Dynamic modeling of the tradeoff between productivity and safety in critical engineering systems

Short-term tradeoffs between productivity and safety often exist in the operation of critical facilities such as nuclear power plants, offshore oil platforms, or simply individual cars. For example, interruption of operations for maintenance on demand can decrease short-term productivity but may be needed to ensure safety. Operations are interrupted for several reasons: scheduled maintenance, maintenance on demand, response to warnings, subsystem failure, or a catastrophic accident. The choice of operational procedures (e.g. timing and extent of scheduled maintenance) generally affects the probabilities of both production interruptions and catastrophic failures. In this paper, we present and illustrate a dynamic probabilistic model designed to describe the long-term evolution of such a system through the different phases of operation, shutdown, and possibly accident. The model's parameters represent explicitly the effects of different components' performance on the system's safety and reliability through an engineering probabilistic risk assessment (PRA). In addition to PRA, a Markov model is used to track the evolution of the system and its components through different performance phases. The model parameters are then linked to different operations strategies, to allow computation of the effects of each management strategy on the system's long-term productivity and safety. Decision analysis is then used to support the management of the short-term trade-offs between productivity and safety in order to maximize long-term performance. The value function is that of plant managers, within the constraints set by local utility commissions and national (e.g. energy) agencies. This model is illustrated by the case of outages (planned and unplanned) in nuclear power plants to show how it can be used to guide policy decisions regarding outage frequency and plant lifetime, and more specifically, the choice of a reactor tripping policy as a function of the state of the emergency core cooling subsystem.     

Acquisition of new technology information for maintenance and replacement policies

In this paper, we propose the first model that considers the option to acquire information on the profitability of a new technology that is not yet available on the market for asset maintenance and replacement decisions. We consider the uncertainty of future asset characteristics by incorporating information acquisition decisions into a non-stationary Markov decision process framework. Using this framework, we optimise asset maintenance and replacement decisions as well as the optimal timing of new technology adoption. Through mathematical analyses, the monotone properties and convexity of the value function and optimal policy are deduced. Deeper numerical analyses highlight the importance of considering the acquisition of information on future technology when formulating a maintenance and replacement policy for the asset. We also deduce a non-intuitive result: an increase in the arrival probability of new technology does not necessarily make the acquisition of additional information more attractive.     

Utility based maintenance analysis using a Random Sign censoring model

Industrial systems subject to failures are usually inspected when there are evident signs of an imminent failure. Maintenance is therefore performed at a random time, somehow dependent on the failure mechanism. A competing risk model, namely a Random Sign model, is considered to relate failure and maintenance times. We propose a novel Bayesian analysis of the model and apply it to actual data from a water pump in an oil refinery. The design of an optimal maintenance policy is then discussed under a formal decision theoretic approach, analyzing the goodness of the current maintenance policy and making decisions about the optimal maintenance time.     

Optimal major and minimal maintenance policies for deteriorating systems

We present a maintenance model for a multi-state semi-Markovian deteriorating system. Our model allows one of three maintenance decisions (do-nothing, minimal maintenance or replacement) to be taken at each state of the system. We use control limit policy and the policy-iteration algorithm to find the optimal maintenance policies that minimizes the expected long-run cost rate of the system. Numerical examples are given to illustrate the proposed policies.     

Machining conditions-based preventive maintenance

In this study we propose an operating conditions-based preventive maintenance (PM) approach for computer numerical control (CNC) turning machines. A CNC machine wears according to how much it is used and the conditions under which it is used. Higher power or production rates result in more wear and higher failure rates. This relationship between the operating conditions and maintenance requirements is usually overlooked in the literature. On CNC turning machines we can control the machining conditions such as cutting speed and feed rate, which in turn affect the PM requirements of the CNC machine. We provide a new model to link the PM decisions to the machining conditions selection decisions, so that these two decision-making problems can be solved together by considering their impact on each other. We establish that our proposed geometric programming model captures the related cost terms along with the technological restrictions of CNC machines. The proposed preventive maintenance index function can be used to provide an intelligent CNC machine degradation assessment.     

Integrated maintenance and production control of a deteriorating production system

We consider a make-to-stock production/inventory system consisting of a single deteriorating machine which produces a single item. We formulate the integrated decisions of maintenance and production using a Markov Decision Process. The optimal dynamic policy is shown to have a rather complex structure which leads us to consider more implementable policies. We present a double-threshold policy and derive exact and approximate methods for evaluating the performance of this policy and computing its optimal parameters. A detailed numerical study demonstrates that the proposed policy and our approximate method for computing its parameters perform extremely well. Finally, we show that policies which do not address maintenance and production control decisions in an integrated manner can perform rather badly.     

An approach to maintenance optimization where safety issues are important

The starting point for this paper is a traditional approach to maintenance optimization where an object function is used for optimizing maintenance intervals. The object function reflects maintenance cost, cost of loss of production/services, as well as safety costs, and is based on a classical cost–benefit analysis approach where a value of prevented fatality (VPF) is used to weight the importance of safety. However, the rationale for such an approach could be questioned. What is the meaning of such a VPF figure, and is it sufficient to reflect the importance of safety by calculating the expected fatality loss VPF and potential loss of lives (PLL) as being done in the cost–benefit analyses? Should the VPF be the same number for all type of accidents, or should it be increased in case of multiple fatality accidents to reflect gross accident aversion?In this paper, these issues are discussed. We conclude that we have to see beyond the expected values in situations with high safety impacts. A framework is presented which opens up for a broader decision basis, covering considerations on the potential for gross accidents, the type of uncertainties and lack of knowledge of important risk influencing factors. Decisions with a high safety impact are moved from the maintenance department to the “Safety Board” for a broader discussion. In this way, we avoid that the object function is used in a mechanical way to optimize the maintenance and important safety-related decisions are made implicit and outside the normal arena for safety decisions, e.g. outside the traditional “Safety Board”.A case study from the Norwegian railways is used to illustrate the discussions.     

A multiobjective genetic algorithm approach to the optimization of the technical specifications of a nuclear safety system

Systems, structures, and components of Nuclear Power Plants are subject to Technical Specifications (TSs) that establish operational limitations and maintenance and test requirements with the objective of keeping the risk associated to the plant within the limits imposed by the regulatory agencies. Recently, in an effort to improve the competitiveness of nuclear energy in a deregulated market, modifications to maintenance policies and TSs are being considered within a risk-informed viewpoint, which judges the effectiveness of a TS, e.g. a particular maintenance policy, with respect to its implications on the safety and economics of the system operation.In this regard, a recent policy statement of the US Nuclear Regulatory Commission declares appropriate the use of Probabilistic Risk Assessment models to evaluate the effects on the system of a particular TS. These models rely on a set of parameters at the component level (failure rates, repair rates, frequencies of failure on demand, human error rates, inspection durations, and others) whose values are typically affected by uncertainties. Thus, the estimate of the system performance parameters corresponding to a given TS value must be supported by some measure of the associated uncertainty.In this paper we propose an approach, based on the effective coupling of genetic algorithms and Monte Carlo simulation, for the multiobjective optimization of the TSs of nuclear safety systems. The method transparently and explicitly accounts for the uncertainties in the model parameters by attempting to minimize both the expected value of the system unavailability and its associated variance. The costs of the alternative TSs solutions are included as constraints in the optimization. An application to the Reactor Protection Instrumentation System of a Pressurized Water Reactor is demonstrated.     

A combined goal programming—AHP approach to maintenance selection problem

This paper presents a ‘Lexicographic’ Goal Programming (LGP) approach to define the best strategies for the maintenance of critical centrifugal pumps in an oil refinery.For each pump failure mode, the model allows to take into account the maintenance policy burden in terms of inspection or repair and in terms of the manpower involved, linking them to efficiency-risk aspects quantified as in FMECA methodology through the use of the classic parameters occurrence (O), severity (S) and detectability (D), evaluated through an adequate application of the Analytic Hierarchy Process (AHP) technique.An extended presentation of the data and results of the case analysed is proposed in order to show the characteristics and performance of this approach.     

Production, operating risk and market uncertainty: a valuation perspective on controlled policies

This paper develops a contingent claims model of an optimally controlled production process characterized by financial and operational risks. Financial risk is depicted by the uncertainty in output prices as determined in competitive markets. Operational uncertainty is portrayed through the risk of system failures which we represent by a nonhomogeneous Poisson process. In the analysis, failure propensity is functionally specified by the system's age, rate of production, and system maintenance expenditures. In this environment, the model obtains an optimal production and maintenance policy maximizing the value of the production effort. Determination of the optimal policies results through the application of stochastic control techniques where production and maintenance expenditure rates are taken as adapted real-valued processes. Further extensions of the model include the analysis of an insurance option on failure repairs and the consequent moral hazard implications. We demonstrate that an appropriately established insurance premium must reflect the producer's operating policy in place and the extent to which operating policies may be modified to maintain the same level of operating risk, as in the absence of an insurance option. The framework presented provides insight into key strategic factors that affect the management of process operations, operating flexibility issues and their resulting economic value.     

Through the eyes of a bird: modelling visually guided obstacle flight

Various flight navigation strategies for birds have been identified at the large spatial scales of migratory and homing behaviours. However, relatively little is known about close-range obstacle negotiation through cluttered environments. To examine obstacle flight guidance, we tracked pigeons (Columba livia) flying through an artificial forest of vertical poles. Interestingly, pigeons adjusted their flight path only approximately 1.5 m from the forest entry, suggesting a reactive mode of path planning. Combining flight trajectories with obstacle pole positions, we reconstructed the visual experience of the pigeons throughout obstacle flights. Assuming proportional–derivative control with a constant delay, we searched the relevant parameter space of steering gains and visuomotor delays that best explained the observed steering. We found that a pigeon''s steering resembles proportional control driven by the error angle between the flight direction and the desired opening, or gap, between obstacles. Using this pigeon steering controller, we simulated obstacle flights and showed that pigeons do not simply steer to the nearest opening in the direction of flight or destination. Pigeons bias their flight direction towards larger visual gaps when making fast steering decisions. The proposed behavioural modelling method converts the obstacle avoidance behaviour into a (piecewise) target-aiming behaviour, which is better defined and understood. This study demonstrates how such an approach decomposes open-loop free-flight behaviours into components that can be independently evaluated.     

Health-health analysis-an alternative method for economic appraisal of health policy and safety regulation. Some empirical Swedish estimates

Health-health analysis (HHA) focuses on statistical lives themselves as a numeraire. The underlying principle is that the expected gains in health and safety of reduced risks in one area may result in increasing risks somewhere else in society. By reducing one risk other risks may increase due to changed individual behaviour.In addition to this direct effect, another indirect effect will also be present. Expenditure on a particular health policy or safety regulation must be financed in one way or another, which will result in an opportunity cost or income effect leaving less resources for other health and safety promoting activities in society. Thus, we will have an effect that reduces safety and health benefits induced by that income loss. Whether the total net health effect from a specific safety regulation or health policy is positive or negative must be empirically analysed. One way of estimating the income loss that induces one death, which we call the value of an induced death (VOID), is to estimate it as a multiple of the traditional value to avert a statistical death, also named the value of a statistical life (VOSL).A contingent valuation (CV) study eliciting the willingness-to-pay (WTP) for reducing the overall risk of dying was performed as a postal questionnaire in Sweden in 1998. By use of data from this study, it was possible to estimate the VOID and the VOSL in Sweden amounting to SEK116 and SEK20.8 million respectively, indicating that the net health result confined to mortality effects, will be negative (more lives will be lost than saved) if a health policy or safety regulation will cost more than SEK116 million per life saved.     

Adaptive sequential predictive maintenance policy with nonperiodic inspection for hard failures

This paper proposes a novel adaptive maintenance policy for degrading systems subject to hard failure. Compared with traditional condition-based maintenance policies, the proposed predictive maintenance policy makes maintenance decisions adaptively based on model prognostic results. The prognostic model is continuously updated based on newly inspected data. The inspection times and preventive maintenance activities are scheduled online in a sequential manner based on the most current prediction of system reliability. A computationally efficient optimization scheme is proposed for obtaining optimal maintenance parameters. The proposed policy is demonstrated and its performance is evaluated through extensive simulations.     

Optimal multi-level classification and preventive maintenance policy for highly reliable products

Highly reliable products are widely used in aerospace, automotive, integrated manufacturing and other fields. With increasing market demand and competition, product classification for different segment market segments has become more and more critical. Leading manufacturers are always searching and designing classification policies for highly reliable products. On the other hand, preventive maintenance can improve the operation efficiency of the product, extend the service life and reduce enormous losses brought by failures. These two factors are taken into account by many large enterprises when making sound economical and operational decisions. Therefore, this research proposes a joint multi-level classification and preventive maintenance model (JMCPM model) under age-based maintenance. Different preventive maintenance policies are developed for corresponding level units. Accordingly, the optimal joint policy of multi-level classification and preventive maintenance can be obtained by JMCPM. In this model, degradation-based burn-in is utilised to eliminate defective units and collect degradation data. The degradation data are the basis of classification and can be used to estimate the residual life. Then, for making full use of these data, linear discriminant analysis is employed to design classification rules. The objective of the JMCPM model is to minimise the average cost per unit time by properly choosing the settings of classification and preventive maintenance intervals simultaneously. Finally, a simulation study is carried out for evaluating the performance of the JMCPM model. For an illustration of the proposed model and the methods of inference developed here, a real case involving degradation data from electrical connectors is analysed.     

Integrated fleet assignment and aircraft routing based on delay propagation

Airlines’ expensive resources, especially aircraft, are to be optimally scheduled to cover flights of timetables. However, the irregular flight, due to bad weather, mechanical fault and so on, is inevitable. Moreover, flight delays become more severe with the rapid development of the air transport industry in China and have huge irregular flight cost. In order to alleviate flight delays impact on the flight plan, we present a double objective multi-commodity network flow model of flight delay propagation-based aircraft scheduling and minimize the total delay propagation and airline operation cost as the optimization objective. Branch-and-price solution and column generation algorithm are used to solve the problem. Computational results obtained by using data from a major domestic airline show that our approach can reduce delay propagation significantly, thus improving on-time performance and robustness of aircraft scheduling, and decreasing the total cost simultaneously.     

Experience,Equity and Foreign Investment Risk: A PIC Perspective

We analyze foreign investment risk-mitigating effects of host-country policy stability, firm experience and equity stakes using an empirical context largely ignored by international business (IB) research: project investment companies (PICs). PICs permit cleaner separation of individual investment project risk from the parent firm, which may otherwise pool risk characteristics from managing multiple projects across different industries and countries. PICs also permit potentially unbiased, prospective risk assessment at the time of a project’s initial announcement based on the mix of debt and equity funding the project. Consistent with previous IB research, our analyses of 396 PICs announced in 53 countries from 1990–2006 indicate that investment risk measured as the percentage of equity-to-total capital funding a PIC decreases with greater host-country policy stability, lead-investor experience in the host country, and lead-investor equity stakes. But contrary to previous IB research, we find that lead-sponsor experience and equity stakes reduce investment risk less as host-country policy stability decreases. From a PIC perspective, investor experience and equity stakes are complements to (not substitutes for) host-country policy stability. Our PIC-based evidence re-invigorates research and related practice and policy debates about how investor experience and equity holdings affect foreign project decisions and suggests new avenues for future work.     

The effect of an advisory system on pilots' go/no-go decision during take-off

The take-off phase of modern airliners is a relatively critical phase of flight. Thus, about 12% of all civil aviation accidents happen during take-off. In this paper we describe results of an experimental study of a prototype cockpit advisory take-off monitoring system designed to help pilots to make better and safer go/no-go decisions in the case of abnormal events during take-off. We describe, first, the basic aspects of the take-off task and, second, some of the information processing and risk assessment problems involved in making go/no-go decisions at high speeds during take-off. Third, we describe a prototype advisory take-off monitoring system (ATOMS), which as the result of a research project, has been designed to improve pilots' judgement of acceleration and deceleration during the take-off roll. Fourth, we report on results of an experimental study of this prototype system in a full-flight simulator — results that indicate that ATOMS has a promising potential to improve take-off safety. Finally, we discuss implications of the experimental results for systems support for pilots during take-off.     

Risk-based maintenance--techniques and applications

Plant and equipment, however well designed, will not remain safe or reliable if it is not maintained. The general objective of the maintenance process is to make use of the knowledge of failures and accidents to achieve the possible safety with the lowest possible cost. The concept of risk-based maintenance was developed to inspect the high-risk components usually with greater frequency and thoroughness and to maintain in a greater manner, to achieve tolerable risk criteria. Risk-based maintenance methodology provides a tool for maintenance planning and decision making to reduce the probability of failure of equipment and the consequences of failure. In this paper, the risk analysis and risk-based maintenance methodologies were identified and classified into suitable classes. The factors affecting the quality of risk analysis were identified and analyzed. The applications, input data and output data were studied to understand their functioning and efficiency. The review showed that there is no unique way to perform risk analysis and risk-based maintenance. The use of suitable techniques and methodologies, careful investigation during the risk analysis phase, and its detailed and structured results are necessary to make proper risk-based maintenance decisions.     

Empirical analysis of maintenance performance measurement in Belgian industries

Performance measurement is a fundamental instrument of management. For maintenance management, one of the key issues is to ensure the maintenance activities planned and executed have given the expected results. This can be facilitated by effective use of rigorously defined key performance indicators (KPI) that are able to measure important aspects of maintenance function. In this paper, an industrial survey was carried out to explore the use of performance measurement in maintenance management. Based on survey responses, analyses were performed on popularly used KPI, how these KPI are sourced or chosen; the influence of manufacturing environment and maintenance objectives on KPI choice and effective use of these KPI in decision support and performance improvement. It was found that maintenance performance measurement is dominated by lagging indicators (equipment, maintenance cost and safety performance). There is lesser use of leading (maintenance work process) indicators. The results showed no direct correlations between the maintenance objectives pursued and the KPI used. Further analysis showed that only a minority of the companies have a high percentage of decisions and changes triggered by KPI use and only a few are satisfied with their performance measurement systems. Correlation analysis showed a strong positive linear relationship between degree of satisfaction and process changes/decisions triggered by KPI use, with the least satisfied people having the least decisions and changes triggered by KPI use. The results indicate some ineffectiveness of performance measurement systems in driving performance improvement in industries.