Modelling condition monitoring intervals: A hybrid of simulation and analytical approaches

This paper reports on a study of modelling condition monitoring intervals. The model is formulated based upon two important concepts. One is the failure delay time concept, which is used to divide the failure process of the item into two periods, namely a normal working period followed by a failure delay time period from a defect being first identified to the actual failure. The other is the conditional residual time concept, which assumes that the residual time also depends on the history condition information obtained. Stochastic filtering theory is used to predict the residual time distribution given all monitored information obtained to date over the failure delay time period. The solution procedure is carried out in two stages. We first propose a static model that is used to determine a fixed condition monitoring interval over the item life. Once the monitored information indicates a possible abnormality of the item concerned, that is the start of the failure delay time, a dynamic approach is employed to determine the next monitoring time at the current monitoring point given that the item is not scheduled for a preventive replacement before that time. This implies that the dynamic model overrides the static model over the failure delay time since more frequent monitoring might be needed to keep the item in close attention before an appropriate replacement is made prior to failure. Two key problems are addressed in the paper. The first is which criterion function we should use in determining the monitoring check interval, and the second is the optimization process for both models, which can be solved neither analytically nor numerically since they depend on two unknown quantities, namely, the available condition information and a decision of the time to replace the item over the failure delay time. For the first problem, we propose five appealingly good criterion functions, and test them using simulations to see which one performs best. The second problem was solved using a hybrid of simulation and analytical solution procedures. We finally present a numerical example to demonstrate the modelling methodology.     

Condition-based maintenance optimization considering improving prediction accuracy

Condition-based maintenance (CBM) aims to reduce maintenance cost and improve equipment reliability by effectively utilizing condition monitoring and prediction information. It is observed that the prediction accuracy often improves with the increase of the age of the component. In this research, we develop a method to quantify the remaining life prediction uncertainty considering the prediction accuracy improvement, and an effective CBM optimization approach to optimize the maintenance schedule. Any type of prognostics methods can be used, including data-driven methods, model-based methods and integrated methods, as long as the prediction method can produce the predicted failure time distribution at any given inspection point. Furthermore, we develop a numerical method to accurately and efficiently evaluate the cost of the CBM policy. The proposed approach is demonstrated using vibration monitoring data collected from pump bearings in the field as well as simulated degradation data. The proposed policy is compared with two benchmark maintenance policies and is found to be more effective.     

Optimum Redundancy with Equipment that Operates and Idles

Despite their widespread exclusion from the literature on reliability and replacement, many items of equipment are subject to failures to idle as well as failures to operate. For such equipment a new optimisation problem arises at the stage of systems design in terms of the optimal use of redundancy to maximise expected systems life. The models hitherto used as the basis for the solution of this optimization problem ignore the dependence that should exist for the solutions upon the operating and idling requirements to which the system is to be exposed. In this paper a simple model for equipment subject to such opposite failure modes is constructed which takes explicit account of the proportion of time the equipment is in use. The implications of this new model for the selection of an optimum redundancy configuration are illustrated for the case where four identical items of equipment are available.     

Availability analysis of crank-case manufacturing in a two-wheeler automobile industry

The paper describes the availability of crank-case manufacturing system in an automobile industry. The units discussed here fail either directly from normal working state or indirectly through partial failure state. The machines are subjected to both preventive and corrective maintenance. Failure and repair times of the units are independent. The problem is formulated using probability consideration and supplementary variable technique. The system of equations governing the working of system consists of ordinary as well as partial differential equations. Lagrange method and Runge–Kutta method is used to solve partial differential equation and ordinary differential equation respectively. The study reveals that successful program of preventive and routine maintenance will reduce equipment failures, extend the life of the equipment, and increase the system availability to considerable margin.     

An asset residual life prediction model based on expert judgments

An appropriate and accurate residual life prediction for an asset is essential for cost effective and timely maintenance planning and scheduling. The paper reports the use of expert judgments as the additional information to predict a regularly monitored asset’s residual life. The expert judgment is made on the basis of measured condition monitoring parameters, and is treated as a random variable, which may be described by a probability distribution due to the uncertainty involved. Since most expert judgments are in the form of a set of integer numbers, we can either directly use a discrete distribution or use a continuous distribution after some transformation. A key concept used in this paper is condition residual life where the residual life at the point of checking is conditional on, among others, the past expert judgments made on the same asset to date. Stochastic filtering theory is used to predict the residual life given available expert judgments. Artificial, simulated and real data are used for validating and testing the model developed.     

A Condition Based Maintenance Model with Exponential Failures and Fixed Inspection Intervals

This paper discusses a condition based maintenance model with exponential failures, and fixed inspection intervals. A condition of the equipment, such as vibration, is monitored at equidistant time intervals. If the variable indicating the condition is above a threshold an instantaneous maintenance action is performed and the monitored condition takes on its initial value. The equipment can fail only once within an inspection interval. The probability of failure is exponential and the failure rate is dependent on the condition. The cost to be minimized is the long-run average cost of maintenance actions and failures. We study the optimal solution to this problem obtained via dynamic programming and compare it to an approximate steady state solution based on renewal theory.     

Realization of multi-input/multi-output switched linear systems from Markov parameters

This paper presents a four-stage algorithm for the realization of multi-input/multi-output (MIMO) switched linear systems (SLSs) from Markov parameters. In the first stage, a linear time-varying (LTV) realization that is topologically equivalent to the true SLS is derived from the Markov parameters assuming that the discrete states have a common MacMillan degree and a mild condition on their dwell times holds. In the second stage, stationary point set of a Hankel matrix with fixed dimensions built from the Markov parameters is examined. Splitting of this set into disjoint intervals and complements reveals linear time-invariant dynamics prevailing on these intervals. Clustering over a feature space permits recovery of the discrete states up to similarity transformations which is complete if a unimodality assumption holds and the discrete states satisfy a residence requirement. In the third stage, the switching sequence is estimated by three schemes. The first scheme is non-iterative in time. The second scheme is based on matching the estimated and the true Markov parameters of the SLS system over segments. The third scheme works also on the same principle, but it is a discrete optimization/hypothesis testing algorithm. The three schemes operate on different dwell time and model structure requirements, but the dwell time requirements are weaker than that needed to recover the discrete states. In the fourth stage, the discrete state estimates are brought to a common basis by a novel basis transformation which is necessary for predicting outputs to prescribed inputs. Robustness of the four-stage algorithm to amplitude bounded noise is studied and it is shown that small perturbations may only produce small deviations in the estimates vanishing as noise amplitude diminishes. Time complexities of the stages are also studied. A numerical example illustrates the derived results.     

On the Optimality of an Equal Life Policy for Equipment Subject to Technological Improvement

The equipment replacement model with equal replacement intervals has been studied by a number of authors. When new equipment is identical to old equipment, it has been proved that an optimum policy involves replacement at equal intervals (the equal life assumption). However, when equipment is subject to technological change, no such proof exists.Since almost all equipment is subject to technological improvement over time, and since the assumption of equal lives vastly simplifies the analysis, such a proof is highly desirable. In the absence of a proof, authors have attempted to justify the equal life assumption as a good approximation to a correct solution. For example, Smith argues that the process of discounting makes future replacements less important and with gradual technological improvement an equal life policy is close to optimum. This paper shows that under the definition of technological change employed by most authors working on equipment replacement problems, it is unnecessary to assume that an equal life policy is optimum, rather the optimality of such a policy can be proven.     

高端装备制造企业组织创新演化机制研究——基于混沌理论

以演化视角分析高端装备制造企业组织创新类型和创新演化过程,并基于种群竞争模型,构建企业组织创新演化模型,通过分析模型平衡点稳定性及其演化趋势,发现当创新演化初始点出现在进入鞍点轨线附近时,其微小变化将导致最终演化状态的巨大差别。应用Lyapunov指数法判定高端装备制造企业组织创新演化的混沌性,运用创新演化模型模拟企业组织创新混沌演化过程并分析创新混沌演化规律。研究结果表明:①渐进式创新和激进式创新之间较强的竞争效应是企业组织创新演化进入混沌的一般条件;②鞍点是企业组织创新混沌演化的转折点,在进入鞍点轨线的影响域中,创新演化轨道的断裂位置和跃迁方向敏感依赖于初始条件;③环境变化对激进式创新的压力会增强创新演化的混沌性。     

A multi-objective genetic algorithm for robust flight scheduling using simulation

Traditional methods of developing flight schedules generally do not take into consideration disruptions that may arise during actual operations. Potential irregularities in airline operations such as equipment failure are not adequately considered during the planning stage of a flight schedule. As such, flight schedules cannot be met as planned and their performance is compromised, which may eventually lead to huge losses in revenue for airlines. In this paper, we seek to improve the robustness of a flight schedule by re-timing its departure times. The problem is modeled as a multi-objective optimization problem, and a multi-objective genetic algorithm (MOGA) is developed to solve the problem. To evaluate flight schedules, SIMAIR 2.0, a simulation model which simulates airline operations under operational irregularities, has been employed. The simulation results indicate that we are able to develop schedules with better operation costs and on-time performance through the application of MOGA.     

An approximate algorithm for prognostic modelling using condition monitoring information

Established condition based maintenance modelling techniques can be computationally expensive. In this paper we propose an approximate methodology using extended Kalman-filtering and condition monitoring information to recursively establish a conditional probability density function for the residual life of a component. The conditional density is then used in the construction of a maintenance/replacement decision model. The advantages of the methodology, when compared with alternative approaches, are the direct use of the often multi-dimensional condition monitoring data and the on-line automation opportunity provided by the computational efficiency of the model that potentially enables the simultaneous condition monitoring and associated inference for a large number of components and monitored variables. The methodology is applied to a vibration monitoring scenario and compared with alternative models using the case data.     

Mathematical analysis of a population model with an age–weight structured two-stage life history: asymptotic behavior of solutions

In this paper, we propose a model for the dynamics of a physiologically structured population of individuals whose life cycle is divided into two stages: the first stage is structured by the weight, while the second one is structured by the age, the exit from the first stage occurring when a threshold weight is attained. The model originates in a complex one dealing with a fish population and covers a large class of situations encompassing two-stage life histories with a different structuring variable for each state, one of its key features being that the maturation process is determined in terms of a weight threshold to be reached by individuals in the first stage. Mathematically, the model is based on the classical Lotka–MacKendrick linear model, which is reduced to a delayed renewal equation including a constant delay that can be viewed as the time spent by individuals in the first stage to reach the weight threshold. The influence of the growth rate and the maturation threshold on the long-term behavior of solutions is analyzed using Laplace transform methods.     

Allocation of a relevation in redundancy problems

The relevation can be considered as a replacement or repair policy in reliability, in which, when a unit fails, the unit is restored to a working condition just previous to the failure, in the sense that the age of the unit is not changed but the failure rate changes. It can be also considered as a generalization of the minimal repair policy and the load‐sharing model. In this paper, we consider the problem of where to allocate a relevation in a system to increase the reliability of the system and the particular cases of load‐sharing and minimal repair policies.     

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At present, in degradation tests, product failure is generally defined as degradation of performance below or above a specified critical value (that is called single point degradation). Although this definition is simple and practical, it is not reasonable enough and degradation failure of the product can not be completely described. In this paper, a single point degradation model is improved, and an interval degeneration model is proposed. We discuss the interval degradation model when the degradation path is liner, and obtain life distribution functions for all kinds of linear interval degradation model. Numerical integration and Monte Carlo simulation methods are used to analyze and compare the life distribution of the interval degradation model and the single point degradation model, and the relationship between the interval degradation and the single point degradation is revealed. Finally, an real data example is analysis to show that interval egradation is more reasonable and effective in practice.     

A model to predict the residual life of rolling element bearings given monitored condition information to date

In condition monitoring practice, one of the primary concernsof maintenance managers is how long the item monitored can survivegiven condition information obtained to date. This relates tothe concept of the condition residual time where the survivaltime is not only dependent upon the age of the item monitored,but also upon the condition information obtained. Once sucha probability density function of the condition residual timeis available, a consequencial decision model can be readilyestablished to recommend a ‘best’ maintenance policybased upon all information available to date. This paper reportson a study using the monitored vibration signals to predictthe residual life of a set of rolling element bearings on thebasis of a chosen distribution. A set of complete life dataof six identical bearings along with the history of their monitoredvibration signals is available to us. The data were obtainedfrom a laboratory fatigue experiment which was conducted underan identical condition. We use stochastic filtering to predictthe residual life distribution given the monitored conditionmonitoring history to date. As the life data are available,we can compare them with the prediction. The predicted resultsare satisfactory and provide a basis for further studies. Itshould be pointed out that although the model itself is developedfor the bearings concerned, it can be generalized to modellinggeneral condition-based maintenance decison making providedsimilar conditions are met.     

Estimation under failure-censored step-stress life test for the generalized exponential distribution parameters

Accelerated life testing of materials is used to collect failure data quickly when the lifetime of a specimen under use condition is too long. This article considers estimates of the generalized exponential distribution parameters under step-stress partially accelerated life testing with Type-II censoring. The maximum likelihood approach is applied to derive point and asymptotic confidence interval estimations of the model parameters. The performance of the estimators is evaluated numerically for different parameter values and different sample sizes via their mean square error. Also, the average confidence intervals lengths and the associated coverage probabilities are obtained. A simulation study is conducted for illustration.     

Models and algorithms for a staff scheduling problem

We present mathematical models and solution algorithms for a family of staff scheduling problems arising in real life applications. In these problems, the daily assignments to be performed are given and the durations (in days) of the working and rest periods for each employee in the planning horizon are specified in advance, whereas the sequence in which these working and rest periods occur, as well as the daily assignment for each working period, have to be determined. The main objective is the minimization of the number of employees needed to perform all daily assignments in the horizon. We decompose the problem into two steps: the definition of the sequence of working and rest periods (called pattern) for each employee, and the definition of the daily assignment to be performed in each working period by each employee. The first step is formulated as a covering problem for which we present alternative ILP models and exact enumerative algorithms based on these models. Practical experience shows that the best approach is based on the model in which variables are associated with feasible patterns and generated either by dynamic programming or by solving another ILP. The second step is stated as a feasibility problem solved heuristically through a sequence of transportation problems. Although in general this procedure may not find a solution (even if one exists), we present sufficient conditions under which our approach is guaranteed to succeed. We also propose an iterative heuristic algorithm to handle the case in which no feasible solution is found in the second step. We present computational results on real life instances associated with an emergency call center. The proposed approach is able to determine the optimal solution of instances involving up to several hundred employees and a working period of up to 6 months. Mathematics Subject Classification (2000): 90B70, 90C10, 90C27, 90C39, 90C57, 90C59     

高原驻训航材有寿件需求预测研究

高原驻训航材保障中,有寿件重要度较高,部队必须携带足够的备件数量来满足任务需要.根据高原驻训条件下航材有寿件需求特点,将有寿件的需求分为到寿更换需求和随机故障消耗产生的需求两部分,分别用数学公式和仿真计算方法进行预测,再将两者结果相加为总需求量.通过实例分析验证,采用工程测算方法可以避免逐一分析器材寿命的繁杂,而单独考虑随机故障消耗可以进一步保证有寿件备件不短缺率在90%以上.该预测,该模型可以为航材有寿件到寿情况分析以及有寿件的携行方案制定提供参考.     

被检测系统的可靠性和最优检测策略

研究被检测系统的一个模型,假定系统有4种运行状态(正常工作、异常工作、正常故障和异常故障)．系统故障时不需检测,系统工作时必须经过检测才能知道它是正常还是异常．系统开始工作后,每隔一段随机时间对它检测一次,直到系统故障或检测出系统处于异常状态为止．利用概率分析和随机模型的密度演化方法,导出了系统的一些新的可靠性指标和最优检测策略．     

The effect of testing equipment shift on optimal decisions in a repetitive testing process

Repetitive testing process is commonly used in the final testing stage of semiconductor manufacturing to ensure high outgoing product quality and to reduce testing errors. The decision on testing lot size and the number of testing repetitions ultimately determines the effectiveness of the testing process. Setting the retest rule is often difficult in practice due to uncertainties in the incoming product quality and testing equipment condition. In this paper, we study a repetitive testing process where the testing equipment may shift randomly to an inferior state. We develop a cost model that helps us to make optimal decisions on retesting rule. Through numerical analysis, we provide practical insights about the effects of testing equipment shift rate, testing errors, and different costs such as cost of testing and cost of rejecting conforming products on the optimal decision and the system performance. We find that significant penalty may result if the potential testing equipment shift is ignored.