Repair Durations Research Articles

Simulation and factor analysis for post-earthquake recovery of densely populated urban residential communities in China

Post-earthquake recovery simulation is crucial for resilient community planning, and different interconnected factors are required to be considered holistically. To identify the key infrastructural characteristics affecting the recovery of densely populated urban residential communities (URCs) in China, a comprehensive methodology for resilience assessment and analysis is established on a systematic integration of multiple analysis tools (e.g. population-based functionality indicators, post-earthquake recovery simulations, and infrastructural dependence analyses). The methodology can consider the dependence among residential buildings, supporting buildings, and utility networks, as well as the relationships between their functionalities and resident outmigration; it also includes infrastructural repair sequences to allow flexible repair plans to be simulated. A case study is employed to conduct a factor analysis to clarify the impacts of three important infrastructural characteristics. Results show that improvements on the seismic performance of residential buildings facilitate recovery more significantly than utility networks, and the use of redundant utility pipelines can hardly impact the recovery of URCs. In long-term community recoveries, utility networks play more important role due to the cascading effects arising from the extension of repair durations. The proposed methodology can promote the understanding of community recovery, and the results demonstrate the effectiveness of identifying key factors.

Resilience-based Recovery Scheduling of Transportation Network in Mixed Traffic Environment: A Deep-Ensemble-Assisted Active Learning Approach

Devising effective post-hazard recovery strategies is critical in enhancing the resilience of transportation networks (TNs). However, existing work does not consider the multiclass users’ travel behavior in network functionality quantification and the metaheuristic solution procedures often suffer from extensive computational burden due to the exploration need in large solution space and the expensive functionality quantification. This study develops a bilevel decision-making framework for the resilience-based recovery scheduling of the TN in a mixed traffic environment with connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). The lower level quantifies the TN's functionality over time considering different travel behavior of CAV and HDV users arisen from their different levels of information perception. The upper level presents a novel deep-ensemble-assisted active learning approach to balance optimization performance and computational cost. This framework can help decision makers better quantify the TN's functionality to support effective recovery scheduling of TN with different mixed traffic scenarios ranging from HDV-only to future CAV-dominant traffic. The optimization approach bears the potential to be extended to solving general large-scale network recovery scheduling problems effectively and efficiently. The proposed methodology is demonstrated using a real-world traffic network in Southern California under earthquake considering deterministic and stochastic repair durations.

A stochastic detailed scheduling model for periodic maintenance of military rotorcraft

PurposeThe purpose of this paper is to develop a stochastic detailed schedule for a preventive/scheduled/periodic maintenance program of a military aircraft, specifically a rotorcraft or helicopter.Design/methodology/approachThe new model, entitled the military “periodic aviation maintenance stochastic schedule” (PAM-SS), develops a stochastic detailed schedule for a PUMA SA 330SM helicopter for the 50-h periodic inspection, using cyclic operation network (CYCLONE) and Monte Carlo simulation (MCS) techniques. The PAM-SS model identifies the different periodic inspection tasks of the maintenance schedule, allocates the resources required for each task, evaluates a stochastic duration of each inspection task, evaluates the probability of occurrence for each breakdown or repair, develops the CYCLONE model of the stochastic schedule and simulates the model using MCS.FindingsThe 50-h maintenance stochastic duration follows a normal probability distribution and has a mean value of 323 min and a standard deviation of 23.7 min. Also, the stochastic maintenance schedule lies between 299 and 306 min for a 99 per cent confidence level. Furthermore, except the pilot and the electrical team (approximately 90 per cent idle), all other teams are around 40 per cent idle. A sensitivity analysis is also performed and yielded that the PAM-SS model is not sensitive to the number of technicians in each team; however, it is highly sensitive to the probability of occurrence of the breakdowns/repairs.Practical implicationsThe PAM-SS model is specifically developed for military rotorcrafts, to manage the different resources involved in the detailed planning and scheduling of the periodic/scheduled maintenance, mainly the 50-h inspection. It evaluates the resources utilization (idleness and queue), the stochastic maintenance duration and identifies backlogs and bottlenecks.Originality/valueThe PAM-SS tackles military aircraft planning and scheduling in a stochastic methodology, considering uncertainties in all inspection task durations and breakdown or repair durations. The PAM-SS, although developed for rotorcrafts can be further developed for any other type of military aircraft or any other scheduled maintenance program interval.

Integrated Maintenance-Quality strategies with reworking activities

Abstract: This paper deals with integrated models joining Non Quality effects and preventive maintenance (PM) policies. We consider a manufacturing system composed of a single machine subject to random failures and producing conforming and non conforming products. In order to decrease the random failure rate of our manufacturing system, we apply a preventive maintenance policy with minimal repair and non-negligible durations of maintenance actions. Our objective is to determine an optimal integrated maintenance-quality strategy taking into consideration the decreased quality of output products and the economic impact of reworking activities. In fact, we developed two different strategies: For the first strategy, we propose reworking activities for all non conforming products in order to improve their quality condition and to sell all batches at the best price P max . The aim of this strategy is to found the optimal number of batches produced and reworked N * before each preventive maintenance action, maximizing the total net profit per time unit PT 1 . For the second strategy, the same problem is studied but for a finite horizon. Two mathematical models are developed in order to find the optimal value of the decision variable N * for both strategies. Numerical examples are presented in order to illustrate proposed models and a sensitivity study is developed to evaluate the influence of the variation of models’ parameters.

Reliability of Erasure Coded Storage Systems: A Combinatorial-Geometric Approach

We consider the probability of data loss, or equivalently, the reliability function for an erasure coded distributed data storage system under worst case conditions. Data loss in an erasure coded system depends on probability distributions for the disk repair duration and the disk failure duration. In previous works, the data loss probability of such systems has been studied under the assumption of exponentially distributed disk failure and disk repair durations, using well-known analytic methods from the theory of Markov processes. These methods lead to an estimate of the integral of the reliability function. Here, we address the problem of directly calculating the data loss probability for general repair and failure duration distributions. A closed limiting form is developed for the probability of data loss and it is shown that the probability of the event that a repair duration exceeds a failure duration is sufficient for characterizing the data loss probability. For the case of constant repair duration, we develop an expression for the conditional data loss probability given the number of failures experienced by a each node in a given time window. We do so by developing a geometric approach that relies on the computation of volumes of a family of polytopes that are related to the code. An exact calculation is provided and an upper bound on the data loss probability is obtained by posing the problem as a set avoidance problem. Theoretical calculations are compared to simulation results.

Outage data analysis of utility power transformers based on outage reports during 2002–2009

Statistical analysis of failures and forced outages of power transformers constitute an important basis for asset management of these transformers. Results of the statistical analysis can be used, for example, to enhance utility reliability, influence transformer design and technology, and improve maintenance and condition monitoring practices. In addition, various methods for transformer reliability evaluation require that the expected values of component outage rates, outage durations, and repair durations be known. In this paper, outage data are obtained from the Egyptian Electricity Transmission Company (EETC). This work presents outage data analysis over eight years, from 2002 to 2009, for 1922 (average number) transformers in voltage populations ranging from 33kV to 500kV and MVA rating from 5MVA to 500MVA. Forced outages due to correct and false action of transformer’s protection systems are carefully considered. Outage data analysis is conducted according to two basic phases. In the first phase, failure and repair analysis of transformers is performed while in the second phase impact of transformer outages on customers is assessed. Percentage average number of failures (%AANF) and annual average repair time (AART) per transformer are used to represent the failure and repair data of power transformers. Two indicators are used to represent the impact of transformer outages on customer interruptions. These indicators are the annual average interrupted MW (AAIMW) and annual average customer-interruption duration (AACID). A summary of the main outcomes of the work presented in this paper is provided in the conclusions’ section; however, it is worthy to be mentioned here that the fire-fighting systems are responsible for the highest number of false trips in all voltage subpopulations except the 220kV subpopulation where the dominant cause of false trips is the busbar protection. Therefore, it is recommended to improve the maintenance and design of this protection equipment to reduce the failure rate of power transformers.

Joint production control and product quality decision making in a failure prone multiple-product manufacturing system

This paper considers joint production control and product quality specifications decision making in unreliable multiple-product manufacturing system. This is with the knowledge that an optimum compromise should guide the decision making process. In fact, tight process specifications will generally lead to products with good quality and higher market values, but at the same time associated with a higher rate of non-conforming parts rejection leading to higher non quality costs and lower plant productivity. Moreover, in unreliable manufacturing context the decision maker should adopt an adequate production policy to hedge against future capacity shortages caused by machine failures in order to meet customer demand. This paper intends to extend previous findings to tackle this problem and study the overall decision making process aiming to guide the production and quality specification decisions in multiple-product context. The overall optimal decision policy is defined here as one that maximises the long term average per unit time profit of a combined measure of quality and quantity dependent sales revenue, minus inventory and backlog costs, in the presence of random plant failures and random repair durations.

Integrated product specifications and productivity decision making in unreliable manufacturing systems

This paper considers joint production control and product specifications decision making in a failure prone manufacturing system. This is with the knowledge that tight process specifications, while leading to a product of more reliable quality and higher market value, are at the same time associated with higher levels of non-conforming parts, a higher rate of parts rejection and thus a lowering of overall plant productivity. The decision making is further complicated by the lack of reliability of the production process, which imposes that an adequate, also to be designed, level of inventory of finished parts be maintained. The overall optimal decision policy is defined here as one that maximizes the long term average per unit time profit of a combined measure of quality and quantity dependent sales revenue, minus inventory and backlog costs, in the presence of random plant failures and random repair durations. Policy optimization is achieved via a revisited model of the Bielecki–Kumar theory for Markovian machines and a simulation and experimental design based methodology for the more general cases.

The Investigation of Coal Washing Plant Equipment by Using Multivariate Statistical Analysis

Coal washing plant equipment, regardless of its type, is frequently broken down in accordance with use and age. Maintenance and repair durations for broken equipment account for a large ratio among the total working period. In this study, the repair data for coal sieves used in a fine coal washing plant has been analyzed using multivariate statistical techniques, including cluster analysis, principal component analysis, and factor analysis. Through the results from the multivariate statistical analyses, it is determined that there are two groups of coal sieves according to their repair durations. These analyses are believed to assist the management of coal washing plants and determine priorities to improve plant issues.

Correspondence analysis of repair data: a case study for electric cable shovels

In mining operation, effective maintenance scheduling is very important because of its effect on the performance of equipment and production costs. Classifying equipment on the basis of repair durations is considered one of the essential works to schedule maintenance activities effectively. In this study, repair data of electric cable shovels used in the Western Coal Company, Turkey, has been analyzed using correspondence analysis to classify shovels in terms of repair durations. Correspondence analysis, particularly helpful in analysing cross-tabular data in the form of numerical frequencies, has provided a graphical display that permitted more rapid interpretation and understanding of the repair data. The results indicated that there are five groups of shovels according to their repair duration. Especially, shovels numbered 2, 3, 7, 10 and 11 required a repair duration of<1 h and maintained relatively good service condition when compared with others. Thus, priority might be given to repair them in maintenance job scheduling even if there is another failed shovel waiting to be serviced. This type of information will help mine managers to increase the number of available shovels in operation.

Availability, reliability and downtime of systems with repairable components

Closed-form expressions are derived for the steady-state availability, mean rate of failure, mean duration of downtime and lower bound reliability of a general system with randomly and independently failing repairable components. Component failures are assumed to be homogeneous Poisson events in time and repair durations are assumed to be exponentially distributed. The results are expressed in terms of the mean rates of failure and mean durations of repair of the individual components. Closed-form expressions are also derived for the rates of change of the various probabilistic system performance measures with respect to the mean rate of failure and the mean duration of repair of each component. These expressions provide a convenient framework for identifying important components within the system and for decision-making aimed at upgrading the system availability or reliability, or reducing the mean duration of system downtime. Example applications to an electrical substation system demonstrate the use of the formulas developed in the paper.

Comparing of system reliabilities of repairable coherent systems in a changing environment

In this paper multicomponent reliability systems are considered where component failure rate and repair completion rate depend on the environment state and, ages and current repair durations of the other components. this is a generalization of the model of Shaked M. and Shanthikumar J.G.[3]. Sufficient conditions on the set of the rates which imply stochastic ordering between first failure times of two such systems are found. Sufficient conditions on rates which imply that the first failure time of such a system is new better than used(NBU) are given. Some results of [3] may be regarded as the special cases of our assertions.

On the First Failure Time of Dependent Multicomponent Reliability Systems

In this paper multicomponent reliability systems are considered, where component failure and repair completion rates depend on the state, ages and current repair durations of the other components. This is a generalization of a model of Ross (Ross, S. M. 1984. A model in which component failure rates depend on working set. Naval Res. Logist. Quart. 31 297–300.). Sufficient conditions on the sets of rates which imply stochastic ordering between first failure times of two such systems are found. Sufficient conditions on the rates which imply that the first failure time of such a system is new better than used (NBU) are given. Some results of Barlow and Proschan (Barlow, R. E., Proschan, F. 1976. Theory of maintained systems: Distribution of time to first system failure. Math. Oper. Res. 1 32–42.), Chiang and Niu (Chiang, D. T., Niu, S. C. 1980. On the distribution of time to first system failure. J. Appl. Probab. 17 481–489.), and Ross (Ross, S. M. 1976. On the time to first failure in multicomponent exponential reliability systems. Stochastic Process. Appl. 4 167–173.) are obtained as special cases. A counterexample to an apparently stronger result of Miller (Miller, D. R. 1979. Almost sure comparison of renewal processes and poisson processes, with application to reliability theory. Math. Oper. Res. 4 406–413.) is also given. Further results and a discussion are included.

Determining sample size when searching for rare items : Richard W. Madsen and James E. Holstein. IEEE Trans. Reliab.R-31 (5), 451 (1982)

In this paper multicomponent reliability systems are considered where component failure rate and repair completion rate depend on the environment state and, ages and current repair durations of the other components. this is a generalization of the model of Shaked M. and Shanthikumar J.G.[3]. Sufficient conditions on the set of the rates which imply stochastic ordering between first failure times of two such systems are found. Sufficient conditions on rates which imply that the first failure time of such a system is new better than used(NBU) are given. Some results of [3] may be regarded as the special cases of our assertions.

Readiness Models for Random and Scheduled Demand Systems

The control of the operational status of aerospace systems is of paramount importance to both our military and civilian aerospace programs. To establish and maintain the operational capability of an aerospace system to be ready at any demand time (random or scheduled), it is necessary to determine and evaluate its status in order to select and execute maintenance actions which will, in turn, improve the operational capability of the system. Consequently, readiness testing and replacement-repair actions are necessary, even though test severity, errors, frequency, duration and coverage as well as repair durations and uncertainties all influence the probability that the system will be ready for use when demanded. To relate the effects of these realistic factors to system readiness, a conceptual framework has been formulated which provides an apparently endless series or hierarchy of status control models. Progressively more complicated models have been developed which include the effects of imperfect maintenance, man-in-the-loop, tolerance limit tradeoffs, and checkout equipment designs. In general, these models have been and will continue to be important for structuring the evaluation and predictive effort regarding present systems as well as for providing better standards and parametric tradeoffs for the preliminary design of new aerospace systems.