An application of DPCA to oil data for CBM modeling

In multivariate time series analysis, dynamic principal component analysis (DPCA) is an effective method for dimensionality reduction. DPCA is an extension of the original PCA method which can be applied to an autocorrelated dynamic process. In this paper, we apply DPCA to a set of real oil data and use the principal components as covariates in condition-based maintenance (CBM) modeling. The CBM model (Model 1) is then compared with the CBM model which uses raw oil data as the covariates (Model 2). It is shown that the average maintenance cost corresponding to the optimal policy for Model 1 is considerably lower than that for Model 2, and when the optimal policies are applied to the oil data histories, the policy for Model 1 correctly indicates almost twice as many impending system failures as the policy for Model 2.     

Condition-based maintenance with imperfect inspections for continuous degradation processes

A condition-based maintenance (CBM) strategy is now recognized as an efficient approach to perform maintenance at the best time before failures so as to save lifetime cycle cost. For continuous degradation processes, a significant source of variability lies in measurement errors caused by imperfect inspections, and this may lead to “false positive” or “false negative” observations, and consequently to inopportune maintenance decisions. To the best of our knowledge, researches on CBM optimization with imperfect inspections remain limited for continuous degradation processes, even though the subject is of practical interest for the implementation of a CBM policy. Imperfect inspections are indeed imperfect but still return interesting information on the system degradation level, and making them perfect can be expensive. Therefore, we analyze the economic performance of a maintenance policy with imperfect inspections, and compare it with the classical policy with perfect inspections to see which policy offers the best benefit in a given situation. Furthermore, a CBM policy with a two-stage inspection scheme is proposed to take benefit of mixing both perfect and imperfect inspections in the same maintenance policy. Through numerical experiments and a real case study, it is shown that the policy with imperfect inspections can be better than the classical one, and that the proposed policy with a two-stage inspection scheme always leads to the minimum long run maintenance cost rate.     

A condition-based maintenance model

Condition-based maintenance (CBM) is very appealing becauseit enables one to make maintenance decisions based on the currentinformation about the system. Various monitoring techniqueshave been developed to obtain this information, but there arestill very few mathematical models capable of utilizing it foreffective maintenance decision making. In this paper, we propose a CBM model for situations where onlypartial information is available through monitoring a signalprocess that does not necessarily exhibit monotone behaviour.The evolution of the signal process is determined by randomfactors and minor maintenance actions between inspections. Theobjective is to find the replacement policy that maximizes thetotal expected profit during the lifetime of the system. Wewill show that, under weak monotonicity assumptions, the optimalpolicy is of a control-limit type, and we will develop an algorithmfor finding the limit for an -optimal policy.     

基于历史故障数据的租赁设备预防维护策略优化

针对租赁设备的特殊性,提出了一种周期预防维护策略模型。该策略综合考虑设备的当前维护周期、预防维护、小修以及惩罚机制等因素对维护成本的影响,从设备的当前维护周期出发,构造出故障率分布的平滑函数,以设备的历史故障数据信息为依据,使用最大似然估计解析方法对设备的故障率分布函数参数进行有效估计,建立以租赁企业维护成本最小化为目标的周期预防维护策略模型。最后是算例分析,研究表明,该策略符合租赁设备维护的实际情况,可为租赁企业提供有效的维护解决方案。     

Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness

The prediction of surface roughness is a challengeable problem. In order to improve the prediction accuracy in end milling process, an improved approach is proposed to model surface roughness with adaptive network-based fuzzy inference system (ANFIS) and leave-one-out cross-validation (LOO-CV) approach. This approach focuses on both architecture and parameter optimization. LOO-CV, which is an effective measure to evaluate the generalization capability of mode, is employed to find the most suitable membership function and the optimal rule base of ANFIS model for the issue of surface roughness prediction. To find the optimal rule base of ANFIS, a new “top down” rules reduction method is suggested. Three machining parameters, the spindle speed, feed rate and depth of cut are used as inputs in the model. Based on the same experimental data, the predictive results of ANFIS with LOO-CV are compared with the results reported recently in the literature and ANFIS with clustering methods. The comparisons indicate that the presented approach outperforms the opponent methods, and the prediction accuracy can be improved to 96.38%. ANFIS with LOO-CV approach is an effective approach for prediction of surface roughness in end milling process.     

Coordinated maintenance in a multi-component system with compound Poisson deterioration

This paper proposes a coordinated maintenance model in a multi-component system with compound Poisson deterioration. The main contribution is a policy-iteration approach for Semi-Markov processes that optimizes the threshold at which the component is eligible for preventive maintenance if another component requires corrective maintenance. The methodology is novel as we develop explicit expressions for the policy evaluation and prove these expressions to satisfy the set of linear equations which characterize traditional policy evaluation. By doing so, long-run average cost savings are achieved, since setup costs can be shared.     

Accelerating data uncertainty quantification by solving linear systems with multiple right-hand sides

The subject of this work is accelerating data uncertainty quantification. In particular, we are interested in expediting the stochastic estimation of the diagonal of the inverse covariance (precision) matrix that holds a wealth of information concerning the quality of data collections, especially when the matrices are symmetric positive definite and dense. Schemes built on direct methods incur a prohibitive cubic cost. Recently proposed iterative methods can remedy this but the overall cost is raised again as the convergence of stochastic estimators can be slow. The motivation behind our approach stems from the fact that the computational bottleneck in stochastic estimation is the application of the precision matrix on a set of appropriately selected vectors. The proposed method combines block conjugate gradient with a block-seed approach for multiple right-hand sides, taking advantage of the nature of the right-hand sides and the fact that the diagonal is not sought to high accuracy. Our method is applicable if the matrix is only known implicitly and also produces a matrix-free diagonal preconditioner that can be applied to further accelerate the method. Numerical experiments confirm that the approach is promising and helps contain the overall cost of diagonal estimation as the number of samples grows.     

A sequential condition‐based repair/replacement policy with non‐periodic inspections for a system subject to continuous wear

This paper studies a condition‐based maintenance policy for a repairable system subject to a continuous‐state gradual deterioration monitored by sequential non‐periodic inspections. The system can be maintained using different maintenance operations (partial repair, as good as new replacement) with different effects (on the system state), costs and durations. A parametric decision framework (multi‐threshold policy) is proposed to choose sequentially the best maintenance actions and to schedule the future inspections, using the on‐line monitoring information on the system deterioration level gained from the current inspection. Taking advantage of the semi‐regenerative (or Markov renewal) properties of the maintained system state, we construct a stochastic model of the time behaviour of the maintained system at steady state. This stochastic model allows to evaluate several performance criteria for the maintenance policy such as the long‐run system availability and the long‐run expected maintenance cost. Numerical experiments illustrate the behaviour of the proposed condition‐based maintenance policy. Copyright © 2003 John Wiley & Sons, Ltd.     

Benchmarking of maintenance performance: a case study in two manufacturers of furniture

When profit margins of a plant are decreasing, the need forreliable and efficient maintenance policy becomes more important.Measuring maintenance performance is important for companiesto recognize whether their planned goals are achieved or not.Also, such measurements can be utilized for benchmarking, whichis one of the tools for never-ending improvement. But, theseobjectives cannot be achieved without well-documented data ofthe relevant variables. Better data coverage and quality isnecessary for following maintenance performance developmentand it would, in many cases, clarify the ambiguity concerningthe main problem in the context, namely that neither the productionnor maintenance department can show what effect maintenancehas on profitability. A more effective maintenance policy indirectly implies improvementsin product quality and manufacturing process effectiveness.Elongation of the production time, i.e. reducing the downtimedue to failures, planned replacements and repair, in additionto the improvement in the total maintenance activities, arealso some of the results that can be expected when an efficientmaintenance policy is used. Measuring and monitoring maintenanceperformance measures is required partly for detecting, and eventuallytreating as soon as possible, undesirable changes and partlyto make benchmarking with the best in the branch, which savesappreciable economic losses for companies. In this paper, a model for how to identify the measurable variables,which are needed to develop measures for monitoring maintenanceperformance behaviour systematically, is developed. Five maintenanceperformance measures are proposed and applied. An additionalmodel for systematically analysing the trend of maintenanceperformance measures, for an overall assessment of the company'ssituation, is presented. Two case studies in manufacturers offurniture are conducted to verify these models.     

Multi-objective OLSR for proactive routing in MANET with delay,energy, and link lifetime predictions

In this paper, we develop a multi-objective approach for proactive routing in a Mobile Ad Hoc Network (MANET). We consider three routing objectives: minimizing average end-to-end delay, maximizing network energy lifetime, and maximizing packet delivery ratio. Accordingly, we develop three routing metrics: mean queuing delay on each node, energy cost on each node, and link stability on each link. For the proposed multi-objective approach, we develop efficient prediction methods: (a) predicting queuing delay and energy consumption using double exponential smoothing, and (b) predicting residual link lifetime using a heuristic of the distributions of the link lifetimes in MANET. Extensive simulation (by using ns2) is performed for the comparison of this multi-objective OLSR with existing OLSRs. The results show that the multi-objective OLSR is effective in finding optimal routing by tradeoffs among proposed objectives.     

基于子空间多核学习的企业财务困境预测方法

财务指标的异构性是影响企业财务困境预测精度的重要因素,现有多核学习方法能够用于解决异构数据学习问题。本文首先介绍了子空间多核学习财务困境预测理论框架,在此基础上根据子空间学习的最大化方差准则、类别可分性最大化准则、非线性子空间映射原理,提出了三种子空间多核学习方法,分别为最大化方差投影子空间多核学习、类别可分性最大化子空间多核学习、非线性子空间多核学习。利用采集的我国上市公司数据进行实验,对比所提出的方法同现有代表性财务困境预测方法,并对实验结果进行分析。实验结果表明,本文提出的子空间多核学习财务困境预测框架行之有效,该框架下所构造的子空间多核学习预测方法能够有效地提升财务困境预测精度。     

An extended grey forecasting model for omnidirectional forecasting considering data gap difference

To achieve effective and efficient decision making in a highly competitive business environment, an enterprise must have an appropriate forecasting technique that can meet the requirements of both timeliness and accuracy. Accordingly, in the early stages, building a forecasting model with incomplete information and limited samples is very important to a business. Grey system theory is one of the prediction methods that can be built with a small sample and yet has a strong ability to make short-term predictions. The purpose of this study is to come up with an improved forecasting model based on the concept of this theory to enlarge the applicability of the grey forecasting model in various situations. By extending the data transforming approach, this method generalizes a building procedure for the grey model to grasp the data outline and information trend. Specifically, a novel inverse accumulating generation operator is developed to enable omnidirectional forecasting. The research utilizes observations of the titanium alloy fatigue limit along with temperature changes as raw data to verify the performance of the proposed method. The experimental results show that not only can this method expand the application scope of the grey forecasting model, but also improve its forecasting accuracy.     

Analysis of seat allocation and overbooking decisions with hybrid information

We investigate a single-leg airline revenue management problem where an airline has limited demand information and uncensored no-show information. To use such hybrid information for simultaneous overbooking and booking control decisions, we combine expected overbooking cost with revenue. Then we take a robust optimization approach with a regret-based criterion. While the criterion is defined on a myriad of possible demand scenarios, we show that only a small number of them are necessary to compute the objective. We also prove that nested booking control policies are optimal among all deterministic ones. We further develop an effective computational method to find the optimal policy and compare our policy to others proposed in the literature.     

Optimal maintenance strategy under renewable warranty with repair time threshold

In this paper, we consider a periodic preventive maintenance model, from the manufacturer's perspective, which can be implemented to reduce the maintenance cost of a repairable product during a given warranty period. The product is assumed to deteriorate with age and the warranty policy we adopt in this paper takes into account the two factors of failure time and repair time of the product when the product failure occurs. Under the proposed two-factor warranty, a repair time threshold is pre-determined and if the repair takes more time than that of the threshold, the failed product is replaced with a renewed warranty policy. Otherwise, the product is only minimally repaired to return to the operating state. During such a renewable warranty period, preventive maintenance is conducted to reduce the rate of degradation periodically while the product is in operation. By assuming certain cost structures, we formulate the expected warranty cost during the warranty period from the manufacturer's perspective when a periodic preventive maintenance strategy is adapted. Although more frequent preventive maintenance increases the warranty cost, the chance of product failures would be reduced. The main aim of this paper is to accomplish the optimal trade-off between the warranty cost and the preventive maintenance period by determining the optimal preventive maintenance period that minimizes the total expected warranty cost during the warranty period. Assuming the power law process for the product failures, we illustrate our proposed maintenance model numerically and study the impact of relevant parameters on the optimal preventive maintenance policy.     

基于两类失效的产品保修策略与出保维修优化

基于消费者对保修的需求,设计消费者偏爱的保修策略是制造商巩固市场地位、提升市场竞争力的一种战略决策。与此同时,以较低费用制定较长出保服役时间的出保维修策略也是消费者一直追求的目标。本文以两类失效产品为研究对象,首先从制造商角度将消费者偏爱的更新免费更换保修策略与产品定价相融合,开展了产品保修策略设计。其次,从消费者角度将预防维修与经典周期更换策略相融合,提出了维修—周期更换策略,且将其作为出保维修策略并对相应的性能进行了说明。通过数值实验发现,利润模型可对保修开展设计;与总费用模型作为目标函数相比,费用率模型作为目标函数可降低寿命周期费用;与经典周期更换策略相比,提出的维修—周期更换策略能使出保服役时间更长、费用率更低。     

Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure

A new method for predicting failures of a partially observable system is presented. System deterioration is modeled as a hidden, 3-state continuous time homogeneous Markov process. States 0 and 1, which are not observable, represent good and warning conditions, respectively. Only the failure state 2 is assumed to be observable. The system is subject to condition monitoring at equidistant, discrete time epochs. The vector observation process is stochastically related to the system state. The objective is to develop a method for optimally predicting impending system failures. Model parameters are estimated using EM algorithm and a cost-optimal Bayesian fault prediction scheme is proposed. The method is illustrated using real data obtained from spectrometric analysis of oil samples collected at regular time epochs from transmission units of heavy hauler trucks used in mining industry. A comparison with other methods is given, which illustrates effectiveness of our approach.     

Age-dependent production planning and maintenance strategies in unreliable manufacturing systems with lost sale

In this paper, we formulate an analytical model for the joint determination of an optimal age-dependent buffer inventory and preventive maintenance policy in a production environment that is subject to random machine breakdowns. Traditional preventive maintenance policies, such as age and periodic replacements, are usually studied based on simplified and non-realistic assumptions, as well as on the expected costs criterion. Finished goods inventories and the age-dependent likelihood of machine breakdowns are usually not considered. As a result, these policies could significantly extend beyond the anticipated financial incomes of the system, and lead to crises. In order to solve this problem, a more realistic analysis model is proposed in this paper to consider the effects of both preventive maintenance policies and machine age on optimal safety stock levels. Hence, a unified framework is developed, allowing production and preventive maintenance to be jointly considered. We use an age-dependent optimization model based on the minimization of an overall cost function, including inventory holdings, lost sales, preventive and corrective maintenance costs. We provide optimality conditions for the manufacturing systems considered, and use numerical methods to obtain an optimal preventive maintenance policy and the relevant age-dependent threshold level production policy. In this work, this policy is called the multiple threshold levels hedging point policy. We include numerical examples and sensitivity analyses to illustrate the importance and the effectiveness of the proposed methodology. Compared with other available optimal production and maintenance policies, the numerical solution obtained shows that the proposed age-dependent optimal production and maintenance policies significantly reduce the overall cost incurred.     

Dynamic monitoring and control of software project effort based on an effort buffer

The improvement to the monitoring and control efficiency of software project effort is a challenge for project management research. We propose to overcome this challenge through the use of a model for the buffer determination and monitoring of software project effort. This software project effort buffer was originally determined on the basis of a risk management factor analysis with total consideration for project managers’ risk preference. The effort buffer was next allocated to different stages according to the buffer allocation cardinal. An effort deviation monitoring and control model was then established based on the grey prediction model, including the establishment of a deviation monitoring and control model, a simulation test of the accuracy and the deviation prediction algorithm flow chart. The method system was eventually applied to an actual project and compared with the actual project data. The results show that the relative error test accuracy of the proposed model is qualified according to the test standard of the grey model, signifying that it could be used for the prediction of effort deviation and decision-making. The proposed model could use the dynamic control system to monitor and control software project effort in an effective manner.     

A particle swarm optimization-based hybrid algorithm for minimum concave cost network flow problems

Traditionally, minimum cost transshipment problems have been simplified as linear cost problems, which are not practical in real applications. Some advanced local search algorithms have been developed to solve concave cost bipartite network problems. These have been found to be more effective than the traditional linear approximation methods and local search methods. Recently, a genetic algorithm and an ant colony system algorithm were employed to develop two global search algorithms for solving concave cost transshipment problems. These two global search algorithms were found to be more effective than the advanced local search algorithms for solving concave cost transshipment problems. Although the particle swarm optimization algorithm has been used to obtain good results in many applications, to the best of our knowledge, it has not yet been applied in minimum concave cost network flow problems. Thus, in this study, we employ an arc-based particle swarm optimization algorithm, coupled with some genetic algorithm and threshold accepting method techniques, as well as concave cost network heuristics, to develop a hybrid global search algorithm for efficiently solving minimum cost network flow problems with concave arc costs. The proposed algorithm is evaluated by solving several randomly generated network flow problems. The results indicate that the proposed algorithm is more effective than several other recently designed methods, such as local search algorithms, genetic algorithms and ant colony system algorithms, for solving minimum cost network flow problems with concave arc costs.     

A new method for parameter sensitivity estimation in structural reliability analysis

For the parameter sensitivity estimation with implicit limit state functions in the time-invariant reliability analysis, the common Monte Carlo simulation based approach involves multiple trials for each parameter being varied, which will increase associated computational cost and the cost may become inevitably high especially when many random variables are involved. Another effective approach for this problem is featured as constructing the equivalent limit state function (usually called response surface) and performing the estimation in FORM/SORM. However, as the equivalent limit state function is polynomial in the traditional response surface method, it is not a good approximation especially for some highly non-linear limit state functions. To solve the above two problems, a new method, support vector regression based response surface method, is therefore presented in this paper. The support vector regression algorithm is employed to construct the equivalent limit state function and FORM/SORM is used in the parameter sensitivity estimation, and then two illustrative examples are given. It is shown that the computational cost of the sensitivity estimation can be greatly reduced and the accuracy can be retained, and results of the sensitivity estimation obtained by the proposed method are in satisfactory agreement with those computed by the conventional Monte Carlo methods.