Modeling of uncertainties in reliability centered maintenance — a probabilistic approach

Uncertainties in the decision making of reliability centered maintenance (RCM) are discussed. These uncertainties might be unacceptable in many practical applications, leading to non-optimum maintenance strategies. An alternative approach, opening for specified uncertainties is shown to correct this defect. Exemplifying the approach, a simple fire detection system is discussed.     

Predictive maintenance of complex system with multi-level reliability structure

Onboard sensors, which constantly monitor the states of a system and its components, have made the predictive maintenance (PdM) of a complex system possible. To date, system reliability has been extensively studied with the assumption that systems are either single-component systems or they have a deterministic reliability structure. However, in many realistic problems, there are complex multi-component systems with uncertainties in the system reliability structure. This paper presents a PdM scheme for complex systems by employing discrete time Markov chain models for modelling multiple degradation processes of components and a Bayesian network (BN) model for predicting system reliability. The proposed method can be considered as a special type of dynamic Bayesian network because the same BN is repeatedly used over time for evaluating system reliability and the inter-time–slice connection of the same node is monitored by a sensor. This PdM scheme is able to make probabilistic inference at any system level, so PdM can be scheduled accordingly.     

A reliability centered approach to remote condition monitoring. A railway points case study

Railway turnouts, consisting of switches and a crossing, are complex electro-mechanical devices which are exposed to severe environmental influences and which are essential for the operation of any railway bar horizontal lifts. Their safe and reliable operation must be assured if the rail mode of transport is to flourish. Conventionally, the continuous availability of turnout mechanisms has been assured by high levels of routine maintenance, to some extent tailored to the criticality of a particular point location. However, traffic increases and shortened maintenance windows require better approaches to turnout maintenance. The authors of the present paper undertook the development of algorithms to detect gradual failure in railway turnout which should allow a move to an RCM² approach to the management of switch and crossing maintenance. They demonstrate the approach using data from tests on a commonly found point mechanism and include a discussion of the benefits of adopting a Kalman Filter for pre-processing the data collected during tests.     

Age-related maintenance versus reliability centred maintenance: a case study on aero-engines

Reliability Centred Maintenance (RCM) is a procedure carried out as part of the logistic support analysis (LSA) process and is described in the US Department of Defence Military Standards (Mil Std 2173). RCM allows logisticians the opportunity to determine the best maintenance policy for each component within a system. However, the only data that are available to carryout RCM using Mil Std 2173 are of MTBF. This implies that all the necessary mathematical models need to be based on the exponential distribution. This is a serious drawback to the whole concept of RCM as the exponential distribution cannot be used to model items that fail due to wear, or any other mode that is related to their age. In this paper, a new approach to RCM is proposed using the concepts of soft life and hard life to optimise the total maintenance cost. For simplicity, only one mode of failure is considered for each component. However, the model can be readily applied to multiple failure modes. The proposed model is applied to find the optimal maintenance policies in the case of military aero-engines using Monte Carlo simulation. The case study shows a potential benefit from setting soft lives on relatively cheap components that can cause expensive, unplanned engine rejections.     

A study of availability-centered preventive maintenance for multi-component systems

This paper studies preventive maintenance (PM) in simultaneously considering three actions, mechanical service, repair and replacement for a multi-components system based on availability. Mechanical service denotes the activities including lubricating, cleaning, checking and adjusting, etc. which is set to alleviate strength degradation. Repair is defined on that not only slow down the degraded velocity but also restore the degraded strength partly. Replacement is settled to recover a component to its original condition. According to the definitions, the degradation of components is analyzed from its failure mechanisms and the improvements of various actions to it in reliability were measured by using two improved factors. Following the proposed model of reliability, the mean-up and mean-down times of each component are also investigated and the replacement intervals of components are determined based on availability maximization. Here, the minimum one among the intervals is chosen as the PM interval of system for programming the periodical PM policy. The selection of action for the components on every PM stage is decided by maximizing system benefit in maintenance. Repeatedly, the scheduling is progressed step by step and is terminated until the system extended life reaching to its expected life. The complete schedule provides the information, the actions adopted for the components, the availability and the total cost of system on each stage. Validly, a multi-components system is used as an example to describe the proposed algorithm.     

A discrete-time Bayesian network reliability modeling and analysis framework

Dependability tools are becoming an indispensable tool for modeling and analyzing (critical) systems. However the growing complexity of such systems calls for increasing sophistication of these tools. Dependability tools need to not only capture the complex dynamic behavior of the system components, but they must be also easy to use, intuitive, and computationally efficient. In general, current tools have a number of shortcomings including lack of modeling power, incapacity to efficiently handle general component failure distributions, and ineffectiveness in solving large models that exhibit complex dependencies between their components. We propose a novel reliability modeling and analysis framework based on the Bayesian network (BN) formalism. The overall approach is to investigate timed Bayesian networks and to find a suitable reliability framework for dynamic systems. We have applied our methodology to two example systems and preliminary results are promising. We have defined a discrete-time BN reliability formalism and demonstrated its capabilities from a modeling and analysis point of view. This research shows that a BN based reliability formalism is a powerful potential solution to modeling and analyzing various kinds of system components behaviors and interactions. Moreover, being based on the BN formalism, the framework is easy to use and intuitive for non-experts, and provides a basis for more advanced and useful analyses such as system diagnosis.     

Selective critique of risk assessments with recommendations for improving methodology and practise

Risk assessments are often criticised for defending activities that could harm the environment and human health. The risk assessments produce numbers which are used to prove that the risk associated with the activity is acceptable. In this way, risk assessments seem to be a tool generally serving business. Government agencies have based their regulations on the use of risk assessment and the prevailing practise is supported by the regulations. In this paper, we look more closely into this critique. Are risk assessments being misused or are risk assessments simply not a suitable tool for guiding decision-making in the face of risks and uncertainties? Is the use of risk assessments not servicing public interests? We argue that risk assessments may provide useful decision support but the quality of the risk assessments and the associated risk assessment processes need to be improved. In this paper, three main improvement areas (success factors) are identified and discussed: (1) the scientific basis of the risk assessments needs to be strengthened, (2) the risk assessments need to provide a much broader risk picture than what is typically the case today. Separate uncertainty analyses should be carried out, extending the traditional probabilistic-based analyses and (3) the cautionary and precautionary principles need to be seen as rational risk management approaches, and their application would, to a large extent, be based on risk and uncertainty assessments.     

Applying RCM in large scale systems: a case study with railway networks

In 2000, the European Union founded a project named ‘RAIL: Reliability centered maintenance approach for the infrastructure and logistics of railway operation’ aimed to study the application of Reliability centered maintenance (RCM) techniques to the railway infrastructure. In this paper, we present the results obtained into the RAIL project, including a RCM methodology adapted to large infrastructure networks and a RCM toolkit to perform the RCM analysis, including cost aspects and maintenance planning guidance. This paper addresses the problem of applying RCM to large scale railway infrastructure networks to achieve an efficient and effective maintenance concept. Railways use nowadays very traditional preventive maintenance (PM) techniques, relying mostly on ‘blind’ periodic inspection and the ‘know-how’ of maintenance staff. RCM was seen as a promising technique from the beginning of the RAIL project because of several factors. First, technical insights obtained were better than the existing, so that several maintenance processes could be revised and adjusted. Second, the interdisciplinary approach used to make the analysis was very enriching and very encouraging for maintenance staff consulted. Third, using the RCM structured approach allowed to achieve well-documented analysis and clear decision diagrams. Our methodology includes some new features to overcome the problems of RCM observed in other projects. As a whole, our methodology and Computerized Maintenance Management Systems have produced two short-term benefits: reduction of time and paperwork because databases and tools are accessible through Internet, and creation of a permanent, accurate, and better collection of information. It will also have some long-term benefits: better PM will increase equipment life and will help to reduce corrective maintenance costs; Production will increase as unscheduled downtime decreases; purchase costs of parts and materials will be reduced; more effective and up-to-date record of inventory/stores reports; and better knowledge of the systems to help the company to chose those systems with the best LCC. The results have been corroborated with the application of our methodology to signal equipment in several railway network sections, as shown in this paper. Because of the successful conclusion of the project, the Spanish railway company (RENFE) and the German railway company (DB A.G.), not only decided to adopt RCM to enhance PM, but they have started a large project to implement Total Preventive Maintenance relying on the implantation of the RCM methodology.     

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.     

Maintenance strategy for tilting table of rolling mill based on reliability considerations

Reliability centred maintenance (RCM) is a new strategic framework for ensuring that any asset continues to perform, as its users want it to perform. RCM is a process used to determine the maintenance requirement of any physical asset in its operating context. RCM process entails asking seven questions about each of the selected assets. It makes use of two documents namely, RCM information worksheet and RCM decision worksheet. RCM decision diagram integrates all the decision processes into a single strategic framework. RCM concept developed by US commercial airlines industry has been successfully implemented by Military, Navy, Nuclear power plants, electric power generation and distribution undertakings and several other sectors. These projects have been carried out in the United Kingdom, The Republic of Ireland, the United States, Hong Kong, Australia, Spain and Singapore. The fact that people has enthusiastically received RCM at all levels and has enabled users to achieve some remarkable successes in all of these countries, suggests that it can be universally employed. Literature review indicates that RCM approach is not conventionally applied in process industries in India. Presently, predictive maintenance (PDM) approach along with conventional preventive maintenance is used in continuous/process industries. This approach if implemented in totality will increase the production cost to a large degree and make the production uneconomical. Similarly breakdown maintenance (BDM) approach cannot be applied in such industries as each breakdown involves huge costs. RCM approach is a compromise between PDM and BDM approach for optimising the cost and ensuring the availability of machine.The RCM approach has been applied to the tilting table system of rolling mill for the research work reported in this paper. In the present study, preventive maintenance tasks suggested for power transmission subsystem, guiding and transportation subsystem and hydraulic subsystem in tilting tables are 14 scheduled on-condition tasks, 10 scheduled on-restoration tasks, seven scheduled discard task. Whereas for 14 failure modes no scheduled maintenance has been proposed. Existing maintenance schedule for tilting tables indicates the maintenance action as and when required. Hence RCM based schedule specifies that additional preventive maintenance tasks need to be executed as compared to none initially. Cost incurred for this can be offset from the savings accrued from reduction in loss of production due to repetitive breakdowns. The methodology of RCM adopted in western industries cannot be applied as it is to Indian industries because of labour oriented nature, partially computerised information systems, non-availability of the information about cost of loss of production due to breakdown and age-reliability pattern of equipment, insufficient maintenance database. These problems can be overcome by development of sound MMIS, formulation of RCM review group and imparting suitable training to acquire the relevant skills in RCM. Thus RCM methodology can be applied to Indian industry for reduction of breakdowns as well as optimisation of preventive maintenance cost. This can further boost up the prospects of Indian industry to offer the products at globally competitive prices.     

Spare parts stock control for redundant systems using reliability centered maintenance data

In the classical approach to determine how many spare parts to stock, the spare parts shortage costs or the minimum fill rate is a key factor. A difficulty with this approach lies in the estimation of these shortage costs or the determination of appropriate minimum fill rates. In an attempt to overcome this problem, we propose to use the data gathered in reliability centered maintenance (RCM) studies to determine shortage costs. We discuss the benefits of this approach. At the same time, the approach gives rise to complications, as the RCM study determines downtime costs of the underlying equipment, which have a complex relation with the shortage cost for spare parts in case multiple pieces of equipment have different downtime costs. A further complication is redundancy in the equipment. We develop a framework that enables the modeling of these more complicated systems. Based on the framework, we propose an approximative, analytic method that can be used to determine minimum stock quantities in case of redundancy and multiple systems. In a quantitative study we show that the method performs well. Moreover, we show that including redundancy information in the stocking decision gives significant cost benefits.     

An analytical framework for reliability growth of one-shot systems

In this paper, we introduce a new reliability growth methodology for one-shot systems that is applicable to the case where all corrective actions are implemented at the end of the current test phase. The methodology consists of four model equations for assessing: expected reliability, the expected number of failure modes observed in testing, the expected probability of discovering new failure modes, and the expected portion of system unreliability associated with repeat failure modes. These model equations provide an analytical framework for which reliability practitioners can estimate reliability improvement, address goodness-of-fit concerns, quantify programmatic risk, and assess reliability maturity of one-shot systems. A numerical example is given to illustrate the value and utility of the presented approach. This methodology is useful to program managers and reliability practitioners interested in applying the techniques above in their reliability growth program.     

Component reliability specification

Building reliability into a product is costly and needs to be traded against the consequences of product unreliability. This article is the third in a series of three articles, where the first deals with optimal investment in reliability, which involves executing two tasks—(i) deciding on the reliability requirements and (ii) deciding on component specifications (SP) to achieve the desired reliability. The second article deals with the first task and in this third article, we focus on the second task.     

Cost and reliability based strategies for fatigue maintenance planning of floating structures

Floating structures are designed in such way that the appearance of fatigue failures cannot be avoided, implying the need for inspections during their life. Their maintenance has to be planned from an economic point of view so as to minimize maintenance costs but satisfying a minimum reliability level. A method is proposed to quantify the repair costs resulting of different reliability-based maintenance strategies. As an application of this approach a side shell structure typical of a floating production unit is analysed and the influence of different parameter with respect to the repair cost is also studied here.     

Interpretations of alternative uncertainty representations in a reliability and risk analysis context

Probability is the predominant tool used to measure uncertainties in reliability and risk analyses. However, other representations also exist, including imprecise (interval) probability, fuzzy probability and representations based on the theories of evidence (belief functions) and possibility. Many researchers in the field are strong proponents of these alternative methods, but some are also sceptical. In this paper, we address one basic requirement set for quantitative measures of uncertainty: the interpretation needed to explain what an uncertainty number expresses. We question to what extent the various measures meet this requirement. Comparisons are made with probabilistic analysis, where uncertainty is represented by subjective probabilities, using either a betting interpretation or a reference to an uncertainty standard interpretation. By distinguishing between chances (expressing variation) and subjective probabilities, new insights are gained into the link between the alternative uncertainty representations and probability.     

Optimizing preventive maintenance for mechanical components using genetic algorithms

This paper presents periodic preventive maintenance (PM) of a system with deteriorated components. Two activities, simple preventive maintenance and preventive replacement, are simultaneously considered to arrange the PM schedule of a system. A simple PM is to recover the degraded component to some level of the original condition according to an improvement factor which is determined by a quantitative assessment process. A preventive replacement is to restore the aged component by a new one. The degraded behavior of components is modeled by a dynamic reliability equation, and the effect of PM activities to reliability and failure rate of components is formulated based on age reduction model. While scheduling the PM policy, the PM components within a system are first identified. The maintenance cost and the extended life of the system under any activities-combination, which represents what kind of activities taken for these chosen components, are analyzed for evaluating the unit-cost life of the system. The optimal activities-combination at each PM stage is decided by using genetic algorithm in maximizing the system unit-cost life. Repeatedly, the PM scheduling is progressed to the next stage until the system's unit-cost life is less than its discarded life. Appropriately a mechatronic system is used as an example to demonstrate the proposed algorithm.     

Key attributes of the SAPHIRE risk and reliability analysis software for risk-informed probabilistic applications

The Idaho National Laboratory is a primary developer of probabilistic risk and reliability analysis (PRRA) tools, dating back over 35 years. Evolving from mainframe-based software, the current state-of-the-practice has led to the creation of the SAPHIRE software. Currently, agencies such as the Nuclear Regulatory Commission, the National Aeronautics and Aerospace Agency, the Department of Energy, and the Department of Defense use version 7 of the SAPHIRE software for many of their risk-informed activities. In order to better understand and appreciate the power of software as part of risk-informed applications, we need to recall that our current analysis methods and solution methods have built upon pioneering work done 30–40 years ago. We contrast this work with the current capabilities in the SAPHIRE analysis package. As part of this discussion, we provide information for both the typical features and special analysis capabilities, which are available. We also present the application and results typically found with state-of-the-practice PRRA models. By providing both a high-level and detailed look at the SAPHIRE software, we give a snapshot in time for the current use of software tools in a risk-informed decision arena.     

Reliability-centered predictive maintenance scheduling for a continuously monitored system subject to degradation

This paper tries to integrate sequential imperfect maintenance policy into condition-based predictive maintenance (CBPM). A reliability-centered predictive maintenance policy is proposed for a continuously monitored system subject to degradation due to the imperfect maintenance. It is assumed that the system hazard rate is a known function of the system condition and then can be derived directly through CBPM. A hybrid hazard rate recursion rule based on the concept of age reduction factor and hazard rate increase factor is built up to predict the evolution of the system reliability in different maintenance cycles. Whenever the system reliability reaches the threshold R, an imperfect preventive maintenance (PM) is performed on the system. The optimal reliability threshold R is determined by minimizing the cumulative maintenance cost per unit time in the residual life of the system which is based on simulation. Finally, a discussion is presented to show how the optimal results depend on the different cost parameters.     

Closed-form analytical results for condition-based maintenance

Preventive maintenance is applied to improve the device availability or decrease the repair costs when the device failures are in deterioration (or aging) phase. Preventive maintenance can be made more efficient by periodic monitoring wherein the state of deterioration can be assessed. This leads to the notion of condition-based maintenance. In this paper, we study the condition-based maintenance, and derive closed-form expressions of system availability when the device undergoes both deterioration as well as Poisson type failures. These closed-form solutions enable us to find faster algorithms to determine the optimal inspection policy.