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The purpose of this paper is the simultaneous determination of optimal replacement threshold and inspection scheme for a system within condition-based maintenance (CBM) framework.

A proportional hazards model (PHM) is used for risk of failure and a Markovian process to model the system covariates. Total expected long-run cost (including replacement, inspection and downtime costs) is formulated in terms of replacement threshold and inspection scheme. Through an iterative procedure, for all different values of replacement thresholds, their associated optimal inspection scheme is determined using an effective search algorithm. By evaluating the corresponding costs, the optimal replacement threshold and its associated optimal inspection scheme are, then, identified.

The mathematical formulation, that takes into account all different costs, required for the simultaneous determination of optimal replacement threshold and optimal inspection scheme for an item subjected to CBM using PHM is provided. The proposed approach is compared against classical age policy and one state-of-the-art policy through a numerical example. The results show that the proposed approach outperforms other comparing policies.

In practical situations where CBM is implemented, inspections and downtime often incur cost. Under such circumstances, findings of this paper can be utilized for the determination of optimal replacement threshold and optimal inspection scheme so that the CBM cost is minimized.

In most of the reported researches, it is often assumed that inspections have no cost and/or that the time for replacements (either preventive or at failure) is negligible. In the contrary, in this paper the author takes all cost factors including inspection costs, replacement time(s) and their associated downtime costs into account in the simultaneous determination of optimal replacement threshold and optimal inspection scheme.

The purpose of this paper is to determine optimal replacement threshold and optimal inspection interval for an item subjected to condition-based maintenance (CBM). The primarily assumption is that the item's failure replacement cost depends on the item's degradation state at which failure occurs and/or the time the item fails. The cost of inspection is also taken into account.

The control limit replacement policy framework, already reported by some research referred to in this paper, is first extended to include the non-decreasing failure replacement cost assumption. Then, for alternative inspection intervals, replacement thresholds together with their associated total cost including the inspection cost are computed. By comparing the total costs, the optimal inspection interval and its corresponding optimal replacement threshold are simultaneously identified.

The mathematical formulation required for the determination of optimal replacement threshold and optimal inspection interval for an item subjected to CBM under the assumption of non-decreasing failure cost is provided.

In some practical situations where CBM is implemented, the failure replacement cost may depend on the time the failure happens and/or may depend on the system's degradation state. In addition, inspections often incur cost. Under such circumstances, findings of this paper can be utilized for the determination of optimal replacement threshold and optimal inspection interval for the underlying system.

Using the approach proposed in this paper, one could obtain the optimal replacement threshold and the optimal inspection interval for a system subjected to CBM.

The objective of this paper is to demonstrate basic economic principles underlying the use of inspection systems and to derive a basis for comparing inspection costs with the alternative costs of quality improvement.

For this purpose, the impact of final, rectifying and sequential inspection on unit production costs is assessed using a production process model with uniform defect propensity in the single process steps. Based on this model, an objective function with the objective of minimizing unit production costs including inspection costs is formulated and a genetic algorithm method used to optimize it.

Two distinct patterns of optimal inspection allocation could be detected for changing defect rate, processed volume, fixed inspection costs and variable input costs. These allocation patterns highlight the basic economic relations of an inspection approach and verify the assumption that sequential inspection schemes are cost‐optimal. However, the benefits of quality improvement are found to be superior to any inspection scheme for a majority of cases.

The findings are derived from a theoretical examination. Further limitations stem from the cost‐centred approach which only covers the internal component of an economic assessment of quality. However, the economic perspective advanced in this paper can in future be subjected to empirical testing and be elaborated by subsequent research.

Although built on simplifying assumptions, the process model can provide important insights into basic economic relations and demonstrate that inspection is an inferior way of dealing with quality problems. It can thereby help to promote a better economic understanding of quality.

This paper provides an economic assessment of an important aspect of quality management, which has so far not been advanced.

The continued improvement of additive manufacturing (AM) processing has led to increased part complexity and scale. Processes such as electron beam directed energy deposition (DED) are able to produce metal AM parts several meters in scale. These structures pose a challenge for current inspection techniques because of their large size and thickness. Typically, X-ray computed tomography is used to inspect AM components, but low source energies and small inspection volumes restrict the size of components that can be inspected. This paper aims to develop digital radiography (DR) as a method for inspecting multi-meter-sized AM components and a tool that optimizes the DR inspection process.

This tool, SMART DR, provides optimal orientations and the probability of detection for flaw sizes of interest. This information enables design changes to be made prior to manufacturing that improve the inspectabitity of the component and areas of interest.

Validation of SMART DR was performed using a 40-mm-thick stainless-steel blade produced by laser-based DED. An optimal orientation was automatically determined to allow radiographic inspection of a thickness of 40 mm with a 70% probability of detecting 0.5 mm diameter flaws. Radiography of the blade using the optimal orientation defined by SMART DR resulted in 0.5-mm diameter pores being detected and indicated good agreement between SMART DR’s predictions and the physical results.

This paper addresses the need for non-destructive inspection techniques specifically developed for AM components.

This paper aims to address the effect of inspection intervals on cost function in condition‐based maintenance (CBM) and show how selecting an appropriate inspection scheme may reduce the cost associated to a CBM program.

In CBM, replacement policy is often defined as a threshold for replacement or leaving an item in operation until next inspection, depending on monitoring information. The control limit replacement policy framework, already reported by some research referred to in this paper, is utilized to determine the optimal replacement threshold. Having released the assumption that the inspections are performed at fixed and constant intervals, an iterative procedure is proposed to evaluate alternative inspection schemes and their associated total average cost of replacements and inspections.

The paper proposes an approach in which preventive and failure replacement costs as well as inspection cost are taken into account to determine the optimal replacement policy and an age‐based inspection scheme such that the total average cost of replacements and inspections is minimized.

In many practical situations where CBM is implemented, e.g. manufacturing processes, inspections require labor, specific test devices, and sometimes suspension of the operations. Thus, when inspection cost is considerable, by applying the proposed approach, one can obtain an inspection scheme that reduces the cost.

Using the approach proposed in the paper, a cost‐effective age‐based inspection scheme for a system under CBM is determined.

This article considers Inverse Gaussian distribution as the basic lifetime model for the test units. The unknown model parameters are estimated using the method of moments, the method of maximum likelihood and Bayesian methods. As part of maximum likelihood analysis, this article employs an expectation-maximization algorithm to simplify numerical computation. Subsequently, Bayesian estimates are obtained using the Metropolis–Hastings algorithm. This article then presents the design of optimal censoring schemes using a design criterion that deals with the precision of a particular system lifetime quantile. The optimal censoring schemes are obtained after taking into account budget constraints.

This article first presents classical and Bayesian statistical inference for Progressive Type-I Interval censored data. Subsequently, this article considers the design of optimal Progressive Type-I Interval censoring schemes after incorporating budget constraints.

A real dataset is analyzed to demonstrate the methods developed in this article. The adequacy of the lifetime model is ensured using a simulation-based goodness-of-fit test. Furthermore, the performance of various estimators is studied using a detailed simulation experiment. It is observed that the maximum likelihood estimator relatively outperforms the method of moment estimator. Furthermore, the posterior median fares better among Bayesian estimators even in the absence of any subjective information. Furthermore, it is observed that the budget constraints have real implications on the optimal design of censoring schemes.

The proposed methodology may be used for analyzing any Progressive Type-I Interval Censored data for any lifetime model. The methodology adopted to obtain the optimal censoring schemes may be particularly useful for reliability engineers in real-life applications.

Develops a framework which provides a step towards better planning of production, scheming inspection and preventive maintenance. Studies the effects of an imperfect production process on the optimal production cycle time. The system is assumed to deteriorate during the production process and produce some proportion of defective items. Extends to the cases where the proportion of defective items and the cost of process restoration are not constant. Provides a comparative study of two monitoring policies where the preventive maintenance setting is used and not used in the deteriorating production process. These models are directly relevant to the management of the quality and reliability of the production process. When scheming inspection is adopted, it is shown that the optimal inspection intervals are equally spaced in the imperfect production process under the different policies, respectively. Provides a numerical example to illustrate the derivation of the optimal production cycle time in the models.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.