In this stage, we will start quantifying all of the previous work based on test results. By this stage, prototypes should be ready for testing and more detailed analysis. Typically, this involves an iterative process where different types of tests are performed, the results are analyzed, design changes are made, and tests are repeated. A wide array of tools are available for the reliability engineer to uncover product weaknesses, predict life and manage the reliability improvement efforts. The following is a summary of the most commonly used tools.
Design of Experiments (DOE) provides a methodology to create organized test plans to identify important variables, to estimate their effect on a certain product characteristic and to optimize the settings of these variables to improve the design robustness. Within the DFR concept, we are mostly interested in the effect of stresses on our test units. DOEs play an important role in DFR because they assist in identifying the factors that are significant to the life of the product, especially when the physics of failure are not well understood. Knowing the significant factors results in more realistic reliability tests and more efficient accelerated tests (since resources are not wasted on including insignificant stresses in the test).
With testing comes data, such as failure times and censoring times. Test results can be analyzed with Life Data Analysis (LDA) techniques to statistically estimate the reliability of the product and calculate various reliability-related metrics with a certain confidence interval. Applicable metrics may include reliability after a certain time of use, conditional reliability, B(X) information, failure rate, MTBF, median life, etc. These calculations can help in verifying whether the product meets its reliability goals, comparing designs, projecting failures and warranty returns, etc.
As an alternative to testing under normal use conditions and LDA, Quantitative Accelerated Life Testing (QALT) can also be employed to cut down on the testing time. By carefully elevating the stress levels applied during testing, failures occur faster and thus failure modes are revealed (and statistical life data analysis can be applied) more quickly.
Highly Accelerated Tests (HALT/HASS) are qualitative accelerated tests used to reveal possible failure modes and complement the physics of failure knowledge about the product. However, data from qualitative tests cannot be used to quantitatively project the product's reliability.
A very important aspect of the DFR process also includes performing Failure Analysis (FA) or Root Cause Analysis (RCA). FA relies on careful examination of failed devices to determine the root cause of failure and to improve product reliability. This is where the engineers come face-to-face with the failure, see what a failure actually looks like and study the processes that lead to it. FA provides better understanding of physics of failure and can discover issues not foreseen by techniques used prior to testing (such as FMEA). FA helps with developing tests focused on problematic failure modes. It can also help with selecting better materials and/or designs and processes, and with implementing appropriate design changes to make the product more robust.
System Reliability Analysis with Reliability Block Diagrams (RBDs) can be used in lieu of testing an entire system by relying on the information and probabilistic models developed on the component or subsystem level to model the overall reliability of the system. It can also be used to identify weak areas of the system, find optimum reliability allocation schemes, compare different designs and to perform auxiliary analysis such as availability analysis (by combining maintainability and reliability information).
Fault Tree Analysis (FTA) may be employed to identify defects and risks and the combination of events that lead to them. This may also include an analysis of the likelihood of occurrence for each event.
Reliability Growth (RG) testing and analysis is an effective methodology to discover defects and improve the design during testing. Different strategies can be employed within the reliability growth program, namely: test-find-test (to discover failures and plan delayed fixes), test-fix-test (to discover failures and implement fixes during the test) and test-fix-find-test (to discover failures, fix some and delay fixes for some). RG analysis can track the effectiveness of each design change and can be used to decide if a reliability goal has been met and whether, and how much, additional testing is required.