Optimal maintenance planning for sustainable energy efficiency lighting retrofit projects by a control system approach

The energy savings achieved by implementing energy efficiency (EE) lighting retrofit projects are sometimes not sustainable and vanish rapidly given that lamp population decays as time goes by if without proper maintenance activities. Scope of maintenance activities refers to replacements of failed lamps due to nonrepairable lamp burnouts. Full replacements of all the failed lamps during each maintenance interval contribute to a tight project budget due to the expense for the lamp failure inspections, as well as the procurement and installation of new lamps. Since neither “no maintenance” nor “full maintenance” is preferable to the EE lighting project developers (PDs), we propose to design an optimal maintenance plan that optimises the number of replacements of the failed lamps, such that the EE lighting project achieves sustainable performance in terms of energy savings whereas the PDs obtain their maximum benefits in the sense of cost–benefit ratio. This optimal maintenance planning (OMP) problem is aptly formulated as an optimal control problem under control system framework, and solved by a model predictive control (MPC) approach. An optimal maintenance plan for an EE lighting retrofit project is designed as a case study to illustrate the effectiveness of the proposed control system approach.     

A methodology for joint optimization for maintenance planning, process quality and production scheduling

Performance of a manufacturing system depends significantly on the shop floor performance. Traditionally, shop floor operational policies concerning maintenance scheduling, quality control and production scheduling have been considered and optimized independently. However, these three aspects of operations planning do have an interaction effect on each other and hence need to be considered jointly for improving the system performance. In this paper, a model is developed for joint optimization of these three aspects in a manufacturing system. First, a model has been developed for integrating maintenance scheduling and process quality control policy decisions. It provided an optimal preventive maintenance interval and control chart parameters that minimize expected cost per unit time. Subsequently, the optimal preventive maintenance interval is integrated with the production schedule in order to determine the optimal batch sequence that will minimize penalty-cost incurred due to schedule delay. An example is presented to illustrate the proposed model. It also compares the system performance employing the proposed integrated approach with that obtained by considering maintenance, quality and production scheduling independently. Substantial economic benefits are seen in the joint optimization.     

An optimization algorithm inspired by propagation of yeast for fleet maintenance decision making problem involving fatigue structures

To minimize airline maintenance costs and maximize fleet availability, we developed a fleet maintenance decision-making model based on CBM with collaborative optimization (CO) for fatigue structures. The model is divided into two levels: a system level and a subsystem level. Different optimization routines are used at these two levels. The system level focuses on maximizing fleet availability and the subsystem level focuses on minimizing aircraft maintenance costs. Moreover, we proposed an optimization algorithm inspired by the propagation of yeast (OA/PY) to handle the situation where optimal solution is not unique. Finally, a case study regarding a fleet of 10 aircrafts is conducted, and the results demonstrated the effectiveness of the proposed algorithm. In the case study, aircraft maintenance planning (subsystem level) was obtained, and then it was adjusted with OA/PY to obtain optimal fleet maintenance plan (system level). Total incremental maintenance cost caused by the adjustment in the proposed method was reduced by 70.65%.     

A condition-based maintenance policy for a production system under excessive environmental degradation

In this paper a condition-based maintenance model is proposed for a single-unit system of production of goods and services. The system is subject to random deterioration which impacts not only the product quality but also the environment. We assume that the environment degrades whenever a specific level of system deterioration is reached. The proposed maintenance model aims to assess the degradation in such a way to reduce the deterioration of the environment. To control this deterioration, inspections are performed and after which the system is preventively replaced or left as it is. Preventive replacement occurs whenever the level of the system degradation reaches a specific level threshold. The objective is to determine optimal inspection dates which minimize the average total cost per unit of time in the infinite horizon. Cost function is composed of inspection and maintenance costs in addition to a penalty cost due to environmental deterioration. The maintenance optimization model is formally derived. On the basis of Nelder–Mead method, inspection dates as optimal solutions are computed. A numerical example is provided to illustrate the proposed maintenance model.     

Maintenance scheduling and production control of multiple-machine manufacturing systems

This paper deals with the production and preventive maintenance control problem for a multiple-machine manufacturing system. The objective of such a problem is to find the production and preventive maintenance rates for the machines so as to minimize the total cost of inventory/backlog, repair and preventive maintenance. A two-level hierarchical control model is presented, and the structure of the control policy for both identical and non-identical manufacturing systems is described using parameters, referred to here as input factors. By combining analytical formalism with simulation-based statistical tools such as experimental design and response surface methodology, an approximation of the optimal control policies and values of input factors are determined. The results obtained extend those available in existing literature to cover non-identical machine manufacturing systems. A numerical example and a sensitivity analysis are presented in order to illustrate the robustness of the proposed approach. The extension of the proposed production and preventive maintenance policies to cover large systems (multiple machines, multiple products) is discussed.     

Optimization of preventive maintenance for a multi-state degraded system by monitoring component performance

This study aims to construct an optimal preventive maintenance model for a multi-state degraded system under the condition that individual components or sub-systems can be monitored in real time. Given the requirement of minimum system availability, the total maintenance cost is minimized by determining the maintenance activities of components in degraded states. The general non-homogeneous continuous-time Markov model (NHCTMM) and its analogous Markov reward model (NHCTMRM) are used to quantify the intensity of state transitions during the degradation process, allowing the determination of various performance indicators. The bound approximation approach is applied to solve the established NHCTMMs and NHCTMRMs, thus obtaining instantaneous system state probabilities to overcome their inherent computational difficulties. Furthermore, this study utilizes a genetic algorithm to optimize the proposed model. A simulation illustrates the feasibility and practicability of the proposed approach.     

Neural-network-based stochastic linear quadratic optimal tracking control scheme for unknown discrete-time systems using adaptive dynamic programming

In this paper, a stochastic linear quadratic optimal tracking scheme is proposed for unknown linear discrete-time (DT) systems based on adaptive dynamic programming (ADP) algorithm. First, an augmented system composed of the original system and the command generator is constructed and then an augmented stochastic algebraic equation is derived based on the augmented system. Next, to obtain the optimal control strategy, the stochastic case is converted into the deterministic one by system transformation, and then an ADP algorithm is proposed with convergence analysis. For the purpose of realizing the ADP algorithm, three back propagation neural networks including model network, critic network and action network are devised to guarantee unknown system model, optimal value function and optimal control strategy, respectively. Finally, the obtained optimal control strategy is applied to the original stochastic system, and two simulations are provided to demonstrate the effectiveness of the proposed algorithm.     

相依多模失效下冷贮备系统最优切换及视情维护决策

针对存在冲击影响的冷贮备系统,研究其最优切换及视情维护决策问题.首先,在系统结构和切换式运行和维护特性分析的基础上,制定基于周期切换和状态检测的切换式离线视情维护策略;其次,建立累积冲击过程影响下系统退化所致的软失效和极端冲击过程所致的硬失效竞争可靠性模型;再次,通过分析两类冲击过程影响下系统运行与备用设备交替使用、维修过程中的状态转移特性,重点推导各检测周期时刻系统状态概率分布的迭代计算模型;然后,以系统平均费用率最小为目标,建立解析决策模型,以求解系统的最优切换周期和维护阈值.最后,以矿井主通风系统为案例验证策略及模型的有效性,并分析模型对参数的灵敏度.结果表明,系统的最优维修策略随机冲击影响的不同而变化显著.     

基于线性二次型高斯基准的多变量预测控制技术经济性能评估

由于受控过程参数的漂移及缺乏维护,令采用的控制器性能逐渐降低,需要做经济性能评估,以确保其最佳运行状态.因为目前最小方差评估算法没有考虑控制器的约束条件,对此我们采用线性二次型高斯(linearquadratic Gaussian,LQG)基准的模型预测控制(model predictive control,MPC)双层优化控制结构,将控制和输出的加权值引入上层经济性能指标,通过求解LQG问题获取控制与输出方差关系的离散点集,进一步拟合Pareto最优曲面方程,建立优化命题并求解最优经济指标及设定值.对延迟焦化加热炉的多变量MPC控制进行了性能评估及分析,证明该方法可以改进控制器设计,提高经济效益.     

冷/温混合贮备系统最优切换及视情维修决策

针对周期性切换冷/温混合贮备系统,研究其最优切换以及视情维修决策,在系统劣化建模的基础上,分析系统结构和切换式运行维修特性,制定基于周期切换和检测的离线视情维修策略.首先,通过分析系统运行与备用设备交替使用、维修过程中的状态转移特性,推导各检测周期时刻系统状态概率分布模型以及各维修活动的概率;然后,以系统有限时间范围内平均费用率最小为目标建立解析优化模型,以决策最优切换周期和维护阈值,并采用遗传算法对模型进行求解;最后,以汽轮发电机定子冷却水泵系统为对象验证策略和模型的正确性和有效性,并对参数进行灵敏度分析.实验结果表明,所提出离线视情维修策略能够有效地降低系统的维修成本.     

基于退化状态空间划分的风电机组视情维修决策

风场的运维成本约占其收入的三分之一之多,风电机组的最优维修问题一直是风电系统降低运维成本的主要途径.针对同一风场多台风力机组成的系统,制定基于状态检测的视情机会维修策略,提出基于退化状态空间划分的多设备系统状态维修决策建模方法,在此基础上建立维修成本最小的解析模型,以决策风力机最优的状态检测周期和维修阈值.实验结果表明,基于状态监测的风力机视情维修机会方案可以很好地节约系统运维成本.     

Distributed joint dynamic maintenance and production scheduling in manufacturing systems: Framework based on model predictive control and Benders decomposition

Scheduling the maintenance based on the condition, respectively the degradation level of the system leads to improved system's reliability while minimizing the maintenance cost. Since the degradation level changes dynamically during the system's operation, we face a dynamic maintenance scheduling problem. In this paper, we address the dynamic maintenance scheduling of manufacturing systems based on their degradation level. The manufacturing system consists of several units with a defined capacity and an individual dynamic degradation model, seeking to optimize their reward. The units sell their production capacity, while maintaining the systems based on the degradation state to prevent failures. The manufacturing units are jointly responsible for fulfilling the demand of the system. This induces a coupling constraint among the agents. Hence, we face a large-scale mixed-integer dynamic maintenance scheduling problem. In order to handle the dynamic model of the system and large-scale optimization, we propose a distributed algorithm using model predictive control (MPC) and Benders decomposition method. In the proposed algorithm, first, the master problem obtains the maintenance scheduling for all the agents, and then based on this data, the agents obtain their optimal production using the distributed MPC method which employs the dual decomposition approach to tackle the coupling constraints among the agents. The effectiveness of the proposed method is investigated on two case studies.     

Multi-objective imperfect maintenance optimization for production system with an intermediate buffer

In several production systems, buffer stocks are built between consecutive machines to ensure the continuity of supply during interruptions of service caused by breakdowns or planned maintenance actions. However, in previous research, maintenance planning is performed individually without considering buffer stocks. In order to balance the trade-offs between them, in this study, an integrated model of buffer stocks and imperfective preventive maintenance for a production system is proposed. This paper considers a repairable machine subject to random failure for a production system by considering buffer stocks. First, the random failure rate of a machine becomes larger with the increase of the number of random failures. Thus, the renewal process is used to describe the number of random failures. Then, by considering the imperfect maintenance action reduced the age of the machine partially, a mathematical model is developed in order to determine the optimal values of the two decision variables which characterize the proposed maintenance strategy and which are: the size of the buffer stock and the maintenance interval. The optimal values are those which minimize the average total cost per time unit including maintenance cost, inventory holding cost and shortage cost, and satisfy the availability constraint. Finally, a heuristic procedure is used to solve the proposed model, and one experiment is used to evaluate the performance of the proposed methods for joint optimization between buffer stocks and maintenance policy. The results show that the proposed methods have a better performance for the joint optimization problem and can be able to obtain a relatively good solution in a short computation time.     

Robust production and maintenance planning in stochasticmanufacturing systems

In this paper the authors consider a preventive maintenance and production model of a flexible manufacturing system with machines that are subject to breakdown and repair. The preventive maintenance can be used to reduce the machine failure rates and improve the productivity of the system. The control variables are the rate of maintenance and the rate of production; the objective is to choose a control process that optimizes a robust cost of inventory/shortage, production, and maintenance. The value function is shown to be locally Lipschitz and to satisfy a Hamilton-Jacobi-Isaacs equation. A sufficient condition for optimal control is obtained. Finally, an algorithm is given for solving the optimal control problem numerically     

基于服务性能合同模式下单部件系统视情维修策略研究

针对供应商系统维修的低效率以及维修成本参数较难获得的问题,提出了基于服务性能合同模式(PBC)下的单部件系统最优视情维修策略模型。首先,基于Gamma分布,描述单部件系统连续递增的退化过程,依据系统实时检测状态与预防维修阈值、故障阈值之间的关系,实施不同的维修策略;其次,分析单位更新周期内的检测次数和使故障设备恢复如新的维修方式,以供应商利润率最大化为目标函数,以最佳维修阈值与检测间隔时间为决策变量,建立以利润为中心的视情维修优化模型;最后,利用改进灰狼算法求解数学模型,通过算例验证所提出模型的有效性,并进行了各维修费用参数对目标函数以及最优维修策略的灵敏度分析。     

Maintenance policies in markovian deterioration models of parallel unit systems

Markovian deterioration models of parallel unit systems are dealt with. Units of a system are supposed to be of different types, and a state of the system is represented by a vector whose component corresponds to the state of each unit. Therefore all the states of the system are only partially ordered, while they are totally ordered in ordinary markovian deterioration models. It is proved under some conditions that the optimal maintenance policy is a partial control limit policy, which is a generalization of the well-known control limit policy in the ordinary case. An algorithm improving the successive approximation method is proposed for obtaining the optimal policy. A typical case of the model is also discussed.     

Variance-penalized Markov decision processes: dynamic programming and reinforcement learning techniques

In control systems theory, the Markov decision process (MDP) is a widely used optimization model involving selection of the optimal action in each state visited by a discrete-event system driven by Markov chains. The classical MDP model is suitable for an agent/decision-maker interested in maximizing expected revenues, but does not account for minimizing variability in the revenues. An MDP model in which the agent can maximize the revenues while simultaneously controlling the variance in the revenues is proposed. This work is rooted in machine learning/neural network concepts, where updating is based on system feedback and step sizes. First, a Bellman equation for the problem is proposed. Thereafter, convergent dynamic programming and reinforcement learning techniques for solving the MDP are provided along with encouraging numerical results on a small MDP and a preventive maintenance problem.     

Hybrid state-space fuzzy model-based controller with dual-ratesampling for digital control of chaotic systems

We develop a hybrid state-space fuzzy model-based controller with dual-rate sampling for digital control of chaotic systems. A Takagi-Sugeno (TS) fuzzy model is used to model the chaotic dynamic system and the extended parallel-distributed compensation technique is proposed and formulated for designing the fuzzy model-based controller under stability conditions. The optimal regional-pole assignment technique is also adopted in the design of the local feedback controllers for the multiple TS linear state-space models. The proposed design procedure is as follows: an equivalent fast-rate discrete-time state-space model of the continuous-time system is first constructed by using fuzzy inference systems. To obtain the continuous-time optimal state-feedback gains, the constructed discrete-time fuzzy system is then converted into a continuous-time system. The developed optimal continuous-time control law is finally converted into an equivalent slow-rate digital control law using the proposed intelligent digital redesign method. The main contribution of the paper is the development of a systematic and effective framework for fuzzy model-based controller design with dual-rate sampling for digital control of complex such as chaotic systems. The effectiveness and the feasibility of the proposed controller design method is demonstrated through numerical simulations on the chaotic Chua circuit     

(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.     

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.