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Copyright of Maintenance & Reliability / Eksploatacja i Niezawodność is the property of Polish Scientific & Technical Society Consumables, Polish Maintenance Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Maintenance & Reliability / Eksploatacja i Niezawodność is the property of Polish Scientific & Technical Society Consumables, Polish Maintenance Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We will discuss the joint optimization of the jobs sequence as well as inspection policy for a single system expected to process n jobs with different processing times. The system has a two-stage failures process, i.e., first a defect arises in the system, and if the defect is not detected, the system eventually fails. The interrupted job due to failure should be restarted after corrective replacement of the system. The possibility of inspecting the system before starting a job is considered to detect a potential defect. We develop two models to find the optimal policy based on either total expected makespan, or total expected cost. In the cost optimization model, we assume a common due date for all jobs and incur a penalty cost per unit time that the makespan exceeds the due date. We develop a recursive formula to obtain the expected makespan, and the number of failures and preventive replacements and present the application of our proposed models to a system, which is supposed to process four jobs. We compare the results of the direct calculation (recursive formula) with a Monte Carlo simulation model, and discuss how changing the models’ parameters can impact the optimal policy. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a repairable system whose failures follow a non-homogenous Poisson process with the power law intensity function. The system is subject to corrective and multiple types of preventive maintenance. A corrective maintenance has a minimal effect on the system; however, a preventive maintenance may reduce the system's age. We assume the effects of different preventive maintenance on the system are not identical and derive the likelihood function to estimate the parameters of the failure process as well as the effects of preventive maintenance. Moreover, we derive the conditional reliability and the expected number of failures between two consecutive preventive maintenance types. The proposed methods are applied to a real case study of four trucks used in a mining site in Canada. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the present paper, a system with components subject to soft and hard failures is considered. It is assumed that hard failures are revealed and fixed immediately and present an additional opportunity for inspection (opportunistic inspection), but soft failures are hidden and only corrected at periodic inspections. The objective is to find the optimal maintenance policy for all components and the optimal periodic inspection for the entire system. Two models are considered in this context. In the first model, hard-type and soft-type components are subject to minimal repair or corrective replacement, and soft-type components undergo opportunistic inspections. In the second model, in addition to the assumptions of the first model, hard-type components may be preventively replaced at periodic inspections. In our models, we base the maintenance decision for the soft-type components on the optimal number of minimal repairs until replacement, and for the hard-type components – on the optimal age before replacement. A recursive equation is provided for deriving the required expected values. Hidden failures preclude us from expressing the terms of the objective function in closed form. For this reason, the optimal periodic inspection interval for the system minimizing its total expected life cycle cost is found for both models using simulation. [ABSTRACT FROM AUTHOR]

• A methodology measuring the impacts of maintenance on sustainability is developed. • The methodology utilizes available data and can be easily applied in practice. • The methodology is implemented in a sustainability dashboard (user interface). • A case study from the thermoplastic industry is used for validation purposes. Maintenance practices can play a significant role in pursuing sustainable development in several industries. However, a general method to monitor and assess maintenance interventions' impacts on environmental, social, and economic sustainability does not exist yet. To calculate a sustainability score, in this paper, we propose a methodology to quantify and measure the impacts of maintenance practices on sustainability performance. The proposed methodology uses available data and aggregates correlation algorithms with the best-worst method (BWM), a multi-criteria decision-making method. The proposed methodology can be applied to evaluate sustainability performance when a database for a set of sustainability-related indicators concerning maintenance activities is available. The purpose here is not to have a complicated methodology, but a simple one that can be easily applied in practice. For this purpose, the proposed methodology is implemented in a sustainability dashboard that can be used as a simple sustainability score calculator. A case study is developed to demonstrate the practical implications of the proposed sustainability dashboard. The findings demonstrate the efficiency of the methodology and suggest that it could work as a valuable tool for practitioners and academics to understand the impacts of maintenance practices on an asset's overall sustainability score (OSS). [ABSTRACT FROM AUTHOR]

We investigate the maintenance and inventory policy for a k-out-of-n system where the components' failures are hidden and followa non-homogeneous Poisson process. Two types of inspections are performed to find failed components: planned periodic inspections and unplanned opportunistic inspections. The latter are performed at system failure timeswhen n-k+1 components are simultaneously down. In all cases, the failed components are eitherminimally repaired or replaced with spare parts from the inventory. The inventory is replenished either periodically or when the systemfails. The periodic orders have a random lead-time, but there is no lead-time for emergency orders, as these are placed at system failure times. The key objective is to develop a method to solve the joint maintenance and inventory problem for systems with a large number of components, long planning horizon, and large inventory. We construct a simulation model to jointly optimize the periodic inspection interval, the periodic reorder interval, and periodic and emergency order-up-to levels. Due to the large search space, it is infeasible to try all possible combinations of decision variables in a reasonable amount of time. Thus, the simulation model is integrated with a heuristic search algorithm to obtain the optimal solution. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this paper is to develop a model, which optimizes jointly the inspection frequency and the inventory level for a k-out-of-n system with repairable components whose failures are hidden. Scope: The system is periodically inspected to detect failed components, and the components are either minimally repaired or replaced with spares from the inventory. The system fails between periodic inspections if n-k+1 components are down; in that case, all failed components are inspected and rectified if possible. Otherwise, the failed components are rectified at periodic inspections. An emergency spare is ordered at a system failure, if the inventory is empty and all failed components require replacement. Methodology: Using analytical approach to find the optimal solution is computationally intensive and not practical; a simulation model is developed to solve the problem. Results: The proposed model harmonizes the maintenance and inventory policies and finds the joint optimal solution which results in a minimum total cost. Conclusion: The joint optimization model results in a lower cost compared to separate maintenance and inventory optimization models. Novelty: Few joint models for k-out-of-n systems exist, and none of them investigate repairable components whose failures are hidden and follow a non-homogeneous Poisson process. [ABSTRACT FROM AUTHOR]

• Identical parallel machines scheduling problem with random failures is studied • We consider a two-stage machines' failures consist of defect and failure • The defect and failure arrival rates for the machines have time-dependent functions • We jointly optimize the production schedules and inspection plans • An exact depth-first B&B algorithm is proposed to solve the proposed model Utilizing available information on random machine breakdowns is highly effective in making joint production and inspection decisions. The current paper studies the joint planning of production schedule and inspection of identical parallel machines to process n jobs with different processing times. Each machine's failure is modeled using the two-stage delay time concept. The first stage is a defect arrival which does not cause a machine stoppage, but if it is not removed, may cause the machine failure. A machine's lifetime is divided into two stages in such a failure process: defect arrival and failure arrival stages. The defects and failures of the identical parallel machines follow non-linear and time-dependent distributions. Inspection activities are intended to identify potential defects in the machines and subsequently removing them prior to eventual failures. The objective is to establish the best jobs' schedule of the machines and the best sequence of inspection decisions that minimize the expected makespan. For this problem, unequal defect and failure arrival rates for the machines impose some complications to the expected makespan calculation. Therefore, obtaining an integrated scheduling and inspection plan requires considering all possible combinations of the jobs order and inspections on the machines that further increase the complexity of the algorithm implementation. We propose a branch and bound (B&B) algorithm that restricts the search time and space and demonstrates the effectiveness of integrating inspection and maintenance operations with the jobs sequence to minimize the expected makespan. We propose an upper bound and develop a lower bound to assign the maximum number of jobs on the empty capacity of the machines. We analyze the impact of the model's parameters on the results of the B&B algorithm and validate the algorithm's performance by comparing the results with the analytical solutions for small-sized problems for which analytical solution exists. [ABSTRACT FROM AUTHOR]

Abstract: Trend analysis is a common statistical method used to investigate the operation and changes of a repairable system over time. This method takes historical failure data of a system or a group of similar systems and determines whether the recurrent failures exhibit an increasing or decreasing trend. Most trend analysis methods proposed in the literature assume that the failure times are known, so the failure data is statistically complete; however, in many situations, such as hidden failures, failure times are subject to censoring. In this paper we assume that the failure process of a group of similar independent repairable units follows a non-homogenous Poisson process with a power law intensity function. Moreover, the failure data are subject to left, interval and right censoring. The paper proposes using the likelihood ratio test to check for trends in the failure data. It uses the Expectation–Maximization (EM) algorithm to find the parameters, which maximize the data likelihood in the case of null and alternative hypotheses. A recursive procedure is used to solve the main technical problem of calculating the expected values in the Expectation step. The proposed method is applied to a hospital''s maintenance data for trend analysis of the components of a general infusion pump. [Copyright &y& Elsevier]

This paper proposes a model to find an optimal periodic inspection interval over finite and infinite time horizons for a multi-component repairable system subject to hidden failures. The components' failures can only be rectified at periodic inspections, when a failed component is either minimally repaired, or replaced with some age dependent probabilities. We calculate the excepted cost with delayed replacement or minimal repair of a component. Recursive procedures are developed to calculate probabilities of failures in every interval, expected number of minimal repairs, and expected downtimes for optimization over finite and infinite time horizons. Numerical examples of the calculation of the optimal inspection frequencies are given. The data used in the examples is adapted from a hospital's maintenance data for a general infusion pump. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a model to find the optimal periodic inspection interval on a finite time horizon for a complex repairable system. In general, it may be assumed that components of the system are subject to soft or hard failures, with minimal repairs. Hard failures are either self-announcing or the system stops when they take place and they are fixed instantaneously. Soft failures are unrevealed and can be detected only at scheduled inspections but they do not stop the system from functioning. In this paper we consider a simple policy where soft failures are detected and fixed only at planned inspections, but not at moments of hard failures. One version of the model takes into account the elapsed times from soft failures to their detection. The other version of the model considers a threshold for the total number of soft failures. A combined model is also proposed to incorporate both threshold and elapsed times. A recursive procedure is developed to calculate probabilities of failures in every interval, and expected downtimes. Numerical examples of calculation of optimal inspection frequencies are given. The data used in the examples are adapted from a hospital''s maintenance data for a general infusion pump. [Copyright &y& Elsevier]

In this paper, a new maintenance policy for k -out-of- n :F systems is proposed, which includes age replacement and minor repairs as preventive maintenance actions. After replacing a multi-component system, some components are still usable and can be sold as second-hand products. The price of these components depends on their original lifetime and the replacement time of the system. On the other hand, managers and engineers would like to reduce the imposed costs on the system while increasing system efficiency. With this in mind, long-run average cost is considered as the objective function. In addition, the optimal parameters of the proposed policy are obtained under two constraints: a safety constraint and a constraint for enhancing the relative mean operating time. By considering the first constraint, the system engineer is confident with a desired probability that no unplanned replacement will happen to the system in a pre-specified interval. The second constraint ensures the enhancement of the mean operating time of the system by applying the proposed policy. Moreover, the classical age replacement policy has been achieved as a special case of our results. To study the effect of the model's parameters on the optimal solutions, extensive numerical computations are performed. Finally, a real data set of the tensile strength of cables is used to demonstrate the applicability of the proposed model. [ABSTRACT FROM AUTHOR]