高速公路路面预防性养护探讨分析

高速公路路面养护分预防性养护和矫正性养护,预防性养护在高速公路养护体系中起着至关重要的作用。实施预防性养护与不进行预防性养护,高速公路路面寿命相差较大,充分体现预防性养护的经济效益。     

松花江中意系列微型客车的定期检查与维护

结合实践,对松花江中意微型客车的定期维护期限、检查项目及安全事项进行了论证,以供维护作业参考。     

Periodic preventive maintenance policy with infinite time and limit of reliability based on health index

The aging of machine can be slowed and the life of machine can be increased by implementing periodic predictive maintenance. In this article,an improvement factor considering maintenance cost,age of machine and learning effect in the process of maintaining is constructed to describe the effect of periodic preventive maintenance. The model of improvement factor can be fitted through health index. Based on improvement factor with infinite time,cost function including preventive maintenance cost,minimal repair...     

用变点分析方法评价城市公共汽车预防维修效果

维修保养的效果影响车辆的运行可靠性和维修费用.基于现场数据,采用变点分析方法,分析汽车二级保养和三级保养前、后失效发生率的变化,并由此评价车辆不同级保养的效果.该方法可为企业维修质量的改进提供依据.     

TL2000路面强化剂在重庆鹅公岩大桥桥面预防性养护中的应用

介绍了TL2000路面强化剂主要特点和施工方法,以及在重庆鹅公岩大桥桥面工程中的成功应用.实测数据表明,TL2000路面强化剂可以有效地提高桥面的防渗水性能,适用于钢箱梁桥桥面的预防性养护.     

Heuristics for the identical machine scheduling problem with preventive maintenances

In this paper, two mixed integer programming models integrating production scheduling and preventive maintenances are proposed to derive the optimal solutions for the identical machine scheduling problem with unavailability constraints. In the first model, the maintenance activities are performed periodically and the objective is to minimize the makespan. In the second model, the maintenance activities are flexible and the machines’ continuous working time cannot exceed a maximum allowed time T; the objective is to minimize the total completion time of jobs. For the first problem, we propose a heuristic longest batch time (LBT) and prove that the worst case error bound of LBT is 2. For the second problem, we develop a heuristic modified smallest processing time (MSPT) based on some properties of the optimal solutions. Computational experiments show that both of the heuristics are effective and efficient compared with the results obtained by CPLEX and the other algorithms.     

Evolutionary algorithms for solving unconstrained multilevel lot-sizing problem with series structure

This paper presents a comparative study of evolutionary algorithms which are considered to be effective in solving the multilevel lot-sizing problem in material requirement planning (MRP) systems. Three evolutionary algorithms (simulated annealing (SA), particle swarm optimization (PSO) and genetic algorithm (GA)) are provided. For evaluating the performances of algorithms, the distribution of total cost (objective function) and the average computational time are compared. As a result, both GA and PSO have better cost performances with lower average total costs and smaller standard deviations. When the scale of the multilevel lot-sizing problem becomes larger, PSO is of a shorter computational time.     

Robust optimization for the correlated MIMO downlink with imperfect channel state information

To reduce the performance deterioration induced by imperfect channel state information (CSI) in cor-related multiple input multiple output(MIMO) downlink, the linear transmit/receive filters should be optimized to be robust to imperfect CSI. A sub-optimization algorithm based on minimizing sum MSE conditional on available imperfect CSI estimates subject to a per-user power constraint is proposed. The algorithm adapts the existing MMSE algorithm from uncorrelated single-user MIMO system with perfect CSI to correlated MIMO downlink with imperfect CSI. Simulation shows that the suboptimal algorithm can effectively mitigate the performance loss induced by imperfect CSI and has a good convergence performance. In addition, the effect of spatial correlation on the performance of the proposed algorithm is also simulated.     

Sliding mode robust fault-tolerant control for uncertain systems with time delay

Considering the modeling uncertainties and external disturbance, a kind of sliding mode robust H∞fault-tolerant control method for time delay system with actuator fault is proposed. The upper-bound of the uncertainties is considered as a known constant, while the upper-bound of the actuator fault is unknown. A sufficient condition for the existence of an integral sliding mode dynamics is given in terms of linear matrix inequality(LMI). A novel adaptive law is given to estimate the unknown upper-bound of faults. On this basis, a type of sliding mode robust H∞fault-tolerant control law is designed to guarantee the asymptotic stability and the H_∞ performance index of the system. Finally, the simulation on quad-rotor semi-physical platform demonstrates the reliability and validity of the method.     

Online sequential extreme learning machine based multilayer perception with output self feedback for time series prediction

This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedback to the network input layer to create a temporal relation between the current node inputs and the lagged node outputs while overcoming the limitation of memory which is a vital part for any time-series prediction application. The model can overcome the static prediction problem with most time series prediction models and can effectively cope with the dynamic properties of time series data. A linear and a nonlinear forecasting algorithms based on online extreme learning machine are proposed to implement the output feedback forecasting model. They are both recursive estimator and have two distinct phases: Predict and Update. The proposed model was tested against different kinds of time series data and the results indicate that the model outperforms the original static model without feedback.     

A modified genetic algorithm for product family optimization with platform specified by information theoretical approach

Many existing product family design methods assume a given platform, However, it is not an in-tuitive task to select the platform and unique variable within a product family. Meanwhile, most approachesare single-platform methods, in which design variables are either shared across all product variants or not atall. While in multiple-platform design, platform variables can have special value with regard to a subset ofproduct variants within the product family, and offer opportunities for superior overall design. An informationtheoretical approach incorporating fuzzy clustering and Shannon's entropy was proposed for platform variablesselection in multiple-platform product family. A 2-level chromosome genetic algorithm (2LCGA) was proposedand developed for optimizing the corresponding product family in a single stage, simultaneously determiningthe optimal settings for the product platform and unique variables. The single-stage approach can yield im-provements in the overall performance of the product family compared with two-stage approaches, in which thefirst stage involves determining the best settings for the platform and values of unique variables are found foreach product in the second stage. An example of design of a family of universal motors was used to verify theproposed method.     

Joint optimization of spectral and energy efficiency for multi-pair full-duplex two-way relay networks with imperfect channel state information

An iterative algorithm is proposed for jointly optimizing spectral and energy efficiency in a multipair full-duplex (FD) two-way relaying (TWR) system with imperfect channel state information (CSI). Based on Dinkelbach method, a Taylor expansion based approximation method and the Generalized Lagrange Multiplier Method have been applied iteratively to obtain the near optimal relay amplified matrix and power allocation, respectively. And the simulation results illustrate the effectiveness of the proposed algorithm and the algorithm can converge quickly.     

Distributed model predictive control with one-step delay communication for large-scale systems and a case study

A distributed model predictive control（MPC） scheme with one-step delay communication is proposed for on-line optimization and control of large-scale systems in this paper. Cooperation between subsystems is achieved by exchanging information with neighbor-to-neighbor communication and by optimizing the local problem with the improved performance index in the neighborhood. A distributed MPC algorithm with one-step delay communication is developed for the situation that there is a one-step delay in the information available from its neighbors when a subsystem solves the local optimization problem. The nominal stability is employed for the whole system under the distributed MPC algorithm without the inequality constraints. Finally, the case study of the reactor-storage-separator（RSS） system is illustrated to test the practicality of the presented control algorithm.     

Capacity and QoS based codebook construction for MIMO precoded spatial multiplexing systems with limited feedback

This paper deals with the design and performance analysis of transmit precoder optimization for multiple input multiple output (MIMO) systems with limited feedback of channel state information. A capacity criterion based optimal codebook construction algorithm for MIMO precoded spatial multiplexing systems is pre-sented. The optimal precoder structure combining precoding and power allocation is employed. Simulation results show that the capacity criteria based codebook can achieve higher capacity than that of equally power allocation based codebook of previous research.     

Combustion optimization model for NO<Subscript><Emphasis Type="Italic">x</Emphasis></Subscript> reduction with an improved particle swarm optimization

Abstract: This paper focuses on the combustion optimization to cut down NO _x emission with a new strategy. Firstly, orthogonal experimental design (OED) and chaotic sequences are introduced to improve the performance of particle swarm optimization (PSO). Then, a predicting model for NO _x emission is established on support vector machine (SVM) whose parameters are optimized by the improved PSO. Afterwards, a new optimization model considering coal quantity and air quantity along with the traditional optimization variables is established. At last, the operating parameters are optimized by the improved PSO to cut down the NO _x emission. An application on 600MW unit shows that the new optimization model can cut down NO _x emission effectively and maintain the load balance well. The NO _x emission optimized by the improved PSO is lowest among some state-of-the-art intelligent algorithms. This study can provide important guides for the low NO _x combustion in the power plant.     

Optimal inspection and replacement strategy for systems subject to two types of failures with adjustable inspection intervals

The inspection activities are often carried out to detect possible indication of failures in plant systems. This paper considers a single unit system subject to two types of failures, where one failure mode is the traditional 0-1 logic failure and the other failure mode is described by a two-stage failure process. Adjustable inspections are used to detect the defective stage of the latter. We assume that the inspection duration gets shorter and shorter with a constant ratio. At the same time, preventive replacement is used to avoid the possible failure due to the former failure mode. The renewal process of this system is analyzed and the expected long-run cost per unit time (ELRCUT) is derived. The optimal inspection period and the preventive replacement interval to minimize ELRCUT are studied. At last, a numerical example is presented to illustrate the proposed model.