Proceedings of the 10th IMA International Conference on Modelling in Industrial Maintenance and Reliability

A.A.H. Ahmadini and F.P.A. Coolen

https://doi.org/10.19124/ima.2018.001.01

Abstract

In this paper, we present a new imprecise statistical inference method for accelerated life testing data, where nonparametric predictive inferences at normal stress levels are integrated with a parametric Arrhenius-Weibull model. The method includes imprecision which provides robustness with regard to the model assumptions. The imprecision leads to observations at increased stress levels being transformed into interval-valued observations at the normal stress level, where the width of an interval is larger for observations from higher stress levels. Simulation studies are presented to investigate the performance of the proposed method.

J. Akrouche, M. Sallak, E. Chatelet, F. Abdallah and H. Hajj Chehade

https://doi.org/10.19124/ima.2018.001.02

Abstract

The system availability is defined as the probability that the system is operational at a given time. To compute the availability of a multi-states system (the system and its components could have multi-states), we have to sum over all the probabilities of the components working states, therefore these probability precise values are required. In some cases (rare event failures, new components, …), it isn’t possible to obtain the working probabilities precisely because of the lack of data. In this work, we propose to apply imprecise continuous Markov chain where the failure and repair rates are imprecise. Only few works were developed using this concept. The precise initial distributions are replaced by intervals, which represents the unknown initial probabilities and the unknown transition matrix. The interval constraint propagation method is exploited for the first time, in availability modeling, to compute the imprecise multi-states system availability. The probability interval bounds associated to real variables are contracted, without removing any value that may be consistent with the set of constraints. The proposed methodology is guaranteed, and different examples of complex systems with some properties (convergence, ergodicity, …) are studied. All the numerical examples and results will be discussed in the paper.

A.R. Alberti, C.A.V. Cavalcante, P.A. Scarf and A.L.O. Silva

https://doi.org/10.19124/ima.2018.001.03

Abstract

A hybrid inspection and replacement policy for a protection system is described by a mathematical model that incorporates a set of maintenance quality aspects. A three-state component failure model is assumed, with a defective state preceding failure. The quality of maintenance intervention is modelled by supposing that inspection may induce a defect and is subject to errors in the defective state judgement (false positives and false negatives). The quality of replacement is modelled by supposing that a component arises from a heterogeneous population, composed of weak and strong items. This paper presents a case study on isolation valves used in water distribution systems, that motivated the development of the model. We evaluate the impact of these quality aspects upon cost and production losses. A detailing of the context is presented, highlighting important aspects of the analysis, which should be observed when other studies are carried out in different contexts. We present the results in order to evaluate the impact of the quality aspects regarded, verify conditions that justify investment in higher quality maintenance, and obtain guidance on which aspects should be prioritized in the investment. Defect induction is found to be a key determinant of the cost-optimal policy.

A. Aljazea, M. Luo and S. Wu

https://doi.org/10.19124/ima.2018.001.04

Abstract

Warranty is offered by manufacturers as protection and promotional tools. It also gives customers a certain degree of insurance against product failures for a certain period. Although bringing those benefits, it involves various risks originating from various perspectives of the product lifetime cycle. To prepare risk mitigation plans is therefore needed, which is challenging due to the increasing complexity of the product designs and the long warranty period. Accordingly, the decision made to select the mitigation plan involves a high level of uncertainty. This paper develops a plan to mitigate warranty risk based on cumulative prospect theory, which helps warranty decision makers in selecting the optimal mitigation plan.

J. Bishop, M. Niblett and M. McCarthy

https://doi.org/10.19124/ima.2018.001.05

Abstract

A facility on the Sellafield site is being designed which will receive waste from several of the site’s high hazard facilities during their decommissioning.
Sellafield Ltd. uses a process called T-RAM (Throughput – Reliability, Availability and Maintainability) to develop a reasoned auditable argument that a new plant, and its systems and subsystems, will meet its performance targets. T-RAM activity is focused on areas of the plant that represent the most T-RAM risk as determined through a top-down proactive approach to risk identification. Identified T-RAM risk is mitigated through improved design supported by the development of effective operations, maintenance and asset care strategies using techniques including Failure Mode Effects and Criticality Analysis (FMECA); Reliability Centered Maintenance (RCM) and Reliability Block Diagrams (RBD).
In order to underpin the “Throughput” capability of the facility, the Operational Research (OR) Group has developed a simulation model of the facility which has been used to feedback the expected throughput based on current best knowledge of plant configuration, equipment cycle times, reliability and maintainability data. This is an iterative process, with high risk bottleneck areas identified by the simulation model becoming the focus for T-RAM progressive risk reduction activities. The project is an excellent example of the OR and T-RAM teams working together to influence facility design.

L. Breniere, L. Doyen and O. Gaudoin

https://doi.org/10.19124/ima.2018.001.06

Abstract

Let us consider repairable systems subject to failures and maintenances. Observations can come from only one system or from several identical and independent systems. The maintenance is supposed to be imperfect. This is an intermediate situation between the case where a maintenance renews the system, called As Good As New, and the case when the sole effect of maintenance is to get the system working again, called As Bad As Old. Imperfect maintenance models have a parameter that can be interpreted as the maintenance efficiency. Other parameters lie in the baseline hazard rate of the first failure time. All these parameters can be estimated through maximisation of the log-likelihood, which takes into account the single system or multisystem
nature of the data. The first aim of this paper is to compare the quality of parameter estimation in the single and multi-system cases. The second aim is to compare the quality of goodness-of-fit tests in this framework. Moreover, new goodness-of-fit tests are introduced in the multi-system case.

I.T. Castro, L. Landesa and A. Serna

https://doi.org/10.19124/ima.2018.001.07

The gamma process is a stochastic process extensively used to model the degradation of a system over the time. In this paper, gamma process is used in a different perspective: to model the harvested energy for an Energy Harvesting System (EHS). Energy harvesting refers to harnessing energy from the environment or other energy sources and converting it to electrical energy. Specifically, in this work, gamma process is used to model the harvested energy for a Radio Frequency Energy Harvesting System. A management policy of this EHS is described.

P.P.L. Dantas, T.F.O. Rodriguez and C.A.V. Cavalcante

https://doi.org/10.19124/ima.2018.001.08

Abstract

The maintenance became one of the primaries factors in guaranteeing the proper functioning of a productive system. Despite this, some assumptions assumed by maintenance planning are not fully complied with, such as the availability of spare parts whenever its need. Hence, the integrated models of inventory maintenance and management came out, used to seek the levels that best fit for the two policies. Integration is usually based on the criteria of total costs, but not always this factor is the only interest of the decision maker. Therefore, the present work aims at the construction of a multicriteria model to support an application of an integrated model developed in a previously work. New criteria have been introduced as choice dimensions and an outranking method was developed using PROMETHEE II to generate an order of values that reflects the interests of the decision maker.

A. Delmas, M. Sallak, W. Schon and L. Zhao

https://doi.org/10.19124/ima.2018.001.09

Abstract

Predictive maintenance allows the drawbacks of corrective and preventive maintenance to be overcome by estimating the Remaining Useful Life (RUL) of components. This value helps undertake required tasks at the right time: when the component is deteriorated but right before a failure occurs.
We propose an imprecise RUL estimation method with the use of Hidden Markov Models (HMM). HMMs have been used for prognosis in several studies. Here, we fit the training data by a polynomial and then we frame it with 2 other polynomials of the same degree. This results in 3 distinct trained HMMs: the first is used for the RUL estimation and the other 2 provide the lower and higher bounds. Finally, we propose a strategy that can be adopted when dealing with incomplete or imprecise data. This work has been applied to the IEEE 2008 PHM challenge which was a competition aiming at evaluating prognosis methods.

E. Jantunen, G. Di Orio, F. Larrinaga, M. Becker, M. Albano, and P. Malo

https://doi.org/10.19124/ima.2018.001.10

Abstract

The rapid development of new low-cost sensors of reasonable processing power have led to the introduction of Cyber-Physical Systems (CPS) to support maintenance, which in turn together with a scalable, two level data processing architecture has taken maintenance as such to a new level i.e. Maintenance 4.0. The potential of CPS to support maintenance is explained, and it is related to the requirements set upon the sensors, such as robustness, communication capabilities, intelligence, small size, etc. A new framework and architecture with support for Health management, Prognostics and Collaborative decision-making functionalities that takes the full advantage of the above introduced new technologies is described both theoretically and in the light of some industrial use case examples. A comparative between the approaches implemented in the use cases brings light into the maintenance possibilities to address in such a framework

R. Jiang and Z.Chen

https://doi.org/10.19124/ima.2018.001.11

Abstract

Traditionally, it is assumed that the lead time spare part demand follows the Poisson or normal distribution. It is well-known that the mean of Poisson distribution equals to its variance, which is called Poisson dispersion. Actual demand data scarcely meet the Poisson dispersion. To overcome this problem, several two-parameter variants (e.g., zero-inflated Poisson and hurdle shifted Poisson) of the Poisson distribution have been used for modelling irregular spare part demand data. In this paper, we propose two new two-parameter alternatives, which are transmuted and exponentiated Poisson distributions. The proposed distributions can be over- or under-Poisson dispersive, and hence provide better flexibility without losing the simplicity of the Poisson distribution too much. The appropriateness of the proposed models is illustrated through three real-world examples. Based on the idea that a fitted distribution should provide a good goodness-of-fit to the right tail of the empirical demand distribution so as to achieve good inventory performance, a weighted least square method is also proposed to estimate the model parameters. The appropriateness of the proposed approach is also illustrated.

Keywords. Spare parts demand, transmuted Poisson distribution, exponentiated Poisson distribution, weighted least square method

K. Kalyanam, M. Pachter and D. Casbeer

https://doi.org/10.19124/ima.2018.001.12

Abstract

We formulate a stochastic optimization problem, wherein finite resources are sequentially allocated to incoming tasks so as to maximize a cumulative reward. At each decision stage, the incoming task has known value. However, upon allocating a resource, a task is completed with probability less than one. Furthermore, the decision maker is informed about the task completion status via an error-prone feedback mechanism that is subject to both type I and type II classification errors. The decision maker may choose to reallocate a resource to the current task or move on to the next task in the sequence. We show that a resource is allocated if and only if the expected reward from a task exceeds a threshold. Furthermore, we establish a lower bound on the task completion probability under which the threshold is monotonic decreasing in the number of remaining resources.

G. Knight

https://doi.org/10.19124/ima.2018.001.13

Abstract

Weibull analysis is often seen as a complicated technique employed by reliability engineering enthusiasts and statistical analysis aficionados, with un-relatable features such as “beta parameters” and “characteristic life”. The array of information that is made available from a quick web search doesn’t do much to convince the casual browser of anything different. What does exist is a dearth of information or guidance on just how useful the Weibull distribution is, therefore no compelling case is made for the casually interested party to look further into the use of this as part of an analysis technique. Analysis using the Weibull distribution include determining an appropriate improvement activity or asset management tactic, assessing test data for product life or supporting investment decisions. This paper gives an overview of the Weibull distribution in plain language, explaining the terminology in relatable terms to the general population. Also covered is the way in which each input variable can be calculated or estimated. Applications for Weibull analysis are explored, with proposals of action based upon the results. The different ways in which the data can be presented is also shown, with key points to consider when proposing asset management activities.

J. Ma, M. Fouladirad and A. Grall

https://doi.org/10.19124/ima.2018.001.14

Abstract

As a consequence of high failure rate occurring to wind turbines (WTs), more and more asset owners begin to pay attention to WT’s maintenance planning. To implement an efficient condition-based maintenance policy, the deterioration modeling is of primary importance. The inherence character of wind makes WT’s operation full of uncertainties, hence, the pitch actuator movement is random and it can be modeled by a stochastic process. Meanwhile, the deterioration of the pitch control system related to the usage of the actuator is random. When the wind speed is higher than the rated wind speed (a design WT parameter, when wind speed is higher than the rated wind speed, the pitch system begin to operate) and the wind speed turbulence intensity (TI) is very important, the deterioration rate of the pitch control system increases significantly. For instance the pitch system has more failures in a windy season. Therefore, in order to propose an appropriate maintenance policy for pitch control system, it is more sensible to take into account the influence of wind speed in deterioration modelling.

In this paper, we focus on the deterioration of hydraulic pitch control system. After studying the factors influencing the deterioration, a stochastic process considering wind speed as covariates is used to model the deterioration. A maintenance policy based on an alarm threshold is considered.

J. McGinty, S. Kollias and G. Leontidis

https://doi.org/10.19124/ima.2018.001.15

Abstract

An investigation into approaches to model and predict the costs, risks and outcomes, relating to a common failure mode within a large population of remotely monitored engines will be presented. This investigation will cover the relevant aspects of lifecycle management, customer operating cycle, visibility of classified issues remotely using low sample rate remote monitoring systems and identification of the most appropriate repair regime. Unlike previously identified studies, the datasets used are larger comprising sensor readings from over a 120 gas turbine units (distributed across the world) which total 100 million observations taken across 117 parameters. These are cross referenced with relevant service events (preventative, corrective, fault investigation and observational reports) and service execution data (spares and manpower required) gathered over a period of up-to 11 years. We have now succeeded in identifying behaviours in a particular problem domain using techniques such as K-means, that indicate non-optimal decisions may have been made (with the benefit of hindsight). This indicates that the intended outcome of the overall research to produce of quantitative models of the different strategies and applying them to optimize for the best outcome both in terms of and customer and supplier objectives in a justifiable form is possible.

R. Meesit and J. Andrews

https://doi.org/10.19124/ima.2018.001.16

Abstract

Railway networks are prone to many different potential disruptive events such as technical failures (e.g. the failure of aging components), natural disasters (e.g. flooding) and intentional man-made disasters (e.g. trespass and suicide). Assessing the vulnerability of railway networks can help infrastructure managers to create the right preventive strategies to improve the robustness and the resilience of railway networks before the occurrence of disruptions. This study proposes a stochastic-vulnerability analysis model that enables the critical components of railway networks to be identified. The model is developed using a discrete event simulation technique. Its framework includes modules for assigning the disruption to the network components, predicting the network vulnerability, in terms of passenger delays and journey cancellations, and calculating the risk-based criticality of network components. Finally, an example application of the model is presented using a part of the East Midland railway network in UK.

A. Najem and F.P.A. Coolen

https://doi.org/10.19124/ima.2018.001.17

Abstract

One of the strategies that might be considered to enhance reliability and resilience of a system is swapping components when a component fails, so replacing it by another component from the system which is still functioning. This paper considers cost effective component swapping to increase system reliability. The cost is discussed in two scenarios, namely fixed cost and time dependent cost for system failure.

M. Naybour, R. Remenyte-Prescott and M .J. Boyd

https://doi.org/10.19124/ima.2018.001.18

Abstract

Studies of error rates in community pharmacies have reported error rates of between 0.014% and 3.3% per item dispensed. This suggests up to 36 million items per year may contain errors in England. In addition, literature shows that patient satisfaction with services is directly related to waiting times. There is a need for a method to model pharmacy efficiency balancing safety and waiting times, ensuring that the reliability of the dispensing process is not compromised. In this paper a Coloured Petri Net (CPN) approach is proposed for analysing reliability and efficiency of community pharmacy. A pharmacy team work to complete dispensing and non-dispensing tasks, where non-dispensing tasks require staff to be temporarily removed from the dispensing process. The proposed approach is useful to investigate what affects the error rates and long waiting times, and provides modelling-based evidence to decision makers, looking to optimise staffing levels and improve the reliability of dispensing.

H.S.H. Nguyen, P. Do, B. Iung and H.C. Vu

https://doi.org/10.19124/ima.2018.001.19

Abstract

We develop a joint optimization between maintenance planning based on dynamic grouping approach and maintenance team routing for a geographically dispersed production system (GDPS). First, the interactions between the maintenance planning and routing scheduling are analyzed and formulated. A joint  implementation of Local Search Genetic Algorithm (LSGA) and a new maintenance routing algorithm based on Branch and Bound (BAB) is then proposed to solve the joint optimization problem. The uses and advantages of the proposed joint optimization approach are illustrated through a numerical example of a GDPS consisting of fifteen components located in five different sites.

E. Pereira, B.M. Alkali and O. Niculita

https://doi.org/10.19124/ima.2018.001.20

Abstract

This paper focuses on maintenance management of critical equipment of a New Liquefied Natural Gas (LNG) plant. The challenges associated with the risk of failure affect the operational conditions of the plant equipment, and also the recommendations by the original equipment manufacturer (OEM) to maintain critical system is a difficult task. In this paper a case study of a new gas turbine design within an LNG plant is considered. The maintenance cost, operation and availability are some of the most important concerns to the gas turbine operators. Majority of the gas turbine maintenance are related to the replacement and repair of hot section static parts. A review of the operating and maintenance practices for heavy-duty gas turbines and others critical equipment, with emphasis placed on the types of inspections and factors that influence maintenance schedules is presented. The Reliability Centred Maintenance (RCM) modeling and criticality analysis approach is used to analyse historical failure and maintenance data. To compliment the accuracy of data information careful consultation with the operators is conducted and we test the goodness of fit of using a Weibull model for the analysis. A maintenance optimisation model is discussed and an attempt is made to model the failure pattern to determine and recommend cost effective maintenance management schedule for the gas turbine dry gas seal.

L. Ribeiro, P. Dantas and C.A.V. Cavalcante

https://doi.org/10.19124/ima.2018.001.21

Abstract

Some aspects of a system operation dynamics may prevent excellence in organizations performance. This is imperative for mission-oriented systems since all necessary interventions have to be undertaken during the stoppages between successive missions. Maintenance models that deal with it, however, do not take into account that aspects, such as the length of stoppages or constraints on the maintenance resources. These constraints limit the set of actions that can be carried out during each stoppage. Thus, it should be selected the maintenance actions that would best improve system performance, observing constraints. Traditionally, models that address this issue seek to optimize one of the performance indicators of a system. Thus, an original contribution of this paper is to optimize conflicting indicators through a multicriteria perspective by considering the preferences of the decision maker. Therefore, it has been used FITradeoff (Flexible and Interactive Tradeoff) method in order to asses a global performance value to each alternative. At last, the obtained plan is compared to the one obtained in a traditional way.

L. Ribeiro and C.A.V. Cavalcante

https://doi.org/10.19124/ima.2018.001.22

Abstract

Maintenance planning is a challenge for organizations. This is particularly evident for mission-oriented systems, since maintenance must to be performed within stoppages. In this sense, models indicate a set of actions to be performed in each stoppage by considering the operational dynamics of a system. We propose a model that address this issue in an innovative way, by considering a system working under different operational conditions – i.e. heterogeneous missions – and subject to a budget constraint. We used genetic algorithm (GA) in order to solve this problem have. Finally a case study illustrates the model applicability.

C. Ruiz, H. Liao and E.A. Pohl

https://doi.org/10.19124/ima.2018.001.23

Abstract

Reliability demonstration tests (RDTs) have been widely used in engineering design to verify if a product has met a certain reliability requirement. Such tests are usually conducted and analyzed based on binomial theory for the number of failures or analysis of failure times. Unlike these traditional methods, a degradation-based RDT method is proposed in this paper. Appropriate implementation of this method will speed up reliability demonstration, especially for highly reliable products. However, a big challenge is the measurement error that cannot be avoided in degradation data collection. To incorporate the impact of measurement errors in degradation-based RDT, a random effects stochastic process model explaining both the evolution of product degradation and measurement error is proposed. Under this model, a statistical inference method based an expectation-maximization algorithm is developed to estimate the model parameters. Moreover, the optimal design of degradation-based RDT is developed to minimize the total testing cost considering both the producer’s and consumer’s risks. A numerical example is presented to illustrate the use of the proposed RDT method in practice.

A.C.J. Santos and C.A.V. Cavalcante

https://doi.org/10.19124/ima.2018.001.24

Abstract

Delay-time concept has been used in a large number of applications for modelling important aspects of reality, such as the dynamics of failures, which in practice occurs in two stages for the most devices. Concisely, a defect arises in a component or system followed by a subsequent failure after an interval called delay time. The effort to repair a defect is related to the time from when it can be firstly observed until its discovery by an inspection. However, a small number of contributions deal with the cost related to the time in which the system undergoes in the defective state. In addition, a repair action on a system that undergoes in defective state for a short period of time can have different consequences than the same action after a long period of time. Two examples are: a pipeline leakage defect and the repair of a defect in a tooth. In both cases, the consequences vary depending on the time in defective state. Hence, this paper proposes a way to take the defect cost into consideration according to the time in which the system undergoes in this state.

C. Su and L. Cheng

https://doi.org/10.19124/ima.2018.001.25

Abstract

A two-dimensional (2D) availability-based warranty policy is investigated from the view of the manufacturer. Under the policy, the manufacturer guarantees a negotiated availability for the products during the warranty period. In order to satisfy the availability requirement with rational cost, 2D preventive maintenance (PM) strategy is carried out, which is scheduled every K units of age or L units of usage. This study is to meet products’ availability requirement with the lowest cost by optimizing 2D PM strategy. A numerical example is provided to verify the effectiveness of the proposed policy. The results show that compared with age-based or usage-based PM strategy, 2D PM strategy is superior in terms of warranty cost and operational availability.

A. Tripathi, C. Souprayen, A. Stanley and N. Warrilow

https://doi.org/10.19124/ima.2018.001.26

Abstract

At present there are two main approaches to modelling gas dispersion within the process safety risk assessment arena. Historically the Gaussian approach has dominated, however, with the improvement of computing power available in relatively low-cost platforms, Computational Fluid Dynamics (CFD) has begun to challenge that dominance. Both approaches rely on a mathematical description of transport with advection-diffusion equations. However, there are a number of simplifications that are needed for analytical Gaussian solutions to be derived which are not needed for CFD solutions. The Gaussian approach often applies simplified flows over flat terrain, whereas CFD accounts for flow velocities and turbulence and their effects on mixing and can be applied in more complex three-dimensional environments. The questions this paper asks are: Is CFD modelling more mathematically correct than the Gaussian approach? And, Does the emergence of CFD into the mainstream pose an opportunity or threat? To answer these questions this paper will explore the previously mentioned differences between approaches.

R. Wang, C. Lyu, S. Mei, C. Guo, J. Geng and Y. Xiao

https://doi.org/10.19124/ima.2018.001.27

Abstract

This paper aims at the process of augmented reality maintenance, and studies the voice interaction method to realize a more natural and efficient human-computer interaction between maintenance personnel and virtual objects. Study the application and functional requirements of voice interaction in augmented reality assisted maintenance by analyzing the current interaction status. Determine the information transmission between computer and maintenance personnel and establish a knowledge base for maintenance process. Thus, a suitable semantic-based model of voice interaction mechanism is designed.
In this paper, speech signal and text information are converted by speech recognition and synthesis technology. And the semantic understanding method based on keyword matching is used to analyze the order of maintenance personnel, so as to convey the feedback information to maintenance personnel or make the operating system perform corresponding functions. Finally complete the communication between the maintenance personnel and the virtual objects.

X. Wang, L. Li and M. Xie

https://doi.org/10.19124/ima.2018.001.28

Abstract

The optimization of preventive maintenance (PM) strategies for repairable items sold with warranty contracts has received much attention in the literature. However, the existing research implicitly assumes that maintenance actions within the warranty period are punctual. In practice, it is not uncommon that the actual maintenance instants deviate from the scheduled instants. In this paper, an unpunctual imperfect PM strategy, which allows customers to advance or postpone the scheduled PM actions in a tolerable range, is proposed for repairable items sold with a two-dimensional warranty. The objective of this work is to determine the optimal unpunctual PM strategies under the given warranty period so as to minimize the manufacturers total expected warranty servicing cost. It is shown that the unpunctual PM strategy contains its punctual counterpart as a special case and tends to result in slightly higher warranty servicing cost.

G. Willoughby, T. Ingram, T. Byrne, D. Smith and N. Rahman

https://doi.org/10.19124/ima.2018.001.29

Abstract

The intent of this paper is to illustrate how the current paradigm of reliability information held by a small selection of companies who sell the information could soon be outdated. We believe that Reliability data ownership could be transferred by a collaborative, decentralized approach, with data sourced from the communities that use the data stored in a blockchain. The advent of blockchain as a shared, immutable ledger, has the potential to be the technology that provides the platform for this. Such a ledger can provide a new way of thinking about reliability data potential: Original Equipment Manufacturers (OEM’s) and users contributing data in a transparent manner, clear visibility of underpinning data continually evolving in a live database, self-checking across the community, data beyond the base of the bathtub, quality certification, storage of source information such as Failure Modes Effects Criticality Analysis (FMECAs), direct linkage into the Industrial Internet of Things. This fundamental shift in approach could see a focus of practitioner held information, with trust held within the community. We will present some potential methodologies, and how we are proposing to establish collaborative working groups to establish a standard on this technology. The objective is to provide a platform for the international reliability community that allows for the development of a truly free, trustworthy, reliability resource.

K.J. Wilson and M. Farrow

https://doi.org/10.19124/ima.2018.001.30

Abstract

Frequently,manufacturers are required to demonstrate that productsmeet reliability targets. A typical way of doing this is to use reliability demonstration tests (RDTs), in which a number of products are put on test and the test is passed if a critical number or fewer fail. There are various methods for determining the sample size for such tests. Traditionally this was based on the size of a hypothesis test following the RDT. More recently, Bayesian approaches have been proposed based on the idea of risk criteria. However, these approaches do not lead to a single, optimal sample size and conflate the choice of sample size for the test and
the analysis to be undertaken once the test has been conducted. In this paper we offer an alternative approach to sample size determination based on the idea of assurance. This approach can overcome each of these issues with risk criteria. Assurance chooses the sample size to answer the question: “What is the probability that the RDT will result in a successful outcome?”We demonstrate the use of assurance for sample size calculations in binomial RDTs and discuss the specification of prior distributions for the design and analysis of the test.

Y. Xiao, C. Lv, J. Geng, C. Guo and R. Wang

https://doi.org/10.19124/ima.2018.001.31

Abstract

Maintainability，as an important quality characteristic of the product, always been neglected during the design process, which lead to many problems around products’ useage. The main purpose of the Maintenance centered matrix-based Universal quality characteristics analysis is, by determining the qualitative and quantitative relationship between the general quality features, to find the data flow, information flow between the common quality features and maintainability. Focusing on the maintenance process in a typical maintenance task model, the maintenance factors and general quality characteristics involved in the maintenance task model are analyzed, and the quantitative or qualitative correlation between the maintenance factors and the general quality characteristics is obtained. Finally, The maintenance matrix model integrated with maintenance-oriented, integrated maintenance elements, design specialties and design elements is available. By combining maintainability factors(observation, inspection, disassembly, replacement, installation, commissioning) with other related design discipline factors, correlating other design elements with maintainability elements, reaching a systematic trade-off centered on maintainability, which take maintenance of all elements into account, into the pre-design process.

X. Zhao, Z. Liang, A. Parlikad and M. Xie

https://doi.org/10.19124/ima.2018.001.32

Abstract

This paper presents a maintenance optimization framework for systems suffering from nonlinear continuous degradation. The inspection interval is dynamically determined by the historical system conditions. We model the degradation path as a nonlinear Wiener process with time-varying drift parameter. Techniques to predict remaining useful life (RUL) is utilized to optimize maintenance policy by minimizing the expected cost rate. The effect of imperfect maintenance is assumed to be random in the sense that the maintenance action reduces the systems degradation level by a random proportion described by a beta distribution. Two thresholds on the degradation are determined for the preventive imperfect maintenance and perfect replacement, respectively. We evaluate expected cost rate using Monte Carlo simulation. A dataset from the real-world example is used to provide the pilot parameters as input for the optimization maintenance policies. Afterward, numerical examples are presented to illustrate the proposed method.

Q. Zhou, D. Zhou, Y. Xie and W. Zhou

https://doi.org/10.19124/ima.2018.001.33

Abstract

To ensure products with high reliability and quality, maintainability design and verification is an integral part of design process. As a crucial factor in maintainability design, visibility evaluation is the most important part of maintainability design and verification. However, subjective and expert judgment in evaluation methods are the two major problems limiting the development and application of the visibility design. To solve these problems, this paper proposes a visibility automation analysis and evaluation model based on virtual maintenance. Firstly, two sub-evaluation indicators are designed for visual ability and visual obstruction. Secondly, in responds to the two indicators, two models are established based on visual cone and feature points respectively. Moreover, a comprehensive visual accessibility evaluation model based on line-of-sight interference detection and two models is developed. Finally, a case of an engine coupling fastening bolt visibility assessment is conducted and verifies the effectiveness of the proposed method.

C. Guo , C. Lyu , J. Yan, R. Wang and Y. Xiao

https://doi.org/10.19124/ima.2018.001.34

Abstract
As an important quality characteristic, maintainability is the ability of a product to be repaired efficiently and economically. As the structure design largely determines the level of product maintainability, so maintainability factors should be put more consideration in the structure design. The traditional structure design put less attention on maintainability design, resulting in higher product maintenance cost. In order to enhance the relationship degree between maintainability design and structure design to achieve integrated design, this paper presents a correlative model of maintainability and structure design. First, maintainability factors and structure design factors are classified and compared. Second, according to graph theory, the correlative model is established in the form of graph and formula. Finally a case of the lubrication system is used to verify the scientific of this model.