Reliability engineering

Reliability engineering is a sub-discipline of systems engineering that emphasizes the ability of equipment to function without failure. Reliability is defined as the probability that a product, system, or service will perform its intended function adequately for a specified period of time; or will operate in a defined environment without failure. Reliability is closely related to availability, which is typically described as the ability of a component or system to function at a specified moment or interval of time. The reliability function is theoretically defined as the probability of success. In practice, it is calculated using different techniques, and its value ranges between 0 and 1, where 0 indicates no probability of success while 1 indicates definite success. This probability is estimated from detailed (physics of failure) analysis, previous data sets, or through reliability testing and reliability modeling. Availability, testability, maintainability, and maintenance are often defined as a part of "reliability engineering" in reliability programs. Reliability often plays a vital role in the cost-effectiveness of overall systems. Reliability engineering deals with the prediction, prevention, and management of high levels of "lifetime" engineering uncertainty and risks of failure.…

Reliability engineering is a sub-discipline of systems engineering that emphasizes the ability of equipment to function without failure. Reliability is defined as the probability that a product, system, or service will perform its intended function adequately for a specified period of time; or will operate in a defined environment without failure. Reliability is closely related to availability, which is typically described as the ability of a component or system to function at a specified moment or interval of time. The reliability function is theoretically defined as the probability of success. In practice, it is calculated using different techniques, and its value ranges between 0 and 1, where 0 indicates no probability of success while 1 indicates definite success. This probability is estimated from detailed (physics of failure) analysis, previous data sets, or through reliability testing and reliability modeling. Availability, testability, maintainability, and maintenance are often defined as a part of "reliability engineering" in reliability programs. Reliability often plays a vital role in the cost-effectiveness of overall systems. Reliability engineering deals with the prediction, prevention, and management of high levels of "lifetime" engineering uncertainty and risks of failure. Although stochastic parameters define and affect reliability, reliability is not only achieved by mathematics and statistics. "Nearly all teaching and literature on the subject emphasize these aspects and ignore the reality that the ranges of uncertainty involved largely invalidate quantitative methods for prediction and measurement." For example, it is easy to represent "probability of failure" as a symbol or value in an equation, but it is almost impossible to predict its true magnitude in practice, which is massively multivariate, so having the equation for reliability does not begin to equal having an accurate predictive measurement of reliability. Reliability engineering relates closely to quality engineering, safety engineering, and system safety, in that they use common methods for their analysis and may require input from each other. It can be said that a system must be reliably safe. Reliability engineering focuses on the costs of failure caused by the system downtime, cost of spares, repair equipment, personnel, and cost of warranty claims. ====Statistics-based approach (i.e. MTBF)==== Reliability design begins with the development of a (system) model. Reliability and availability models use block diagrams and Fault Tree Analysis to provide a graphical means of evaluating the relationships between different parts of the system. These models may incorporate predictions based on 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. Maintainability parameters, for example Mean time to repair (MTTR), can also be used as inputs for such models. The most important fundamental initiating causes and failure mechanisms are to be identified and analyzed with engineering tools. A diverse set of practical guidance as to performance and reliability should be provided to designers so that they can generate low-stressed designs and products that protect, or are protected against, damage and excessive wear. Proper validation of input loads (requirements) may be needed, in addition to verification for reliability "performance" by testing. One of the most important design techniques is redundancy. This means that if one part of the system fails, there is an alternate success path, such as a backup system. The reason why this is the ultimate design choice is related to the fact that high-confidence reliability evidence for new parts or systems is often not available, or is extremely expensive to obtain. By combining redundancy, together with a high level of failure monitoring, and the avoidance of common cause failures; even a system with relatively poor single-channel (part) reliability, can be made highly reliable at a system level (up to mission critical reliability). No testing of reliability has to be required for this. In conjunction with redundancy, the use of dissimilar designs or manufacturing processes (e.g. via different suppliers of similar parts) for single independent channels, can provide less sensitivity to quality issues (e.g. early childhood failures at a single supplier), allowing very-high levels of reliability to be achieved at all moments of the development cycle (from early life to long-term). Redundancy can also be applied in systems engineering by double checking requirements, data, designs, calculations, software, and tests to overcome systematic failures. Another effective way to deal with reliability issues is to perform analysis that predicts degradation, enabling the prevention of unscheduled downtime events / failures. RCM (Reliability Centered Maintenance) programs can be used for this. ====Physics-of-failure-based approach==== For electronic assemblies, there has been an increasing shift towards a different approach called physics of failure. This technique relies on understanding the physical static and dynamic failure mechanisms. It accounts for variation in load, strength, and stress that lead to failure with a high level of detail, made possible with the use of modern finite element method (FEM) software programs that can handle complex geometries and mechanisms such as creep, stress relaxation, fatigue, and probabilistic design (Monte Carlo Methods/DOE). The material or component can be re-designed to reduce the probability of failure and to make it more robust against such variations. Another common design technique is component derating: i.e. selecting components whose specifications significantly exceed the expected stress levels, such as using heavier gauge electrical wire than might normally be specified for the expected electric current. ====Common tools and techniques==== Many of the tasks, techniques, and analyses used in Reliability Engineering are specific to particular industries and applications, but can commonly include: Physics of failure (PoF) Built-in self-test (BIT or BIST) (testability analysis) Failure mode and effects analysis (FMEA) Reliability hazard analysis Reliability block-diagram analysis Dynamic reliability block-diagram analysis Fault tree analysis Root cause analysis Statistical engineering, design of experiments – e.g. on simulations / FEM models or with testing Sneak circuit analysis Accelerated testing Reliability growth analysis (re-active reliability) Weibull analysis (for testing or mainly "re-active" reliability) Hypertabastic survival models Thermal analysis by finite element analysis (FEA) and / or measurement Thermal induced, shock and vibration fatigue analysis by FEA and / or measurement Electromagnetic analysis Avoidance of single point of failure (SPOF) Functional analysis and functional failure analysis (e.g., function FMEA, FHA or FFA) Predictive and preventive maintenance: reliability centered maintenance (RCM) analysis Testability analysis Failure diagnostics analysis (normally also incorporated in FMEA) Human error analysis Operational hazard analysis Preventative/Planned Maintenance Optimization (PMO) Manual screening Integrated logistics support Results from these methods are presented during reviews of part or system design, and logistics. Reliability is just one requirement among many for a complex part or system. Engineering trade-off studies are used to determine the optimum balance between reliability requirements and other constraints. ==Reliability modeling== Reliability modeling 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 complete system's availability behavior including effects from logistics issues like spare part provisioning, transport and manpower are fault tree analysis and reliability block diagrams. At a component level, the same types of analyses can be used together with others. The input for the models can come from many sources including testing; prior operational experience; field data; as well as data handbooks from similar or related industries. Regardless of source, all model input data must be used with great caution, as predictions are only valid in cases where the same product was used in the same context. As such, predictions are often only used to help compare alternatives. For part level predictions, two separate fields of investigation are common: The physics of failure approach uses an understanding of physical failure mechanisms involved, such as mechanical crack propagation or chemical corrosion degradation or failure; 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. R(t)=Pr\{T>t\}=\int_{t}^{\infty} f(x)\, dx \ \! , where f(x) \! is the failure probability density function and t is the length of the period of time (which is assumed to start from time zero). There are a few key elements of this definition: Reliability is predicated on "intended function:" Generally, this is taken to mean operation without failure. However, even if no individual part of the system fails, but the system as a whole does not do what was intended, then it is still charged against the system reliability. The system requirements specification is the criterion against which reliability is measured. Reliability applies to a specified period of time. In practical terms, this means that a system has a specified chance that it will operate without failure before time T \! . Reliability engineering ensures that components and materials will meet the requirements during the specified time. Note that units other than time may sometimes be used (e.g. "a mission", "operation cycles"). Reliability is restricted to operation under stated (or explicitly defined) conditions. This constraint is necessary because it is impossible to design a system for unlimited conditions. A Mars rover will have different specified conditions than a family car. The operating environment must be addressed during design and testing. That same rover may be required to operate in varying conditions requiring additional scrutiny. Two notable references on reliability theory and its mathematical and statistical foundations are Barlow, R. E. and Proschan, F. (1982) and Samaniego, F. J. (2007). Often, knowledge of the statistical dependence between component failures is unknown or only partially constrained. When it is desired to model such incomplete knowledge, reliability theory allows for analyses that characterize ranges or bounds on system failure probabilities rather than unique distributions, particularly for small multi-component systems. SR-332 Reliability Prediction Procedure for Electronic Equipment, Telcordia Technologies (January 2011). FD-ARPP-01 Automated Reliability Prediction Procedure, Telcordia Technologies (January 2011). GR-357 Generic Requirements for Assuring the Reliability of Components Used in Telecommunications Equipment, Telcordia Technologies (March 2001). http://standards.sae.org/ja1000/1_199903/ SAE JA1000/1 Reliability Program Standard Implementation Guide ===International standards=== TC 56 Standards: Dependability ==External links== John P. Rankin Collection, The University of Alabama in Huntsville Archives and Special Collections NASA reliability engineering research on sneak circuits.

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