A probabilistic computational framework for bridge network optimal maintenance scheduling

This paper presents a probabilistic computational framework for the Pareto optimization of the preventive maintenance applications to bridges of a highway transportation network. The bridge characteristics are represented by their uncertain reliability index profiles. The in/out of service states of the bridges are simulated taking into account their correlation structure. Multi-objective Genetic Algorithms have been chosen as numerical tool for the solution of the optimization problem. The design variables of the optimization are the preventive maintenance schedules of all the bridges of the network. The two conflicting objectives are the minimization of the total present maintenance cost and the maximization of the network performance indicator. The final result is the Pareto front of optimal solutions among which the managers should chose, depending on engineering and economical factors. A numerical example illustrates the application of the proposed approach.     

Problem identification in maintenance modelling: a case study

This paper is concerned with a problem identification and problem focus process in maintenance modelling. It endeavours to describe the process of moving from vague problem understanding towards more specific problem formulation and problem focus in the pursuit of practical decision making. This process was conducted using several analytical tools that complemented each other such as regression analyses, snapshot modelling and delay time modelling. As in many case studies related to maintenance modelling, this study also makes use of the experience of experts. It can be seen from the paper that subjective data estimates can prove to be a useful input for modelling. The analysis shows how simple modelling of maintenance problems can provide useful insights and better understanding of the problem in hand.     

Availability and cost-centered preventive maintenance scheduling of continuous operating series systems using multi-objective genetic algorithm: A case study

This article presents a multi-objective (maximization of availability and minimization of maintenance cost) preventive maintenance (PM) scheduling model for a continuous operating series system (COSS) which do not provide an off-working period for PM. The objective functions are optimized by using a Multi-Objective Genetic Algorithm (MOGA). The effectiveness of the model is demonstrated through a coal-fired boiler-tube. The case study shows that the model can improve the availability along with profound reduction of the maintenance cost, i.e., increases the profit of the plant.     

A model for preventive maintenance planning by genetic algorithms based in cost and reliability

This work has two important goals. The first one is to present a novel methodology for preventive maintenance policy evaluation based upon a cost-reliability model, which allows the use of flexible intervals between maintenance interventions. Such innovative features represents an advantage over the traditional methodologies as it allows a continuous fitting of the schedules in order to better deal with the components failure rates. The second goal is to automatically optimize the preventive maintenance policies, considering the proposed methodology for systems evaluation.Due to the great amount of parameters to be analyzed and their strong and non-linear interdependencies, the search for the optimum combination of these parameters is a very hard task when dealing with optimizations schedules. For these reasons, genetic algorithms (GA) may be an appropriate optimization technique to be used. The GA will search for the optimum maintenance policy considering several relevant features such as: (i) the probability of needing a repair (corrective maintenance), (ii) the cost of such repair, (iii) typical outage times, (iv) preventive maintenance costs, (v) the impact of the maintenance in the systems reliability as a whole, (vi) probability of imperfect maintenance, etc. In order to evaluate the proposed methodology, the High Pressure Injection System (HPIS) of a typical 4-loop PWR was used as a case study. The results obtained by this methodology outline its good performance, allowing specific analysis on the weighting factors of the objective function.     

Failure analysis of power transformer for effective maintenance planning in electric utilities

In this paper, power transformer failures are analyzed and their root causes are systematically investigated in Tamil Nadu Transmission Corporation Limited (TANTRANSCO)/Tamil Nadu Generation and Distribution Corporation Limited (TANGEDCO) electric utilities, based on 196 failure cases from the year 2009–2013. Failure analysis is conducted in two phases. Initially, voltage level, geographical zone and power transformer components based failure analysis are performed through statistical analysis. Secondly, the most significant factors that cause power transformer failures are identified by using root cause analysis (RCA). Finally, current maintenance practice is reviewed and an effective maintenance planning has been proposed for implementation in order to prevent these failures and to maintain the power transformers in good operating condition during their life cycle. This study provides a practical guidance to help maintenance personnel for the best utilization of the power transformer in electric utilities.     

Analyzing maintenance strategies by agent-based simulations: A feasibility study

Thoroughly planned and implemented maintenance strategies save time and cost. However, the integration of maintenance work into reliability analysis is difficult as common modeling techniques are often not applicable due to state explosion which calls for restrictive model assumptions and oversimplification. From authors’ point of view, agent-based modeling (ABM) of technical and organizational systems is a promising approach to overcome such problems. But since ABM is not well established in reliability analysis its feasibility in this area still has to be demonstrated. For this purpose ABM is compared with Markov chains, namely by analyzing the reliability of a maintained n-unit system with dependent repair events, applying both modeling approaches. Although ABM and Markov chains lead to the same numerical results, the former points out the potentiality of an improved system state handling. This is demonstrated by extending the ABM with operators as additional “agents” featuring their location (x;y) availability (0;1) and different maintenance strategies. This extension highlights the capability of ABM to analyze complex emergent system behavior and allows a systematic refinement and optimization of the maintenance strategies.     

Optimal maintenance strategy for Technology‐enhanced classrooms: a case study

The purpose of the study was to collect data and evaluate an age‐based bulb replacement policy for projectors used in Technology‐enhanced classrooms in a university campus.The Office of Information Technology (OIT) had instituted a policy to replace bulbs in these projectors after either failure or 1800h of operation without failure. However, databases maintained by OIT included the time to bulb failure, the average number of times the bulbs were turned on and off per week, and the number of thermal events. Modeling the failure data with a Weibull proportional hazards model revealed that probability of bulb failure was a function of the number of hours a bulb was used and the frequency at which it was turned on and off. Combining this information with a cost model led to the recommendation for an improved condition‐based replacement policy. Copyright © 2009 John Wiley & Sons, Ltd.     

A portfolio approach to maintenance: a case study of a residential estate

The choice between repairing and replacing a defective piece of equipment is an economic decision that is faced by all maintenance managers, including housing estate managers. Such decisions need to be made within the limits and constraints set by maintenance expenditure budgets and by manpower availability. Our particular problem is concerned with the development of a maintenance policy for a residential estate of the Hong Kong Housing Authority. Our approach is to treat the numerous housing systems as a portfolio and to exploit flexibilities in performing or delaying the repair/replacement of these systems. The cost of a repair/replacement plan for the portfolio is formulated as an integer programme and genetic algorithms (GAs) are employed to generate optimal and sub‐optimal solution plans. The novel features of the approach are the model developed and use of GAs in this particular optimization context. The results and discussion of the case study will help practitioners to better understand the difficulties involved in collecting relevant cost data and in formulating repair/replacement plans for a group of buildings. Copyright © 2007 John Wiley & Sons, Ltd.     

Models for maintenance optimization: a study for repairable systems and finite time periods

The problem of selecting a suitable maintenance policy for repairable systems and for a finite time period is presented. Since the late seventies, examples of models assessing corrective and preventive maintenance policies over an equipment life cycle exist in the literature. However, there are not too many contributions regarding real implementation of these models in the industry, considering realistic timeframes and for repairable systems. Modeling this problem requires normally the representation of different corrective and/or preventive actions that could take place at different moments, driving the equipment to different states with different hazard rates.An approach to pattern the system under finite periods of time has been the utilization of semi-Markovian probabilistic models, allowing later a maintenance policy optimization using dynamic programming. These models are very flexible to represent a given system, but they are also complex and therefore very difficult to handle when the number of the system possible states increases.This paper explores the trade-off between flexibility and complexity of these models, and presents a comparison in terms of model data requirements versus potential benefits obtained with the model.     

Accounting for maintenance in a computer memory reliability prediction model

This paper reviews the benefit of periodic maintenance for improving the reliability of computer memory protected by error detection and correction (EDAC), and presents a new memory reliability model that accounts for such maintenance. Mean time to failure (MTTF) has been the traditional figure of merit for assessing the benefit of memory maintenance. This paper proposes failure probability calculated at maintenance intervals as a more meaningful figure of merit for periodically maintained systems and presents examples using the new model.     

A case study of socio-cultural and technical factors in automobile design: Discourses between designers and potential users on a new electric vehicle in Africa

Technological artifacts emerge and develop within social contexts, inextricably interconnecting technology and society. This article addresses this interconnection empirically, analyzing the implementation of an electric vehicle designed and built by students of automotive engineering at the Technical University of Munich (TUM) in Germany. The students created this vehicle to address problems of mobility for rural populations in Africa, aiming to provide local residents with better access to necessities of life such as healthcare, education, and transportation. The article examines African perceptions of and responses to the vehicle, as well as specific suggestions for future improvement. It discusses not only African reactions to the vehicle and its potential social impact on African societies, but also the concepts and ideas of the designers behind this automotive construction. It also acknowledges the ways in which the engineers based their work on assumptions about African needs rather than on substantial ethnographic research, which diminished their ability to succeed.     

A quantification algorithm for a repairable system in the GO methodology

The GO methodology is an effective method of system reliability analysis. It has been applied to non-repairable systems. This paper discusses the application of the GO method to a repairable system which is described by a Markov model and presents the quantification algorithm of the steady characteristics of the repairable system. The calculation formulas of the ordinary operators and the logical gates are derived and the steady reliability parameters of the system such as average operation probability and average failure frequency can be directly computed by the GO method. The result of an example shows that the algorithm is correct. The algorithm will be useful for the safety analysis of most engineering repairable systems.     

Mobile phone use among motorcyclists and electric bike riders: A case study of Hanoi,Vietnam

Motorcyclist injuries and fatalities are a major concern of many developing countries. In Vietnam, motorcycles are involved in more than 70% of all road traffic crashes. This paper aims to explore the prevalence and factors associated with mobile phone use among motorcyclists and electric bike riders, using a case study of Hanoi, Vietnam. A cross-sectional observation survey was undertaken at 12 sites, in which each site was surveyed during a two-hour peak period from 16:30 to 18:30 for two weekdays and one weekend day. A total of 26,360 riders were observed, consisting of 24,759 motorcyclists and 1601 electric bike riders. The overall prevalence of mobile phone use while riding was 8.4% (95% CI: 8.06–8.74%) with calling having higher prevalence than screen operation: 4.64% (95% CI: 4.39–4.90%) vs. 3.76% (95% CI: 3.52–3.99%) respectively. Moreover, the prevalence of mobile phone use was higher among motorcyclists than electric bike riders: 8.66% (95%CI: 8.30–9.01%) vs. 4.43% (95% CI: 3.40–5.47%) respectively. Logistic regression analyses revealed that mobile phone use while riding was associated with vehicle type, age, gender, riding alone, weather, day of week, proximity to city centre, number of lanes, separate car lanes, red traffic light duration, and police presence. Combining greater enforcement of existing legislations with extensive education and publicity programs is recommended to reduce potential deaths and injuries related to the use of mobile phones while riding.     

A risk-based optimisation for pavement preventative maintenance with probabilistic LCCA: a Chinese case

Compared to major structural repair or even replacement, preventative preservation of in-service pavements has been more popular in engineering practices, but recently, pavement preventative maintenance (PPM) has become more complex in China as the competition for pavement preservation funds has grown and the need to justify decisions has increased. Therefore, the life cycle cost analysis (LCCA) has increasingly attracted attention from transportation agencies. However, most of previous studies were conducted deterministically or only focused on a single factor, while PPM is apparently affected by many potential sources of uncertainty. The risk-based analysis to investigate potential risks and combined effects of multiple factors is a necessary component of PPM. This paper aims at presenting a risk-based PPM with the probabilistic LCCA for a Chinese highway case. Major analysis variables of different range are examined to probe risks of different scenarios, investigate combined effects of multiple variables and identify an optimal preservation strategy.     

Development of a post-flood road maintenance strategy: case study Queensland,Australia

Currently, no road authority takes into account flooding in road deterioration (RD) models; as a result, post-flood rehabilitation treatments may be sub-optimal. This paper proposes a new approach to the development of a post-flood maintenance strategy. The recently developed roughness and rutting-based RD models with flooding, by the current authors, are used as input to predict pavement deterioration after a flood (i.e. assuming a flood in year 1). The HDM-4 model has been used to get the post-flood maintenance strategy with constrained and unconstrained budget, where post-flood rehabilitation starts from year 2. The road groups in state road network of Queensland, Australia, are used as the case study. The unconstrained budget solution aims to keep the network in an excellent condition at a cost of $49.7bn with the possible strongest treatments. The constrained budget strategy uses agency cost and pavement performance as constraints in optimisation and provides a reasonable solution. This strategy requires about $26.1bn in life cycle, which is close to the main road authority of Queensland’s post-flood rehabilitation programme. The paper discusses two other strategies on maximise economic benefits and budget optimisation. It is expected that a road authority would properly investigate its flood-damaged roads before implementation. The paper shows pavement performances with the post-flood strategy. The need for a RD model to predict deterioration after a flood and for post-flood treatment selection is also highlighted.     

A mixed integer programming model for optimal ATC tower height and location: a case study for Singapore Changi Airport's third runway extension

ABSTRACT

With continued growth in air traffic, airports worldwide are expanding their runway infrastructure. This leads to the problem of determining an appropriate location and height for an Air Traffic Control (ATC) tower that can provide the right vantage point for coordinating runway and taxiway movements. The challenge involves finding the right location and optimal height that can satisfy the visibility and obstruction constraints for a complex airport-airside environment with multiple runways and civil infrastructure under different weather conditions. This article formulates the ATC tower location and height problem as a Mixed-Integer-Programming (MIP) model while considering the visibility and obstruction constraints. Singapore Changi Airport's proposed third runway extension is used as a case study to determine the set of location and height of ATC Tower using the proposed approach. A visual analytic test is conducted in an ATC tower simulator for different tower locations and heights under varying visibility conditions.     

Probabilistic physics-of-failure models for component reliabilities using Monte Carlo simulation and Weibull analysis: a parametric study

In reliability engineering, component failures are generally classified in one of three ways: (1) early life failures; (2) failures having random onset times; and (3) late life or ‘wear out’ failures. When the time-distribution of failures of a population of components is analysed in terms of a Weibull distribution, these failure types may be associated with shape parameters β having values <1, 1, and >1 respectively. Early life failures are frequently attributed to poor design (e.g. poor materials selection) or problems associated with manufacturing or assembly processes.

We describe a methodology for the implementation of physics-of-failure models of component lifetimes in the presence of parameter and model uncertainties. This treats uncertain parameters as random variables described by some appropriate statistical distribution, which may be sampled using Monte Carlo methods. The number of simulations required depends upon the desired accuracy of the predicted lifetime. Provided that the number of sampled variables is relatively small, an accuracy of 1–2% can be obtained using typically 1000 simulations.

The resulting collection of times-to-failure are then sorted into ascending order and fitted to a Weibull distribution to obtain a shape factor β and a characteristic life-time η.

Examples are given of the results obtained using three different models: (1) the Eyring–Peck (EP) model for corrosion of printed circuit boards; (2) a power-law corrosion growth (PCG) model which represents the progressive deterioration of oil and gas pipelines; and (3) a random shock-loading model of mechanical failure. It is shown that for any specific model the values of the Weibull shape parameters obtained may be strongly dependent on the degree of uncertainty of the underlying input parameters. Both the EP and PCG models can yield a wide range of values of β, from β>1, characteristic of wear-out behaviour, to β<1, characteristic of early-life failure, depending on the degree of dispersion of the uncertain parameters. If there is no uncertainty, a single, sharp value of the component lifetime is predicted, corresponding to the limit β=∞. In contrast, the shock-loading model is inherently random, and its predictions correspond closely to those of a constant hazard rate model, characterized by a value of β close to 1 for all finite degrees of parameter uncertainty.

The results are discussed in the context of traditional methods for reliability analysis and conventional views on the nature of early-life failures.     

State space models for condition monitoring: a case study

A Condition Monitoring system can increase safety, quality and availability in industrial plants. Safety requirements are especially important in critical machineries, like a turbine driving a centrifugal compressor located at a petrochemical plant in the case study presented in this paper. A Condition Monitoring system is set up for vibration data coming from the turbine. Four years of monthly data observed at two different locations of the equipment are analysed. The core of the system is a model to forecast the state of the machine using data provided by the Condition Monitoring system at each moment in time. The model is based on the State Space framework whose associated recursive algorithms (Kalman Filter and Fixed Interval Smoothing) provide the basis for a number of different operations, from which the most important in the present context is the extrapolation of the distribution of forecasts on which the probability of failure is estimated. The cost model on which the decision of making a preventive replacement is taken is based on the ‘expected cost per unit time’ for a pre-determined critical value of the vibration measure. The system is thoroughly tested on the data.     

A case study: Reliability‐based product development and improvement model

A field data‐driven reliability and improvement program was introduced to ForHealth Technologies, Inc. (FHT) in December 2004 for its IntelliFill i.v. (IFiv) System. Since then, FHT has developed and executed a unique reliability‐based product development and improvement model. As a result of implementing this model, from January 2005 to February 2007, the reliability of IFiv System improved its Mean Syringes Between Failures by 1703%, Operator Interventions Per Thousand Syringes by 45%, and Mean Time Between Failures improved by 1725% with system uptime of over 99%. Furthermore, system‐related product costs were reduced by over 40%. IFiv has now delivered over 16 million doses without any medication errors and helped hospital pharmacies realize their drug cost savings along with higher operational efficiencies. The purpose of this case study is to share FHT's success story of conducting reliability‐based product development and improvement program, while keeping the process simple with fast results. This model is applicable at system level for all industries with positive bottom line impact. Copyright © 2007 John Wiley & Sons, Ltd.