到系统的精确解, 收敛速度较快, 具有良好的数值稳定性, 并且该算法为显式迭代, 可避免迭代过程中求解其他矩阵方程对结果精度产生的影响. 最后通过一个数值算例对该算法的有效性进行了验证.

     

Tool maintenance optimization for multi-station machining systems with economic consideration of quality loss and obsolescence

Tools used in a machining process are vulnerable to frequent wear-outs and failures during their useful life. Maintenance is thus considered essential under such conditions. Additionally, it is widely recognized that the maintenance of manufacturing equipments and the quality of manufactured product are highly interrelated. However, few detailed study has been found in the literature dealing with the effects of maintenance policies on the operational performance of such a system, especially the long-term average cost. The need for a method to determine the optimal tool maintenance policy has become increasingly important. Since the multiple tools in a multi-station machining system generally have significant interactive impacts on the product quality loss, the optimal multi-component maintenance models for several policies are investigated to address the interdependence among these tools. Three distinctive multi-component maintenance policies, i.e., age replacement, block replacement, and block replacement with minimal repair, are identified and analyzed. The proposed approach focuses on these maintenance policies with consideration of both component catastrophic failures, and the interdependence of component degradations on the product quality loss as well as the obsolescence cost. The effects of various maintenance policies on the system performance are simulated, and they are used to determine the best policy for a given system. An illustrative example is used to demonstrate effectiveness and applicability of the proposed approach. The results presented a comparative analysis of specified maintenance policies with respect to the total maintenance cost with consideration of the product quality loss and the obsolescence cost.     

Almost sure convergence of iterative learning control for stochastic systems

This paper proposes an iterative learning control (ILC) algorithm with the purpose of controling the output of a linear stochastic system presented in state space form to track a desired realizable trajectory. It is proved that the algorithm converges to the optimal one a.s. under the condition that the product input-output coupling matrices are full-column rank in addition to some assumptions on noises. No other knowledge about system matrices and covariance matrices is required.     

严格反馈互联系统分散式backstepping自适应迭代学习控制

基于Lyapunov分析方法,针对具有严格反馈形式的非线性互联系统,本文设计了一种分散式backstepping自适应迭代学习控制器.子系统之间的互联项为所有子系统输出项线性有界,为每个子系统设计的控制器仅采用该子系统的信息,不需要子系统之间相互传递信息.在控制器中,引入在时间轴和迭代轴上同时更新的自适应参数,以补偿子系统之间的互联项影响.通过采用本文给出的控制器,可使得每个子系统的输出跟踪相应的参考模型输出,仿真结果验证了本文算法的有效性.