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.     

Risk-based maintenance of ethylene oxide production facilities

This paper discusses a methodology for the design of an optimum inspection and maintenance program. The methodology, called risk-based maintenance (RBM) is based on integrating a reliability approach and a risk assessment strategy to obtain an optimum maintenance schedule. First, the likely equipment failure scenarios are formulated. Out of many likely failure scenarios, the ones, which are most probable, are subjected to a detailed study. Detailed consequence analysis is done for the selected scenarios. Subsequently, these failure scenarios are subjected to a fault tree analysis to determine their probabilities. Finally, risk is computed by combining the results of the consequence and the probability analyses. The calculated risk is compared against known acceptable criteria. The frequencies of the maintenance tasks are obtained by minimizing the estimated risk. A case study involving an ethylene oxide production facility is presented. Out of the five most hazardous units considered, the pipeline used for the transportation of the ethylene is found to have the highest risk. Using available failure data and a lognormal reliability distribution function human health risk factors are calculated. Both societal risk factors and individual risk factors exceeded the acceptable risk criteria. To determine an optimal maintenance interval, a reverse fault tree analysis was used. The maintenance interval was determined such that the original high risk is brought down to an acceptable level. A sensitivity analysis is also undertaken to study the impact of changing the distribution of the reliability model as well as the error in the distribution parameters on the maintenance interval.     

Determining the optimal maintenance action for a deteriorating repairable system

This paper develops a decision model for risk management of the deterioration of a repairable system. When a failure occurs in a deteriorating system, an optimal maintenance decision that includes the possibility of system replacement, as compared to mere deterioration reduction, should be made. There are many uncertainties associated with deterioration, however, so the decision may require a probabilistic analysis. Here, a well-known nonhomogeneous Poisson process with a power law intensity function is used to model the uncertain behavior of the deteriorating system. A Bayesian statistical approach is adopted to allow for the uncertainty of the parameters of the power law intensity function, which imposes a conjugate prior distribution of the parameters. A power law maintenance cost function and the failure cost are analyzed to determine the magnitude of failure risk reduction by minimizing the expected cost incurred from the maintenance action and future failures. A numerical example is given.     

Reliability-based assessment of ageing bridges using risk ranking and life cycle cost decision analyses

Information about present and anticipated bridge reliabilities, in conjunction with decision models, provides a rational and powerful decision-making tool for the structural assessment of bridges. For assessment purposes, an updated reliability (after an inspection) may be used for comparative or relative risk purposes. This may include the prioritisation of risk management measures (risk ranking) for inspection, maintenance, repair or replacement. A life-cycle cost analysis may also be used to quantify the expected cost of a decision. The present paper will present a broad overview of the concepts, methodology and immediate applications of risk-based assessments of bridges. In particular, two practical applications of reliability-based bridge assessment are considered — risk ranking and life-cycle cost analysis.     

P-graph approach to criticality analysis in integrated bioenergy systems

The use of integrated bioenergy systems (IBS) is a prospective solution to address the emergent global demand for clean energy. The sustainability of IBS compared to stand-alone biomass processing facilities is achieved through integration of process units or component plants via their bioenergy products, by-products, wastes, and common utilities. However, such increased component interdependency makes the resulting integrated energy system vulnerable to capacity disruptions. IBS in particular are vulnerable to climate change-induced events (e.g., drought) that reduce the availability of biomass feedstocks in bioenergy production. Cascading failure due to such supply-side disruptive event is an inherent risk in IBS and may pose a barrier to the commercial-scale adoption of such systems. A previous study developed a risk-based criticality index to quantify the effect of a component’s disruption within integrated energy systems. This index is used to rank the component’s relative risk in the network based on the ripple effects of its disruption. In this work, a novel P-graph approach is proposed as an alternative methodology for criticality analysis of component units or plants in an IBS. This risk-based metric can be used for developing risk management polices to protect critical facilities, thereby increasing the robustness of IBS against disruptions. Two case studies on determining the criticality index of process units in an integrated biorefinery and component plants in a bioenergy park are used to demonstrate the effectiveness of this method.     

Minimum expected cost-oriented optimal maintenance planning for deteriorating structures: application to concrete bridge decks

Civil engineering structures are designed to serve the public and often must perform safely for decades. No matter how well they are designed, all civil engineering structures will deteriorate over time and lifetime maintenance expenses represent a substantial portion of the total lifetime cost of most structures. It is difficult to make a reliable prediction of this cost when the future is unknown and structural deterioration and behavior are assumed from a mathematical model or previous experience. An optimal maintenance program is the key to making appropriate decisions at the right time to minimize cost and maintain an appropriate level of safety. This study proposes a probabilistic framework for optimizing the timing and the type of maintenance over the expected useful life of a deteriorating structure. A decision tree analysis is used to develop an optimum lifetime maintenance plan which is updated as inspections occur and more data is available. An estimate which predicts cost and behavior over many years must be refined and reoptimized as new information becomes available. This methodology is illustrated using a half-cell potential test to evaluate a deteriorating concrete bridge deck. The study includes the expected life of the structure, the expected damage level of the structure, costs of inspection and specific repairs, interest rates, the capability of the test equipment to detect a flaw, and the management approach of the owner towards making repairs.     

Bayesian risk-based decision method for model validation under uncertainty

This paper develops a decision-making methodology for computational model validation, considering the risk of using the current model, data support for the current model, and cost of acquiring new information to improve the model. A Bayesian decision theory-based method is developed for this purpose, using a likelihood ratio as the validation metric for model assessment. An expected risk or cost function is defined as a function of the decision costs, and the likelihood and prior of each hypothesis. The risk is minimized through correctly assigning experimental data to two decision regions based on the comparison of the likelihood ratio with a decision threshold. A Bayesian validation metric is derived based on the risk minimization criterion. Two types of validation tests are considered: pass/fail tests and system response value measurement tests. The methodology is illustrated for the validation of reliability prediction models in a tension bar and an engine blade subjected to high cycle fatigue. The proposed method can effectively integrate optimal experimental design into model validation to simultaneously reduce the cost and improve the accuracy of reliability model assessment.     

Fuzzy belief propagation in constrained Bayesian networks with application to maintenance decisions

Bayesian networks have been widely applied to domains such as medical diagnosis, fault analysis, and preventative maintenance. In some applications, because of insufficient data and the complexity of the system, fuzzy parameters and additional constraints derived from expert knowledge can be used to enhance the Bayesian reasoning process. However, very few methods are capable of handling the belief propagation in constrained fuzzy Bayesian networks (CFBNs). This paper therefore develops an improved approach which addresses the inference problem through a max-min programming model. The proposed approach yields more reasonable inference results and with less computational effort. By integrating the probabilistic inference drawn from diverse sources of information with decision analysis considering a decision-maker's risk preference, a CFBN-based decision framework is presented for seeking optimal maintenance decisions in a risk-based environment. The effectiveness of the proposed framework is validated based on an application to a gas compressor maintenance decision problem.     

Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization

Optimization of testing and maintenance activities performed in the different systems of a complex industrial plant is of great interest as the plant availability and economy strongly depend on the maintenance activities planned. Traditionally, two types of models, i.e. deterministic and probabilistic, have been considered to simulate the impact of testing and maintenance activities on equipment unavailability and the cost involved. Both models present uncertainties that are often categorized as either aleatory or epistemic uncertainties. The second group applies when there is limited knowledge on the proper model to represent a problem, and/or the values associated to the model parameters, so the results of the calculation performed with them incorporate uncertainty. This paper addresses the problem of testing and maintenance optimization based on unavailability and cost criteria and considering epistemic uncertainty in the imperfect maintenance modelling. It is framed as a multiple criteria decision making problem where unavailability and cost act as uncertain and conflicting decision criteria. A tolerance interval based approach is used to address uncertainty with regard to effectiveness parameter and imperfect maintenance model embedded within a multiple-objective genetic algorithm. A case of application for a stand-by safety related system of a nuclear power plant is presented. The results obtained in this application show the importance of considering uncertainties in the modelling of imperfect maintenance, as the optimal solutions found are associated with a large uncertainty that influences the final decision making depending on, for example, if the decision maker is risk averse or risk neutral.     

A Dynamic Risk Assessment Methodology for Maintenance Decision Support

The failure mode and effect analysis (FMEA) is a widely applied technique for prioritizing equipment failures in the maintenance decision‐making domain. Recent improvements on the FMEA have largely focussed on addressing the shortcomings of the conventional FMEA of which the risk priority number is incorporated as a measure for prioritizing failure modes. In this regard, considerable research effort has been directed towards addressing uncertainties associated with the risk priority number metrics, that is occurrence, severity and detection. Despite these improvements, assigning these metrics remains largely subjective and mostly relies on expert elicitations, more so in instances where empirical data are sparse. Moreover, the FMEA results remain static and are seldom updated with the availability of new failure information. In this paper, a dynamic risk assessment methodology is proposed and based on the hierarchical Bayes theory. In the methodology, posterior distribution functions are derived for risk metrics associated with equipment failure of which the posterior function combines both prior functions elicited from experts and observed evidences based on empirical data. Thereafter, the posterior functions are incorporated as input to a Monte Carlo simulation model from which the expected cost of failure is generated and failure modes prioritized on this basis. A decision scheme for selecting appropriate maintenance strategy is proposed, and its applicability is demonstrated in the case study of thermal power plant equipment failures. Copyright © 2016 John Wiley & Sons, Ltd.     

Sustainable Manufacturing Oriented Prognosis for Facility Reuse Combining ANN and Reliability

Reuse is considered as one of the most reasonable strategies in realizing sustainability, because it enables longer useful life of facilities. This article presents an effective methodology of artificial neural network–based prognosis combined with reliability methods to evaluate and guarantee the reusability of a facility. The methodology provides the assessment of the degradation trend and prediction of the remaining life of facilities based on online condition monitoring data and historical data utilizing back propagation artificial neural networks. In addition, the corresponding reliability of a facility is calculated by fitting suitable life distribution against the in‐house time‐to‐failure data. Furthermore, maintenance decision is made by predicting the time when reliability or remaining life of a facility reaches the threshold, as determined by the facility's reusability. Application results show that the proposed methodology provides sufficient condition information for reuse decision making from both historical and online perspectives; a facility can be reused for many times during its lifetime until its reuse is no longer economic, which can assist in the achievement of the goal of manufacturing with fewer resources and assets. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimum condition-based maintenance policies for deteriorating systems with partial information

Systems are considered which deteriorate as time goes on and whose conditions can be observed. The optimum maintenance policy with respect to cost is determined, based on a continuous deterioration process, by formulating the decision process as a discrete Markov decision problem. Examples from the field of civil engineering are given. The relation between the type of deterioration and condition-based optimum inspection intervals, optimum repair level, minimum average maintenance costs and mean time to repair is shown, using some numerical results.     

Calibration of Expert Judgments Applied to Uncertainty Assessment

Abstract:

This article contributes a methodology for eliciting expert judgment in support of decision analysis associated with the conceptual design of advanced engineering systems. To provide a basis for decision making in the presence of model and input parameter uncertainties, experts in several disciplines can be utilized to provide model parameter estimates to facilitate analyses. The judgment elicitation methodology was developed to cover a multitude of system disciplines using multiple experts. To address consistency in expert assessments, the methodology includes expert assessment calibration means. A sample application of the resultant expert judgment methodology is discussed. An engineering manager can use the methodology described in this article to assess viability of potential courses of action in high-risk or advanced state-of-the-art technology systems development ventures.     

Direct and indirect effects of mood on risk decision making in safety-critical workers

The study aimed to examine the direct influence of specific moods (fatigue, anxiety, happiness) on risk in safety-critical decision making. It further aimed to explore indirect effects, specifically, the potential mediating effects of information processing assessed using a goodness-of-simulation task. Trait fatigue and anxiety were associated with an increase in risk taking on the Safety-Critical Personal Risk Inventory (S-CPRI), however the effect of fatigue was partialled out by anxiety. Trait happiness, in contrast was related to less risky decision making. Findings concerning the ability to simulate suggest that better simulators made less risky decisions. Anxious workers were generally less able to simulate. It is suggested that in this safety-critical environment happiness had a direct effect on risk decision making while the effect of trait anxiety was mediated by goodness-of-simulation.     

A condition-based maintenance policy with non-periodic inspections for a two-unit series system

This paper considers a condition-based maintenance policy for a two-unit deteriorating system. Each unit is subject to gradual deterioration and is monitored by sequential non-periodic inspections. It can be maintained by good as new preventive or corrective replacements. Every inspection or replacement entails a set-up cost and a component-specific unit cost but if actions on the two components are combined, the set-up cost is charged only once. A parametric maintenance decision framework is proposed to coordinate inspection/replacement of the two components and minimize the long-run maintenance cost of the system. A stochastic model is developed on the basis of the semi-regenerative properties of the maintained system state and the associated cost model is used to assess and optimize the performance of the maintenance model. Numerical experiments emphasize the interest of a control of the operation groupings.     

Human factors impact on risk analysis of complex systems

This paper discusses the methods and techniques that are applied for including human factors considerations into risk analysis of modern plants. The application of new control design principles and the extensive use of automation have strongly modified the role of operators, who have progressively become supervisors of automatically performed procedures and decision makers in a context of shared management processes. This implies that cognitive functions and organisational factors affect risk analysis much more than behavioural and physical performances. Another crucial issue of human reliability assessment concerns the dynamic nature of human-machine interaction. This feature covers a wide spectrum of real situations, but demands quite complex and extensive data. These considerations favour the development of new and evolutionary techniques which must be confronted with the requirements and needs of different types of risk analysis be carried out for different objectives, such as quantitative risk analysis, safety management, accident investigation, risk-based decision making and risk-based regulations. Advantages and areas of application of different techniques are briefly discussed, without attempting to develop a detailed comparison.     

Improving technical specifications from a risk perspective

The Technical Specifications (TSs) for a nuclear power plant is an important licensing document which defines various operational requirements or conditions. In light of the recent trends to move towards risk-based regulation, many researchers analyzed the risk impacts associated with the TS requirements, using the plant models, such as event trees or fault trees, that were developed as part of probabilistic safety assessments. This paper presents the insights gained from a review of these risk-based analyses of TSs, focussing on surveillance requirements and AOT (allowed outage time) requirements.     

Condition-based maintenance for a system subject to a non-homogeneous wear process with a wear rate transition

The aim of this paper is to propose an adaptive maintenance model for a gradually deteriorating system. The system considered initially deteriorates with a nominal deterioration rate and at an unknown time the system's deterioration rate changes and the new deterioration rate is a time-dependent function. To deal with the transition of mode of deterioration in the framework of the maintenance decision rule an adequate online change detection algorithm is used. The maintenance decision rule is chosen in order to minimise the total maintenance cost including the cost of unavailability. The main result of this paper is to point out the interest of using a detection algorithm and hence the appreciation of a decision rule which takes into account transitions in the deterioration rate.     

Choosing among safety improvement strategies: a discussion with example of risk assessment and multi-criteria decision approaches for NASA

This paper focuses on risk assessment and multi-criteria decision making methods applicable to deciding among candidate safety improvement strategies in the face of cost, safety and other uncertainties for NASA flight vehicles, launch vehicles and ground research facilities. Deciding on the best safety improvement strategy to implement on a spacecraft involves balancing safety with other quantifiable criteria such as technical feasibility, schedule, mass, performance, volume, and cost. The strategem of a simplified example is used to illustrate the use of probabilistic risk assessment derived results with multi-criteria decision making in the face of uncertainties. The investigated decision making approaches are intuition, cost/benefit ratio, expected impact, and Analytic Hierarchy Process. A useful sensitivity study, termed Decision Trajectories, is proposed in this paper. The example, using the shuttle auxiliary power units (APUs), is limited to the following criteria: (1) safety improvement of proposed strategies, and (2) the associated recurring and non-recurring costs. These two criteria provide sufficient richness of domain to illustrate the technologies of risk assessment and decision making. Because of the general unfamiliarity of the readers with work being conducted by NASA, this paper also provides the needed background.     

Remote nondestructive evaluation technique using infrared thermography for fatigue cracks in steel bridges

Long‐standing infrastructure is subject to structural deterioration. In this respect, steel bridges suffer fatigue cracks, which necessitate immediate inspection, structural integrity evaluation or repair. However, the inaccessibility of such structures makes inspection time consuming and labour intensive. Therefore, there is an urgent need for developing high‐performance nondestructive evaluation (NDE) methods to assist in effective maintenance of such structures. Recently, use of infrared cameras in nondestructive testing has been attracting increasing interest, as they provide highly efficient remote and wide area measurements. This paper first reviews the current situation of nondestructive inspection techniques used for fatigue crack detection in steel bridges, and then presents remote NDE techniques using infrared thermography developed by the author for fatigue crack detection and structural integrity assessments. Furthermore, results of applying fatigue crack evaluation to a steel bridge using the newly developed NDE techniques are presented.