11 4.2 Reliability prediction

41 Reliability engineering is a special discipline within Systems engineering. Reliability engineers rely heavily on statistics, probability theory, and reliability theory to set requirements, measure or predict reliability and advice on improvements for reliability performance. Many engineering techniques are used in reliability engineering, such as Reliability Hazard analysis, Failure mode and effects analysis (FMEA), Fault tree analysis, Reliability Prediction, Weibull analysis, thermal management, reliability testing and accelerated life testing. Because of the large number of reliability techniques, their expense, and the varying degrees of reliability required for different situations, most projects develop a reliability program plan to specify the reliability tasks that will be performed for that specific system.

75 Reliability prediction

76 Reliability prediction is the combination of the creation of a proper reliability model together with estimating (and justifying) the input parameters for this model (like failure rates for a particular failure mode or event and the mean time to repair the system for a particular failure) and finally to provide a system (or part) level estimate for the output reliability parameters (system availability or a particular functional failure frequency).

77 Some recognized authors on reliability - e.g. Patrick O'Conner, R. Barnard and others - have argued that too many emphasis is often given to the prediction of reliability parameters and more effort should be devoted to prevention of failure. The reason for this is that prediction of reliability based on historic data can be very misleading, because a comparison is only valid for exactly the same designs, products under exactly the same loads

78 context. Even a minor change in detail in any of these could have major effects on reliability. Furthermore, normally the most unreliable and important items (most interesting candidates for a reliability investigation) are most often subjected to many modifications and changes. Also, to perform a proper quantitative reliability prediction for systems is extremely difficult and expensive if done by testing. On part level results can be obtained often with higher confidence as many samples might be used for the available testing financial budget, however unfortunately these tests might lack validity on system level due to the assumptions that had to be made for part level testing. Testing for reliability should be done to create failures, learn from them and to improve the system

79 part. The general conclusion is drawn that an accurate and an absolute prediction - by field data comparison or testing - of reliability is in most cases not possible. A exception might be failures due to wear-out problems like fatigue failures. Mil. Std. 785 writes in its introduction that reliability prediction should be used with great caution if not only used for comparison in trade-off studies.

80 Furthermore, based on latest insights in Reliability centered maintenance (RCM), most (complex) system failures do no occur due to wear-out issues (e.g. a number of 4% has been provided, refer to RCM page). The failures are often a result of combinations of more and multi-type events or failures. The results of these studies have shown that the majority of failures follow a constant failure rate model, for which prediction of the value of the parameters is often problematic and very time consuming (for a high level reliability - part level). Testing these constant failure rates at system level, by for example mil. handbook 781 type of testing, is not practical and can be extremely misleading.

81 Despite all the concerns, there will always be a need for the prediction of reliability.These numbers can be used as a Key performance indicator (KPI) or to estimate the need for spares, man-power, availability of systems, etc.

83 Help assess the effect of product reliability on the maintenance activity and on the quantity of spare units required for acceptable field performance of any particular system. For example, predictions of the frequency of unit level maintenance actions can be obtained. Reliability prediction can be used to size spare populations.

91 The telecommunications industry has devoted much time over the years to concentrate on developing reliability models for electronic equipment. One such tool is the Automated Reliability Prediction Procedure (ARPP), which is an Excel-spreadsheet software tool that automates the reliability prediction procedures in SR-332, Reliability Prediction Procedure for Electronic Equipment. FD-ARPP-01 provides suppliers and manufacturers with a tool for making Reliability Prediction Procedure (RPP) calculations. It also provides a means for understanding RPP calculations through the capability of interactive examples provided by the user.

95 devices. The RPP is aimed primarily at reliability prediction of units.

106 The parts stress modelling approach is an empirical method for prediction based on counting the number and type of components of the system, and the stress they undergo during operation.

availability

1 Reliability engineering is an engineering field, that deals with the study, evaluation, and life-cycle management of reliability: the ability of a system or component to perform its required functions under stated conditions for a specified period of time. It is often measured as a probability of failure or a measure of availability. However, maintainability is also an important part of reliability engineering.

65 "mission" reliability as well as the safety of a system can be increased. This is common practice in aerospace systems that need continues availability and do not have a fail safe mode (e.g. flight computers and related steering systems). However, the "basic" reliability of the system will in this case still be lower. Basic reliability refers to failures that might not result in system failure, but do result in maintenance actions, logistic cost, use of spares, etc.

68 A reliability program plan (RPP) is used to document exactly what "best practices" (tasks, methods, tools, analyses, and tests) are required for a particular (sub)system, as well as clarify customer requirements for reliability assessment. For large scale, complex systems, the Reliability Program Plan is a distinctive document. For simple systems, it may be combined with the systems engineering management plan or an integrated logistics support management plan. A reliability program plan is essential for a successful reliability, availability, and maintainability (RAM) program and is developed early during system development, and refined over the systems life-cycle. It specifies not only what the reliability engineer does, but also the tasks performed by other stakeholders. A reliability program plan is approved by top program management, who is responsible for identifying resources for its implementation.

69 Technically, often, the main objective of a Reliability Program Plan is to evaluate and improve availability of a system and not reliability. Reliability needs to be evaluated and improved related to both availability and the cost of ownership (due to spares costing, maintenance man-hours, transport etc. costs). Often a trade-off is needed between the two. There might be a maximum ratio between availability and cost of ownership. If availability or Cost of Ownership is more important depends on the use of the system (e.g. a system that is a critical link in a production system - for exampl e a big oil platform - is normally allowed to have a very high cost of ownership if this translates to even a minor higher availability as the unavailability of the platform directly results in a massive loss of revenue). Testability of a system should also be addressed in the plan as this is the link between reliability and maintainability. The maintenance (the maintenance concept

70 strategy) can influence the reliability of a system (e.g. by preventive maintenance) - although it can never bring it above the inherent reliability. Maintainability influences the availability of a system - in theory this can be almost unlimited if one would be able to repair a failure in a very short time.

71 A proper reliability plan should normally always address RAMT analysis in its total context. RAMT stands in this case for Reliability, Availability, Maintainability (and maintenance) and Testability in context to users needs to the technical requirements (as translated from the needs).

73 For any system, one of the first tasks of reliability engineering is to adequately specify the reliability and maintainability requirements, as defined by the stakeholders in terms of their overall availability needs. Reliability requirements address the system itself, test and assessment requirements, and associated tasks and documentation. Reliability requirements are included in the appropriate system

76 Reliability prediction is the combination of the creation of a proper reliability model together with estimating (and justifying) the input parameters for this model (like failure rates for a particular failure mode or event and the mean time to repair the system for a particular failure) and finally to provide a system (or part) level estimate for the output reliability parameters (system availability or a particular functional failure frequency).

81 Despite all the concerns, there will always be a need for the prediction of reliability.These numbers can be used as a Key performance indicator (KPI) or to estimate the need for spares, man-power, availability of systems, etc.

84 Provide necessary input to system-level reliability models. System-level reliability models can subsequently be used to predict, for example, frequency of system outages in steady-state, frequency of system outages during early life, expected downtime per year, and system availability.

121 Design For Reliability (DFR), is an emerging discipline that refers to the process of designing reliability into designs. This process encompasses several tools and practices and describes the order of their deployment that an organization needs to have in place to drive reliability and improve maintainability in products, towards a objective of improved availability, lower sustainment costs, and maximum product utilization or lifetime. Typically, the first step in the DFR process is to establish the system’s availability requirements. Reliability must be "designed in" to the system. During system design, the top-level reliability requirements are then allocated to subsystems by design engineers, maintainers, and reliability engineers working together.

engineer

4 Most industries do not have specialized reliability engineers and the engineering task often becomes part of the tasks of a design engineer, logistics engineer, systems engineer or quality engineer. Reliability engineers should have broad skills and knowledge.

43 customers. These tasks are normally managed by a reliability engineer or manager, who may hold an accredited engineering degree and has additional reliability-specific education and training.

68 A reliability program plan (RPP) is used to document exactly what "best practices" (tasks, methods, tools, analyses, and tests) are required for a particular (sub)system, as well as clarify customer requirements for reliability assessment. For large scale, complex systems, the Reliability Program Plan is a distinctive document. For simple systems, it may be combined with the systems engineering management plan or an integrated logistics support management plan. A reliability program plan is essential for a successful reliability, availability, and maintainability (RAM) program and is developed early during system development, and refined over the systems life-cycle. It specifies not only what the reliability engineer does, but also the tasks performed by other stakeholders. A reliability program plan is approved by top program management, who is responsible for identifying resources for its implementation.

115 Reliability test requirements can follow from any analysis for which the first estimate of failure probability, failure mode or effect needs to be justified. Evidence can be generated with some level of confidence by testing. With software-based systems, the probability is a mix of software and hardware-based failures. Testing reliability requirements is problematic for several reasons. A single test is in most cases insufficient to generate enough statistical data. Multiple tests or long-duration tests are usually very expensive. Some tests are simply impractical, and environmental conditions can be hard to predict over a systems life-cycle. Reliability engineering is used to design a realistic and affordable test program that provides enough evidence that the system meets its reliability requirements. Statistical confidence levels are used to address some of these concerns. A certain parameter is expressed along with a corresponding confidence level: for example, an MTBF of 1000 hours at 90% confidence level. From this specification, the reliability engineer can for example design a test with explicit criteria for the number of hours and number of failures until the requirement is met or failed. Other type tests are also possible.

160 A key aspect of reliability testing is to define "failure". Although this may seem obvious, there are many situations where it is not clear whether a failure is really the fault of the system. Variations in test conditions, operator differences, weather, and unexpected situations create differences between the customer and the system developer. One strategy to address this issue is to use a scoring conference process. A scoring conference includes representatives from the customer, the developer, the test organization, the reliability organization, and sometimes independent observers. The scoring conference process is defined in the statement of work. Each test case is considered by the group and "scored" as a success or failure. This scoring is the official result used by the reliability engineer.

161 As part of the requirements pha se, the reliability engineer develops a test strategy with the customer. The test strategy makes trade-offs between the needs of the reliability organization, which wants as much data as possible, and constraints such as cost, schedule, and available resources. Test plans and procedures are developed for each reliability test, and results are documented in official reports.

182 A common reliability metric is the number of software faults, usually expressed as faults per thousand lines of code. This metric, along with software execution time, is key to most software reliability models and estimates. The theory is that the software reliability increases as the number of faults (or fault density) goes down. Establishing a direct connection between fault density and mean-time-between-failure is difficult, however, because of the way software faults are distributed in the code, their severity, and the probability of the combination of inputs necessary to encounter the fault. Nevertheless, fault density serves as a useful indicator for the reliability engineer. Other software metrics, such as complexity, are also used. This metric remains controversial, since changes in software development and verification practices can have dramatic impact on overall defect rates.

199 There are several common types of reliability organizations. The project manager or chief engineer may employ one or more reliability engineers directly. In larger organizations, there is usually a product assurance or specialty engineering organization, which may include reliability, maintainability, quality, safety, human factors, logistics, etc. In such case, the reliability engineer reports to the product assurance manager or specialty engineering manager.

200 In some cases, a company may wish to establish an independent reliability organization. This is desirable to ensure that the system reliability, which is often expensive and time consuming, is not unduly slighted due to budget and schedule pressures. In such cases, the reliability engineer works for the project day-to-day, but is actually employed and paid by a separate organization within the company.

203 The American Society for Quality has a program to become a Certified Reliability Engineer, CRE. Certification is based on education, experience, and a certification test: periodic re-certification is required. The body of knowledge for the test includes: reliability management, design evaluation, product safety, statistical tools, design and development, modeling, reliability testing, collecting and using data, etc.

206 Some Universities offer graduate degrees in Reliability Engineering (e.g., see University of Tennessee, Knoxville, University of Maryland, College Park, Concordia University, Montreal, Canada, Monash University, Australia and Tampere University of Technology, Tampere, Finland). Other reliability engineers typically have an engineering degree, which can be in any field of engineering, from an accredited university or college program. Many engineering programs offer reliability courses, and some universities have entire reliability engineering programs. A reliability engineer may be registered as a Professional Engineer by the state, but this is not required by most employers. There are many professional conferences and industry training programs available for reliability engineers. Several professional organizations exist for reliability engineers, including the IEEE Reliability Society, the American Society for Quality (ASQ), and the Society of Reliability Engineers (SRE).

model

76 Reliability prediction is the combination of the creation of a proper reliability model together with estimating (and justifying) the input parameters for this model (like failure rates for a particular failure mode or event and the mean time to repair the system for a particular failure) and finally to provide a system (or part) level estimate for the output reliability parameters (system availability or a particular functional failure frequency).

80 Furthermore, based on latest insights in Reliability centered maintenance (RCM), most (complex) system failures do no occur due to wear-out issues (e.g. a number of 4% has been provided, refer to RCM page). The failures are often a result of combinations of more and multi-type events or failures. The results of these studies have shown that the majority of failures follow a constant failure rate model, for which prediction of the value of the parameters is often problematic and very time consuming (for a high level reliability - part level). Testing these constant failure rates at system level, by for example mil. handbook 781 type of testing, is not practical and can be extremely misleading.

103 Reliability modelling is the process of predicting or understanding the reliability of a component or system prior to its implementation. Two types of analysis that are often used to model a system reliability behavior are Fault Tree Analysis and Reliability Block diagrams. On component level the same analysis can be used together with others. The input for the models can come from many sources, e.g.: Testing, Earlier operational experience field data or Data Handbooks from the same or mixed industries can be used. In all cases, the data must be used with great caution as predictions are only valid in case the same product in the same context is used. Often predictions are only made to compare alternatives.

111 In a study of the intrinsic failure distribution, which is often a material property, higher (material) stresses are necessary to get failure in a reasonable period of time. Several degrees of stress have to be applied to determine an acceleration model. The empirical failure distribution is often parametrized with a Weibull or a log-normal model.

112 It is a general praxis to model the early (hardware) failure rate with an exponential distribution. This less complex model for the failure distribution has only one parameter: the constant failure rate. In such cases, the Chi-squared distribution can be used to find the goodness of fit for the estimated failure rate. Compared to a model with a decreasing failure rate, this is quite pessimistic (important remark: this is not the case if less hours

122 Reliability design begins with the development of a (system) model. Reliability models use block diagrams and fault trees to provide a graphical means of evaluating the relationships between different parts of the system. These models incorporate predictions based on parts-count failure rates taken from historical data. While the (input data) predictions are often not accurate in an absolute sense, they are valuable to assess relative differences in design alternatives.

173 Arrhenius Model

174 Eyring Model

175 Inverse Power Law Model

176 Temperature-Humidity Model

177 Temperature Non-thermal Model

184 Eventually, the software is integrated with the hardware in the top-level system, and software reliability is subsumed by system reliability. The Software Engineering Institute's Capability Maturity Model is a common means of assessing the overall software development process for reliability and quality purposes.

plan