Generic Reliability Program

An effective reliability program consists of a series of reliability tasks to be imple-mented throughout the product life cycle, including product planning; design and development; verification and validation; production; field deployment; and dis-posal. The reliability activities are not independent exercises; rather, they should be integrated into engineering projects in each stage of the life cycle and assist successful completion of the projects. Figure 3.8 shows the main stages of a typical product life cycle and the reliability tasks that may be implemented in each of the stages. The listed reliability tasks are not intended to be exhaustive;

other reliability techniques, such as redundancy design, are not included because of fewer applications in commercial products.

In the product planning stage, reliability tasks are intended to capture customer expectations, establish competitive reliability requirements, and organize a team and secure the resources needed by the reliability program. The reliability tasks in this stage are explained briefly below.

1. Organizing a reliability team. A cross-functional team should be assembled at the beginning of a product planning stage so that the reliability requirements are considered in the decision-making process. Even though reliability requirements are ultimately driven by customers, the top leaders of some organizations still unfortunately perceive reliability deployment as a luxurious exercise. In these situations it is vital to seek a management champion of the team and assure that the resources needed throughout the reliability program will be in place.

Outputs of the team can be maximized if the team members have diversified expertise, including reliability, market research, design, testing, manufacture, and field service.

2. Quality function deployment (QFD). This is a powerful tool for translating customer expectations into engineering requirements. The method was described in detail in Section 3.2.

3. Reliability history analysis. This task is to collect and analyze customer feedback, test data, and warranty failure data of the prior-generation product.

The analysis should indicate what customer wants were not reasonably satisfied and reveal areas for improvement. Methods of reliability analysis using warranty data are described in Chapter 11.

4. Reliability planning and specification. The objective of this task is to estab-lish a competitive reliability target that is economically achievable and develop an effective reliability program to reach or exceed the target. This task can be assisted by utilizing the results of QFD and reliability historical data analysis.

In the design and development stage and before prototypes are created, reli-ability tasks are to build relireli-ability and robustness into products and to prevent

Organizing a reliability team QFD Reliability history analysis Reliability planning and specification

•Reliability modeling •Reliability allocation •Reliability prediction •Stress derating •Robust design •Concept and design FMEA •

• • • • FTA •Design controls •Accelerated life testing •Accelerated degradation testing •Failure analysis •Reliability estimation •Warranty cost prediction •Reliability design review

•Reliability verification testing •Analytical reliability verification •Accelerated life testing • Accelerated degradation testing •Failure analysis •Process FMEA •Process control plans and charts •Process capability analysis Process control plans and charts •

• Process capability analysis •Stress screening •Acceptance sampling •Failure analysis

Warranty plan development •

• Field failure tracking •Warranty data analysis •Customer feedback analysis •Failure analysis •Six-sigma process implementation •Lesson learned

Product PlanningDesign and Development Verification and ValidationProductionField DeploymentDisposal

FIGURE 3.8 Reliability tasks for a typical product life cycle

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potential failure modes from occurrence. The reliability tasks in this stage are described below.

1. Reliability modeling. This task is to model product reliability according to the architecture of the product. The architecture lays out the logic connections of components, which may be in series, parallel, or more complex configurations.

Product reliability is expressed as a function of component reliabilities. This relationship is useful in reliability allocation, prediction, and analysis. We discuss this task in detail in Chapter 4.

2. Reliability allocation. The reliability target established in the product plan-ning stage should be apportioned appropriately to lower-level structures (sub-systems, modules, or components) of the product. The reliability allocated to a structure becomes the reliability target of that structure. Organizations respon-sible for lower-level structures must achieve their respective targets so that the overall reliability target is attained. Reliability allocation methods are presented in Chapter 4.

3. Reliability prediction. In the early design stage, it is frequently desirable to predict reliability for comparing design alternatives and components, identifying potential design issues, determining if a design meets the allocated reliability target, and projecting reliability performance in the field. Several methods are often employed for prediction in this stage. Part count and part stress analy-sis for electronic equipment, well documented in MIL-HDBK-217 (U.S. DoD, 1995), was a prevailing approach until the mid-1990s. The approach assumes that component lifetimes are exponentially distributed (with a constant failure rate) and that a system is in the logic series of the components. In addition to these assumptions, part stress analysis overemphasizes temperature effects and overlooks other stresses, such as thermal cycling and transient conditions, which are the primary causes of failure in many systems. It was reported repeatedly that the DoD’s handbook produced overly pessimistic results, especially when used in commercial products. Unsurprisingly, the handbook was subjected to overwhelming criticism and it is no longer upgraded. A more recent prediction methodology known as PRISMPlus was developed by the Reliability Analysis Center (RAC), now the Reliability Information Analysis Center (RIAC). The methodology includes component-level reliability prediction models and a pro-cess for assessment of system reliability due to noncomponent variables such as software and process. The prediction program is comprised of RAC failure mod-els and failure data, user-defined data, and a system failure model that applies process-grading factors. Smith and Womack (2004) report that the methodology produced a more realistic result for an airborne system in a correlation study.

There are various commercial software packages that are capable of performing reliability prediction based on this methodology. Another approach to reliability prediction in the early design stage is modeling system reliability as a function of component reliabilities based on the system configuration (Chapter 4). Com-ponent reliabilities may be estimated from historical test data, warranty data, or other sources. Ideally, prediction of component reliability should be driven by

a physics-based model, which describes the underlying failure process. Unfortu-nately, such models are unavailable for most applications, due to the difficulty in understanding and quantifying the failure mechanisms.

4. Stress derating. This task is to enhance reliability by reducing stresses that may be applied to a component to levels below the specified limits. When imple-mented in an electronic design as it often is, derating technique lowers electrical stress and temperature versus the rated maximum values. This alleviates parame-ter variation and degradation and increases long-parame-term reliability. Useful references for this technique include, for example, U.S. DoD (1998) and O’Connor (2002).

5. Robust design. A failure can be attributed to either a lack of robustness or the presence of mistakes induced in design or production. The purpose of robust design is to build robustness and reliability into products in the design stage through implementation of a three-stage process: concept design, parameter design, and tolerance design. In Chapter 5 we describe in detail the methodology of robust reliability design with an emphasis on parameter design. This technique can result in a great improvement in reliability and robustness but has not been implemented as extensively have as conventional reliability tools.

6. Concept and design FMEA (failure mode and effects analysis). As stated above, the causes of failure can be classified into two groups: lack of robustness and presence of mistakes. Concept and design FMEA are performed to uncover potential failure modes, analyze effects, and determine causes of failures. The FMEA process is intended to detect design errors that have been embedded into a design and supports recommendations for corrective actions. In Chapter 6 we describe the FMEA methodology.

7. Fault tree analysis (FTA). Some failure modes of a design may evoke special concerns, especially when safety is involved. In such situations, FTA is often needed to identify the root causes of the failure modes and to assess the probability of failure occurrence. We introduce the FTA technique in Chapter 6.

8. Design controls. This task is aimed at detecting design deficiencies before a design is prototyped. This is accomplished by analyzing product responses to stresses such as temperature, humidity, vibration, mechanical and electrical load, and electromagnetic interference. The common problems uncovered in design control include crack, fatigue, overheating, and open or short circuit, among others. Once concerns are identified and evaluated, corrective actions should be recommended. Implementation of design controls usually requires dedicated computer programs. In Chapter 6 we describe concisely several design control techniques that are applied widely in industry.

9. Accelerated life testing. Testing products in the design stage is essential in nearly all design programs for the purpose of comparing design options, uncovering failure modes, estimating reliability, and verifying a design. Testing a product to failure at a normal operating condition is often unfeasible econom-ically, especially in the current competitive business environment. Instead, we conduct accelerated life tests at higher stress levels, which shortens test time and reduces test cost. This task can be a part of robust reliability design, which often

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requires testing products to failure at different combinations of design settings.

In Chapter 7 we present accelerated life test methods and life data analysis.

10. Accelerated degradation testing. Even under accelerating conditions, test-ing high-reliability products to failure may be too lengthy a task to be affordable.

For some products a failure is said to have occurred if one of the performance characteristics crosses a specified threshold. The characteristics are the indicators of product reliability. Therefore, it is possible to estimate reliability of such prod-ucts by using degradation measurements, which are recorded during testing. This type of test is more efficient than an accelerated life test in terms of test time and cost. This task can be a part of robust reliability design aimed at determining the optimal levels of design parameters. In Chapter 8 we describe accelerated degradation test methods and degradation data analysis.

11. Failure analysis. Accelerated life or degradation testing may produce failures. The failed units should be analyzed for failure modes, effects, and mech-anisms. Failure analyses at the component or material level usually enable a deep understanding of the root causes and may lead to prevent the same failure modes.

All products that fail prior to field deployment should be analyzed thoroughly for causes. Even in the field deployment phase, most warranty return parts are subjected to failure analysis to determine the failure modes and mechanisms in the real world.

12. Reliability estimation. This task is needed throughout the product life cycle for a variety of purposes. In many applications it is not a separate task. Rather, it is a part of, for example, reliability history analysis, accelerated testing, design comparison, and warranty analysis. In Chapters 7, 8, and 11 we present methods for reliability estimation from different types of data.

13. Warranty cost prediction. Warranty cost not only quantifies the revenue that would be eroded by warranty repairs but also indicates customer satisfaction and competitiveness once a product enters the marketplace. From an engineering perspective, warranty cost reflects the reliability as well as maintainability, both of which should be considered in design. Warranty cost depends on warranty policy, product reliability, sales volume, and cost per repair. In the design stage, product reliability may be estimated from test data, computer simulation, or historical data. In Chapter 11 we present methods for estimating warranty cost.

14. Reliability design review. A reliability program should establish several checkpoints at which reliability tasks are reviewed. The objective of the review is to audit whether the reliability program is executed as planned in terms of sched-ule and accuracy. Importantly, the review team should evaluate the possibility that the reliability target will be achieved through implementation of the estab-lished reliability program based on what has been accompestab-lished. If necessary, the team should recommend actions to improve the effectiveness of the pro-gram. Whenever possible, reliability design reviews should be conducted along with engineering design reviews. The concurrent reviews enable the reliability

accomplishments to be examined by design engineers from a product design per-spective, and vice versa. These interdisciplinary reviews usually identify concerns that would not be discovered in individual reviews.

In the product verification and process validation stage, reliability tasks are intended to verify that the design achieves the reliability target, to validate that the production process is capable of manufacturing products that meet the reliability requirements, and to analyze the failure modes and mechanisms of the units that fail in verification and validation tests. As presented in Chapter 1, process planning is performed in this phase to determine the methods of manufacturing the product. Thus, also needed are reliability tasks that assure process capability.

The tasks that may be executed in this phase are explained below.

1. Reliability verification testing. This task is to demonstrate with minimum test time and sample size that a product meets the reliability target. In Chapter 9 we describe test methods, approaches to determination of sample size and test time, and techniques for sample size reduction.

2. Analytical reliability verification. Reliability verification through testing may be too expensive and time consuming to be affordable in some situations.

When there are adequate mathematical models that relate product life to stresses, design parameters, and manufacturing variables, the product reliability may be verified by evaluating such models. This approach, often referred to as virtual validation, involves finite element analysis, computer simulation, and numerical calculation.

3. Process FMEA. This task is performed in the process planning stage to detect potential process failure modes, analyze effects, and determine causes of failure. Then actions may be recommended to correct the process steps and prevent the failure modes from occurrence in production. In Chapter 6 we present the concept, process, and design FMEA, with focus on the design FMEA.

In the production stage, the objective of reliability tasks is to assure that the production process has minimum detrimental impact on the design reliability.

The tasks that may be implemented in this phase are described as follows.

1. Process control plans and charts. Process variation increases unit-to-unit variation and infant mortality and thus should be minimized in each step of the production process. This task is to develop and implement process control plans for the critical performance characteristics which are identified in the fourth house of quality of the QFD process. In Chapter 11 we present statistical process control charts for monitoring infant mortality using early warranty data. Montgomery (2001a) describes in detail methods for process control.

2. Process capability analysis. Process capability measures the uniformity of a production process. A process of low capability produces high variability in performance and low reliability. This task is to estimate the process capability

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and to provide information for minimizing process variation. Process capability analysis is well described in Montgomery (2001a).

3. Stress screening. Some products may have latent defects due to material flaws, process variation, or inadequate design. Defective products will fail in early service time and should be eliminated before being shipped to customers.

For this purpose, stress screening is often conducted. This task is covered in Chapter 10.

4. Acceptance sampling. This task is accomplished, as needed, to make a deci-sion as to whether to accept a particular production lot based on measurements of samples drawn at random from the lot. Due to material defects or the pro-cess running out of control, certain lots may contain a large portion of defective units. Failure to reject such substandard lots will result in low field reliability and customer dissatisfaction. ANSI/ASQ (2003a, b) provide standard methods for acceptance sampling.

In the field deployment stage, reliability tasks are aimed at developing a warranty plan, tracking field failures, assessing field reliability performance, evaluating customer satisfaction, analyzing warrantied parts, and developing con-tainment and permanent corrective actions as needed. The major reliability tasks are described below.

1. Warranty plan development. A preliminary warranty coverage may be planned in the product planning stage. The plan is finalized when the prod-uct is ready for marketing. Although a warranty plan is determined largely by market competition, the final decisions are driven by financial analysis. An impor-tant component of the financial analysis is the warranty repair cost, which may be estimated from warranty repair modeling or reliability prediction. Warranty policies and repair cost estimation are addressed in Chapter 11.

2. Field failure tracking. This task is intended to collect failure information from warranty repairs and customer complaints. The failure information should be as specific and accurate as possible and include failure modes, operating conditions at which the failures occur, failure time and usage (e.g., mileage), and others. Often, a computer system (i.e., a warranty database) is needed to store and retrieve these failure data. This task is covered in Chapter 11.

3. Warranty data analysis. This task is to estimate field reliability, project warranty repair numbers and costs, monitor field failures, and detect unexpected failure modes and patterns. Early detection of unusual failure modes and high failure probability can promote corrective actions to change ongoing manufactur-ing process and repair strategies. For safety-related products such as automobiles, timely warranty data analysis enables the assessment of risks associated with crit-ical failure modes and may warrant recalls. In Chapter 11 we present methods for warranty data analysis.

4. Customer feedback analysis. The real-world usage profile is the ultimate environment in which a product is validated, and customer satisfaction is the

predominant factor that drives the success of a product. Customer feedback on both functional and reliability performance must be analyzed thoroughly to deter-mine what product behaviors do and do not satisfy customers. The results are valuable inputs to the QFD development of the next-generation product. The sources for collecting feedback may include customer surveys, warranty claims, and customer complaints.

5. Six-sigma process implementation. Warranty data analysis of early failures may indicate unusual failure modes and failure probability. The root causes of the failures should be identified and eliminated in subsequent production. This may be accomplished by implementing the six-sigma process. The process is charac-terized by DMAIC (define, measure, analyze, implement, and control). The first step of the process is to define the problem and the project boundaries, followed by creating and validating the measurement system to be used for quantifying the problem. The analyze step is to identify and verify the causes of the prob-lem, and the improve step is to determine the methods of eliminating the causes.

Finally, improvement is implemented and sustained through the use of control plans. The DMAIC approach has been used extensively in industry and has

Finally, improvement is implemented and sustained through the use of control plans. The DMAIC approach has been used extensively in industry and has