Engineering maintainability : how to design for reliability and easy maintenance

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Here are 8 factors to consider when designing a system that will require maintenance.

RAM are inherent product or system attributes that should be considered throughout the development lifecycle. The discussion in this section relies on a standard developed by a joint effort by the Electronic Industry Association and the U. Government and adopted by the U. Department of Defense GEIA that defines 4 processes: understanding user requirements and constraints, design for reliability, production for reliability, and monitoring during operation and use discussed in the next section.

Understanding user requirements involves eliciting information about functional requirements, constraints e. From these emerge system requirements that should include specifications for reliability, maintainability, and availability, and each should be conditioned on the projected operating environments. RAM requirements definition is as challenging but as essential to development success as is the definition of general functional requirements.

System designs based on user requirements and system design alternatives can then be formulated and evaluated.. Reliability engineering during this phase seeks to increase system robustness through measures such as redundancy, diversity, built-in test, advanced diagnostics, and modularity to enable rapid physical replacement. In addition, it may be possible to reduce failure rates through measures such as use of higher strength materials, increasing the quality components, moderating extreme environmental conditions, or shortened maintenance, inspection, or overhaul intervals.

Design analyses may include mechanical stress, corrosion, and radiation analyses for mechanical components, thermal analyses for mechanical and electrical components, and Electromagnetic Interference EMI analyses or measurements for electrical components and subsystems. In most computer based systems, hardware mean time between failures are hundreds of thousands of hours so that most system design measures will be to increase system reliability are focused on software. The most obvious way to improve software reliability is by improving its quality through more disciplined development efforts and test.

Methods for doing so are in the scope of software engineering but not in the scope of this section. However, reliability and availability can also be increased through architectural redundancy, independence, and diversity. Redundancy must be accompanied by measures to ensure data consistency, and managed failure detection and switchover.

Within the software architecture, measures such as watchdog timers, flow control, data integrity checks e. System RAM characteristics should be continuously evaluated as the design progresses. Where failure rates are not known as is often the case for unique or custom developed components, assemblies, or software , developmental testing may be undertaken assess the reliability of custom-developed components. Markov models and Petri nets are of particular value for computer-based systems that use redundancy. Evaluations based on qualitative analyses assess vulnerability to single points of failure, failure containment, recovery, and maintainability.

Analyses from related disciplines during design time also affect RAM. Human factor analyses are necessary to ensure that operators and maintainers can interact with the system in a manner that minimizes failures and the restoration times when they do occur. There is also a strong link between RAM and cybersecurity in computer based systems. On the one hand defensive measures reduce the frequency of failures due to malicious events. Many production issues associated with RAM are related to quality. The most important of these are ensuring repeatability and uniformity of production processes and complete unambiguous specifications for items from the supply chain.

Other are related to design for manufacturability, storage, and transportation Kapur, ; Eberlin Large software intensive systems information systems are affected by issues related to configuration management, integration testing, and installation testing. Depending on organizational considerations, this may be the same or a separate system as used during the design. After systems are fielded, their reliability and availability to assess whether system or product has met its RAM objectives, to identify unexpected failure modes, to record fixes, to assess the utilization of maintenance resources, and to assess the operating environment.

In order to assess RAM, it is necessary to maintain an accurate record not only of failures but also of operating time and the duration of outages. Systems that report only on repair actions and outage incidents may not be sufficient for this purpose. An organization should have an integrated data system that allows reliability data to be considered with logistical data, such as parts, personnel, tools, bays, transportation and evacuation, queues, and costs, allowing a total awareness of the interplay of logistical and RAM issues.

These issues in turn must be integrated with management and operational systems to allow the organization to reap the benefits that can occur from complete situational awareness with respect to RAM. Reliability Testing can be performed at the component, subsystem, and system level throughout the product or system lifecycle.

Stability tests: Stability tests are life tests for integrated hardware and software systems. The goal of such testing is to determine the integrated system failure rate and assess operational suitability. Test conditions must include accurate simulation of the operating environment including workload and a means of identifying and recording failures.

Because of its potential impact on cost and schedule, reliability testing should be coordinated with the overall system engineering effort. Test planning considerations include the number of test units, duration of the tests, environmental conditions, and the means of detecting failures. True RAM models for a system are generally never known. Data on a given system is assumed or collected, used to select a distribution for a model, and then used to fit the parameters of the distribution.

This process differs significantly from the one usually taught in an introductory statistics course. First, the normal distribution is seldom used as a life distribution, since it is defined for all negative times. Second, and more importantly, reliability data is different from classic experimental data. Reliability data is often censored, biased, observational, and missing information about covariates such as environmental conditions. Data from testing is often expensive, resulting in small sample sizes.

These problems with reliability data require sophisticated strategies and processes to mitigate them. In most large programs, RAM experts report to the system engineering organization. At project or product conception, top level goals are defined for RAM based on operational needs, lifecycle cost projections, and warranty cost estimates. These lead to RAM derived requirements and allocations that are approved and managed by the system engineering requirements management function. RAM testing is coordinated with other product or system testing through the testing organization, and test failures are evaluated by the RAM function through joint meetings such as a Failure Review Board.

In some cases, the RAM function may recommend design or development process changes as a result of evaluation of test results or software discrepancy reports, and these proposals must be adjudicated by the system engineering organization, or in some cases, the acquiring customer if cost increases are involved. Once a system is fielded, its reliability and availability should be tracked. Such a system captures data on failures and improvements to correct failures.

This database is separate from a warranty data base, which is typically run by the financial function of an organization and tracks costs only. Unfortunately, the lack of careful consideration of the backward flow from decision to analysis to model to required data too often leads to inadequate data collection systems and missing essential information. Proper prior planning prevents this poor performance. Of particular importance is a plan to track data on units that have not failed.

Units whose precise times of failure are unknown are referred to as censored units. Inexperienced analysts frequently do not know how to analyze censored data, and they omit the censored units as a result. This can bias an analysis. Because of the importance of reliability, availability, and maintainability, as well as related attributes, there are hundreds of standards associated. Some are general but more are specific to domains such as automotive, aviation, electric power distribution, nuclear energy, rail transportation, software, and many others.

Standards are produced by both governmental agencies and professional associations, and international standards bodies such as. The following table lists selected standards from each of these agencies.

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Because of differences in domains and because many standards handle the same topic in slightly different ways, selection of the appropriate requires consideration of previous practices often documented as contractual requirements , domain specific considerations, certification agency requirements, end user requirements if different from the acquisition or producing organization , and product or system characteristics. Becoming a reliability engineer requires education in probability and statistics as well as the specific engineering domain of the product or system under development or in operation.

A number of universities throughout the world have departments of reliability engineering which also address maintainability and availability and more have research groups and courses in reliability and safety — often within the context of another discipline such as computer science, system engineering, civil engineering, mechanical engineering, or bioengineering.

Because most academic engineering programs do not have a full reliability department, most engineers working in reliability have been educated in other disciplines and acquire the additional skills through additional coursework or by working with other qualified engineers. However, only a minority of engineers working in the discipline have this certification. Reliability can be characterized in terms of the parameters, mean, or any percentile of a reliability distribution. However, in most cases, the exponential distribution is used, and a single value, the mean time to failure MTTF for non-restorable systems, or mean time between failures MTBF for restorable systems are used.

The metric is defined as. Maintainability is often characterized in terms of the exponential distribution and the mean time to repair and be similarly calculated, i. Where is the total down time and noutages is the number of outages. As was noted above, accounting for downtime requires definitions and specificity. Down time might be counted only for corrective maintenance actions, or it may include both corrective and preventive maintenance actions.

Where the lognormal rather than the exponential distribution is used, a mean down time can still be calculated, but both the log of the downtimes and the variance must be known in order to fully characterize maintainability. As was the case with maintainability, availability may be qualified as to whether it includes only unplanned failures and repairs inherent availability or downtime due to all causes including administrative delays, staffing outages, or spares inventory deficiencies operational availability.

Probabilistic metrics describe system performance for RAM. Quantiles, means, and modes of the distributions used to model RAM are also useful. Availability has some additional definitions, characterizing what downtime is counted against a system. For inherent availability , only downtime associated with corrective maintenance counts against the system. For achieved availability , downtime associated with both corrective and preventive maintenance counts against a system. Finally, operational availability counts all sources of downtime, including logistical and administrative, against a system.

Availability can also be calculated instantaneously, averaged over an interval, or reported as an asymptotic value. Asymptotic availability can be calculated easily, but care must be taken to analyze whether or not a systems settles down or settles up to the asymptotic value, as well as how long it takes until the system approaches that asymptotic value.

It is defined as the partial derivative of the system reliability with respect to the reliability of a component. Criticality is a guide to prioritizing reliability improvement efforts. Many of these metrics cannot be calculated directly because the integrals involved are intractable. They are usually estimated using simulation. There are a wide range of models that estimate and predict reliability Meeker and Escobar System models are used to 1 combine probabilities or their surrogates, failure rates and restoration times, at the component level to find a system level probability or 2 to evaluate a system for maintainability, single points of failure, and failure propagation.

The three most common are reliability block diagrams, fault trees, and failure modes and effects analyses. There are more sophisticated probability models used for life data analysis. These are best characterized by their failure rate behavior, which is defined as the probability that a unit fails in the next small interval of time, given it has lived until the beginning of the interval, and divided by the length of the interval.

Models can be considered for a fixed environmental condition. They can also be extended to include the effect of environmental conditions on system life. Such extended models can in turn be used for accelerated life testing ALT , where a system is deliberately and carefully overstressed to induce failures more quickly. The data is then extrapolated to usual use conditions. This is often the only way to obtain estimates of the life of highly reliable products in a reasonable amount of time Nelson Also useful are degradation models , where some characteristic of the system is associated with the propensity of the unit to fail Nelson As that characteristic degrades, we can estimate times of failure before they occur.

Subsection 3. As stated above, RAM engineers are accustomed to focus on the hierarchical function structure, since failure can generally be described as the termination or loss of functions and each function could be analyzed independently. Complex systems are better served by the SE suite of tools to systematically develop a vision of behaviors, interfaces, elements, and control structure for a new subsea system.

Functional decomposition as a static representation of the hierarchy structure of functions is often adopted by RAM analysts to become familiar with the system concept. The dependencies are not explicitly highlighted in functional decomposition. This semantic aligns the structure of activity diagrams with that of Petri nets accepted in RAM community, although the activity diagram is more concise than standard Petri nets, especially when it comes to modeling the reactivity of workflow.

It consists of potential states and triggering events that drive the transition between states. The state diagram resembles Markov chains, perferred in RAM community on the surface, but with the distinction that Markov chains as the formal model based on strict mathmatical framework represent less content state diagrams. For instance, when transferring a state diagram to Markov chains for quantitative modeling, sychronization and parallelization of state diagram are abstracted away. Activity diagrams based on flow of control are better used for modelling a process of operation, whereas the state diagram emphasizes events.

They are other models that are not covered in SysML that also support functional analysis. Solely relying on functional architecture to analyze RAM performance of complex systems could be superfical and incomplete, as it only assists in identifying potential failure and repair events but not the associated cause and consequence. Therefore, the physical architecture of a design concept should be developed. The physical architecture analysis defines the components that realize the identified functions. Depending on the role RAM analysts have in the design phase, a technical system is generally considered from a functional instead of architecture point of view.

However, it shall not be the case for new subsea design. However, such breakdown structure does not help in the context of complex system as many parts are interrelated and ought not be analyzed individually. Often times, studying physical aspects in RAM community is a brainstorming process that requires participations from multiple disciplines, for example, Hazard and Operability analysis HAZOP. Few methods are proposed to exclusively incorporate physical properties in framing RAM aspects. Pioneering works have been encountered in the aviation industry, where the method zonal analysis is proposed to highlight the impact of proximity in Common Cause Failure CCF modelling.

For example, the leakage of a pipeline can cause gradual contamination in neighboring areas. In such practice, building physical models of a subsea system can ensure coverage and traceability of defined constraints and assumptions eg, height, width, mass, and the like. However, relying on the requirement table provided in SysML only gives an indication about constraints. The complete architecture analysis can assist in understanding how the local effects on basic components can disturb the system and updating stochastic descriptions of unwanted events, together with expert judgments and experienced practices, for example, using finite element method to study the failure rate of a pipeline considering the effect of sand, fluid composition, ambient temperature, and pressure.

Additional attention should be paid to system structure, that is, the modularity in subsea design environment. Some subsea functions are realized by components located within different modules, but the replacement takes place at a module level. Design structure matric DSM is rather a straightforward modeling technique to handle the modularity replacement problem.

DSM is efficient in organizing the interactions between components and visualizing the shared patterns, and it can help designers to identify the relatively independent modules, and support some tasks such as RAM allocation. Multiple conflict objectives are typical in an engineering design process. For example, the choice of materials to guard against internal corrosion in a pipeline may improve the reliability but may reduce the efficiency of production ie, OPEX.

Decisions are needed to find a balanced solution considering all the assumptions and constraints. The relevant techniques for trade analysis have already been discussed in Refs. However, one should remember that quantification of all the factors identified in the dysfunctional analysis is nearly impossible. Establishing a set of scenarios eg, accidental scenarios and maintenance scenarios is always considered as the supplement to communicate the implications on design. The subjective judgments are largely implemented in such analysis.

Step 1: Operational analysis. The operational analysis introduced here takes place alongside requirement analysis introduced in Subsection 3. It covers the identification of interactions, environment, and boundaries of the system for an overall view but offers only an abstract conceptual view of the design. The main objective is to systematically formulate RAM and functional requirements of a system, based on the needs of identified stakeholders. Step 2: Design analysis. Hereafter, we use the term design analysis to cover both functional and architectural analysis introduced in Subsection 3.

Design analysis assists in the systematic establishment of the design concept and supports the effort to understand and organize the system structure. The advantage for having design analysis is to efficiently eliminate the inconsistency caused by the variations in competence, knowledge base, and experience of RAM analysts.

Step 3: RAM analysis. After defining the static system structure that explains how the components are distributed and connected, RAM methods are reorganized to simulate how the potential occurrences of events eg, failure, test, repair… affect the states of the structure eg, parts, modules, configuration…. As always, the proposed methods in the framework should be updated or replaced based on the real analysis of needs.

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Step 4: Joint concept analysis. Some scenarios generated by RAM analysis may imply modifications of the existing design concept. For example, lifecycle cost analysis, sensitivity analysis, and technology evaluation must be conducted in this step. Step 5: Communication. Communication is indispensable to link the separate contributions of design teams. Effective communication should take place to ensure that all stakeholders understand the basis on which decisions are made and the rationale behind.

Reliability, Availability, and Maintainability

Every revision should be registered as a design risks until it is validated. Adaptations must be made considering subsea specific issues. The accuracy and validity of flow measurement are very important for contractual obligation between custody transfer parties eg, consumer and supplier. The main advantage is that USM has no moving parts so the maintenance requirement is rather low. The sampling module includes sampling devices QS and pumps. When the oil exported from subsea storage passes the sampling module, a representative amount of oil is extracted by sample probe.

The pumps are installed to provide sufficient power for lifting the sample to the dedicated facility located topside via umbilical. The installation of multiple USMs enhances the ability of monitoring the quality of meters and reduces the measurement uncertainty if the resulted measurement is the average of readings from different USMs. The metering module is considered as fully functional when two flow meters are available, where the spare meter can serve as duty or master when needed.

The control system is located on topside to control the operation of sampling module and metering module. Subsea electronic unit SEU is installed to distribute the necessary coded control command to each instrument and collect the data for further transmission to other subsea units or control system. The validity and accuracy of signals from USM, PT, and TT may lessen after installation due to various factors such as outdated calibration, bad piping conditions, and physical damage of parts.

This design concept is assumed to function in spite of failed PT and TT, since the loss of pressure and temperature measurement can be compensated by other transmitters adjusted by calculations. When there is a need to replace the USM, the metering station should be lifted through the rig and recalibrated at the accredited calibration laboratory. Replacement of USM causes an interruption of production as the downtime of metering station is significant. This design concept includes many parts including PT, TT, valve connection, and tubing that have been qualified for subsea applications, except the USM.

The major need from stakeholders is to ensure the accuracy of USM readings against potential deterioration and expected variations from externals. In addition, environmental conditions on metering site eg, ambient temperature and pressure, humidity , piping arrangement and thickness, and power and signal interfaces with electronic units, all can impact the performance of USMs.

These functional requirements result in upgrading or detailing the existing design concept. For instance, the uninterrupted power unit may be needed by the flow computer to avoid possible power outages that cause the loss of data. The Norwegian measurement regulation requires the uncertainty to be less than 0.

Measuring Reliability

Given the analysis of current laboratory result, the uncertainty of this design concept is estimated to be less than 0. Configuration 1 clearly offers higher operational flexibility as the SEU is fully redundant for each USM, at the same time introducing more complexity to the system due to the increasing number of jumpers.

The failure of jumpers can cause jammed, interrupted, or missing signals, which can immediately cause an increase of measurement uncertainty and the need for maintenance. The maintenance of USM assembly includes several tasks such as full isolation of the metering station from the pipeline, removal of hydrocarbon in the units of metering station and lift of whole metering station through the rig. The length of downtime related to maintenance activities of USM assembly is assumed as 2 months ie, hours. The faulty SEU and jumpers ie, flexible connection between units can be restored in 1 week ie, hours after two signals from USM are lost.

Considering the expensive retrieval and intervention, the maintenance requirement agreed by stakeholders is that retrieval for calibration and adjustment is not required during the lifetime of the system ie, 20 years. Consequently, a degraded performance of the flow metering module may be acceptable, which means operator may not immediately shutdown the flow metering module if two out of three USM outputs are lost. Assuming that uncertainty contributions from each USM are uncorrelated, the resulting measurement uncertainty approximately equals the reciprocal of the square root of the number of meters.

For instance, if the measurement uncertainty is estimated as 0. To compare various maintenance strategies for USM assembly, the three possible maintenance strategies are as follows given the considerations from system designer: Strategy I: The activities related to maintenance starts immediately when two USM functions are affected, the metering station is shut down during maintenance. Strategy II: The activities related to maintenance postpone 1 year ie, hours when two USM functions are affected, the metering station is shut down during maintenance.


At the end of lifetime ie, the last 5 years before intervention , it is acceptable to operate metering station with only one USM. The three maintenance strategies imply different RAM performances for the given design concept. The insights to maintenance management had not been discussed in the prior versions of the design proposal from Statoil, 44 as it required participation of RAM analysts to build up a RAM model to simulate system responses under different maintenance strategies.

This work requires the design analysis to study the system behavior for different configurations and under different maintenance strategies, which is elaborated in Subsection 5. Considering two possible configurations and three different maintenance strategies, there are six cases in total for evaluation.


The selection of design concept should consider the maintenance and spare parts costs related to the revealed failure modes and the risk for loss of profit and income related to measurement uncertainty, where all the losses are converted into a monetary unit, that is, Norwegian kroner NOK. The result is briefly discussed in Subsection 5. The system is initially in the working state, where the measurement uncertainty is 0.

When one USM is lost, the system reaches minor degradation state and the measurement uncertainty is increased to 0. When two USMs are lost, the system reaches the major degradation state and the measurement uncertainty is increased to 0. When the system reaches this state, the maintenance event may be planned immediately strategy I , or postponed with acceptance to operate under severe degradation strategy II , or ignored, when in the later phase of operation strategy III.

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  5. When all USMs are lost, the system must shutdown and prepare for maintenance immediately. After maintenance, the faulty USM are replaced ie, as good as new and metering station is restored to working operation state. The state diagrams for SEUs and jumpers can be established in the similar fashion. The functional dependencies between SEU, jumper, and USM can be established by synchronizing the transitions, see details in Subsection 5.

    The state diagram clarifies the possible events, system states and associated transitions, which helps RAM analysts to correctly define the relevant modeling elements, that is, the required actors of normal operation and maintenance and conditions for retrieval processes. The functional dependences can be highlighted by employing such state space modeling, which is beyond the traditional analysis for hierarchy based analytical reduction such as functional trees or physical breakdowns. The architectural aspects are obtained through design analysis in order to provide insight on the causes and consequence of hazards and the suitability of associated countermeasures.

    The physical attributes eg, dimensions, materials, component quality, manufacture process, and locations may impact system behavior. For instance, the location of metering should be distant from control valves, as the noise of valve operation can interfere with USM measurement. The identification of architecture for given system concept assists in following RAM analysis, especially for dysfunctional analysis as shown in Subsection 5.

    The general assumptions and constraints are made on the basis of both design analysis and operational analysis as follows, and they are valid for all cases to be evaluated: For each USM, SEU and jumper only consider two states: faulty and working. The sensor lines are continuously checked, thus the delay for detecting failures on jumper and SEU can be ignored. All components are considered as good as new after maintenance. The activities of maintenance are considered as perfect, thus no adverse effects are induced. Ideally, the subsea operator does not expect any retrieval during the operation until the metering system cannot perform the function as intended.

    There are many suitable approaches for the following quantitative analysis, for example, Petri nets. The number of valid USM input is used to determine when to start maintenance and the uncertainty increment. For instance, case 1 follows maintenance strategy I and then the maintenance of USM assembly is planned when two valid USM inputs are lost. Applying strategy II cases 2 and 5 needs less maintenance than applying strategy I cases 1 4 by paying the price of allowing an increase in measurement uncertainty.

    The downtime due to maintenance is significantly reduced compared to strategies I and II for configuration 1 cases 1 and 2 , however, not for configuration 2 cases 4 and 5. As result, the measurement uncertainty is decreased. The objective of joint concept analysis is to present some common themes that cannot be solved or considered by any individual engineering discipline. These considerations may either require designers to reevaluate the system concept, or RAM analysts to reconstruct the RAM model to achieve more realistic design implications.

    For example, the maintainability analysis shows that it is necessary to consider the separation between measurement instruments and sampling systems. Therefore, DSM is required for design analysis for mastering the interaction between these two modules and subsequent RAM analysis. Another example could be CCF assessment. In this case study, common failure mode for USMs is mainly the deposits, for example, wax.

    The designer indicated that the implemented measure is to heat the flow, thus prevent wax formation. The result of previous RAM analysis gives indications for two cost functions in lifecycle analysis: the total cost for maintenance including resource mobilization and spare parts, and the profit loss due to system downtime and measurement uncertainty. For instance, in this case study, the net present value of oil in subsea storage is assumed as billion NOK and direct costs to replace the USM assembly is estimated as 25 million NOK.

    The result of cost analysis shows that case 1 saves the most. Compared to the most costly case 2, case 1 can save 4. In this case study, by performing operational analysis and design analysis, RAM analysts can easily identify what is beyond the normal operations viewpoint and clarify the assumptions and simplifications for RAM modeling. In this case study, by starting with operational analysis, the issue to be investigated is specified: the impact of maintenance strategies and configurations.

    Design analysis identifies the functional and architectural aspects behind the issue: the system behavior ie, states and transitions of selected configurations under different maintenance strategies. The information can be used to construct a RAM model and the numerical results through simulation can be used for selection of design alternatives.

    It has become apparent that incorporating RAMS aspects as early as possible gives several advantages in form of engineering efforts and budgets. Many companies involved in subsea development have their procedures for framing RAM in design but they still claim that they are not adequate. The similar problem already exists in many industry sectors such as nuclear, satellite, and aviation, where the problem is further amplified by the complexity of design solutions.

    Reliability and Maintainability in Operations Management

    This article selects subsea design as the starting point. Analysts in this context, often dive into RAM analysis before correctly stating the system concept. Development of a system concept by RAM techniques relies on competence, experience, and the knowledge base of analysts, which often results in inconsistency and misunderstandings. Without a more holistic framing, RAM in subsea design has limited possibility to give systematic insight of the design concept, making it necessary to integrate other disciplines to complete industry practice.

    Analysis based on the SE suite of tools could be a prerequisite for specialty analysis like RAM analysis to reduce the risk of working from an inconsistent and incorrect system concept. Then, system designers can correctly capture the indications derived from RAM analysis conducted in a systematic and iterative manner.

    Maintainability, Maintenance, and Reliability for Engineers - CRC Press Book

    This framework serves as a baseline for further refinement in order to direct future effort to improve the process of framing RAM in subsea design. This said, the design analysis and RAM analysis are conducted in sequence thus some overlaps may be latent as system theory or system thinking is indirectly placed in conducting RAM analysis. Additional research could develop RAM methods directly using system theory. One such pioneer work has been completed by Leveson 15 who use system theory to create a new accident model used for safety analysis.

    However, similar work has not been found in RAM domain yet. Moreover, the application is here only demonstrated within subsea design. One remaining work of this article can be to expand the analysis to consider other sectors to enrich the content of the proposed framework and hopefully bring ideas for transfer of knowledge from this article to other domains of interest.

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    The authors gratefully acknowledge the project support, which is financed by the Research Council of Norway, major industry partners and NTNU. Special thanks to our colleague, Antoine Rauzy, who continuously contributes to this body of knowledge and provides inspirations on current issues facing both the RAM and SE communities. The authors are also grateful for the valuable comments and useful suggestions of two anonymous reviewers. Juntao Zhang is a Ph. His research interest is in the area of incorporating reliability and availability in the early phase of subsea design process. Cecilia Haskins is an American living and working in Norway and blending the best of both cultures into her personal and professional life.

    She has been recognized as a Certified Systems Engineering Professional since After earning her Ph. Before that, he had worked as a post doctor fellow in the same department from to His main research interests include system reliability and resilience engineering, safety critical systems and risk management.

    Most of the publications appear in the recent 5 years. He is also serving the academics as the reviewer for more than 20 international journals and the technical committee member for more than 10 international conferences. Liu is the coordinator and main lecturer for two master courses in NTNU, and he also serves as the director of the international master program of RAMS reliability, availability, maintainability and safety. He has deeply involved in several research projects funded by Norwegian research council, NTNU and other institutions.

    In addition, Dr. Liu has a robust research network with universities in different 10 countries. Before starting on her PhD, she worked for several years in industry, including as instrumentation engineer onshore and automation and electrical supervisor offshore in Phillips Petroleum, automation leader at the factory of Nidar, and senior researcher at SINTEF, department for applied cybernetics. She is also a member of IEC committee who maintains the standard on functional safety for process industry sector. She has also been contributing to a high number of studies for industry companies.

    Volume 21 , Issue 6. The full text of this article hosted at iucr. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username. Systems Engineering Volume 21, Issue 6. Juntao Zhang Corresponding Author E-mail address: juntao.

    Engineering maintainability : how to design for reliability and easy maintenance Engineering maintainability : how to design for reliability and easy maintenance
    Engineering maintainability : how to design for reliability and easy maintenance Engineering maintainability : how to design for reliability and easy maintenance
    Engineering maintainability : how to design for reliability and easy maintenance Engineering maintainability : how to design for reliability and easy maintenance
    Engineering maintainability : how to design for reliability and easy maintenance Engineering maintainability : how to design for reliability and easy maintenance
    Engineering maintainability : how to design for reliability and easy maintenance Engineering maintainability : how to design for reliability and easy maintenance
    Engineering maintainability : how to design for reliability and easy maintenance Engineering maintainability : how to design for reliability and easy maintenance
    Engineering maintainability : how to design for reliability and easy maintenance Engineering maintainability : how to design for reliability and easy maintenance
    Engineering maintainability : how to design for reliability and easy maintenance Engineering maintainability : how to design for reliability and easy maintenance
    Engineering maintainability : how to design for reliability and easy maintenance

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