Accelerated Life Testing: A Strategic Approach to Predictable Reliability and Reduced Failures

In today's demanding manufacturing landscape, ensuring product reliability is paramount for customer satisfaction, safety, and cost control. Businesses often face a critical question: how can we accurately quantify product life characteristics and predict future failures efficiently? While traditional life data analysis involves testing products under normal operating conditions, this approach, similar to a "full build" in automation, presents significant challenges. A more strategic approach, akin to a "Proof of Concept" (POC) in automation, is Accelerated Life Testing (ALT).

The Allure (and Peril) of Traditional Life Testing: Just "Go For It!"

The idea of directly testing products under normal use conditions seems straightforward. It allows for analyzing times-to-failure data to quantify product life characteristics. However, this path is often fraught with hidden risks and inefficiencies, particularly for modern products designed for years or decades of service.

The Perceived Advantages:

• Direct Observation of Performance: Life data is obtained under normal operating conditions, providing a direct understanding of how the product performs in its intended environment.

• Focus on Real-World Scenarios: The analysis directly reflects actual customer usage and failure modes.

The Significant Downsides (and Why They Lead to "Common Mistakes"): Our experience consistently shows that relying solely on traditional testing can lead to substantial delays and increased costs:

• Time-Consuming and Costly: For high-reliability products designed to endure for years or decades, obtaining sufficient failure time data under normal use conditions can be very expensive and time-consuming. Manufacturers are pressured to deliver durable products in a timely and competitive manner, which is difficult with long testing periods.

• Difficulty in Capturing Failures: Many modern products have such long lifespans that observing enough failures under normal conditions to make statistically significant predictions becomes impractical.

• Lack of Agility in Design: Without induced early failures, it is challenging to quickly identify and address design weaknesses or potential failure modes early in the development cycle.

The Strategic Advantage of Accelerated Life Testing (ALT): "Test Before You Invest"

Accelerated life testing (ALT) offers a powerful alternative, analogous to a strategic Proof of Concept (POC) in automation. ALT involves testing products in a more severe environment by applying or fluctuating one or more stressors at accelerated levels to induce early failures. This failure data is then extrapolated to predict the reliability characteristics of products at normal use conditions.

Weibull Distribution Graph of varying n

The Undeniable Advantages (and How They Drive Predictable Results and Reduce Failures):

• Profound Acceleration of Failures: ALT significantly reduces the time required to obtain times-to-failure data. Products that might operate for years under normal conditions can be made to fail more quickly by subjecting them to increased stresses like temperature, pressure, voltage, wattage, humidity, loads, or vibration amplitude.

• Quantification of Life Characteristics at Normal Conditions: The primary goal of quantitative ALT (QALT) is to quantify the life characteristics of a product under normal use conditions. This is achieved by extrapolating data from accelerated conditions to estimate a probability density function (pdf) for the product at normal use. From this, crucial reliability metrics such as reliability, probability of failure, mean life (MTTF), failure rate, and B(10) life (the time when 10% of the product population will have likely failed) can be calculated.

• Enhanced Risk Mitigation and Failure Mode Identification: ALT is invaluable for revealing probable failure modes early in the product design phase, allowing engineers to improve product design and eliminate causes of failure proactively. Highly Accelerated Life Testing (HALT) and Highly Accelerated Stress Testing (HAST) are examples of qualitative ALT methods used for this purpose.

• Significant Reduction in Time to Market and Costs: By quickly yielding reliability results, accelerated testing can substantially reduce time to market, lower product development costs, and decrease warranty expenses.

• Robust Data Analysis and Predictability:

    ◦ Life Distributions and Time Transformation Functions (TTFs): A functional relationship, known as a TTF, is assumed between stressors and the parameters of the life distribution. Common life distributions include the Weibull, exponential, and lognormal distributions. The Weibull distribution is often preferred due to its flexibility.

    ◦ Life-Stress Models: Life-stress relationships (e.g., Arrhenius, Eyring, inverse power law, generalized Eyring, temperature-humidity, temperature-nonthermal, general log-linear, proportional hazards) are used to quantify how the life distribution changes across different stress levels. The generalized Eyring model is particularly versatile as it allows for multiple stressors.

    ◦ Parameter Estimation and Inference: Maximum Likelihood Estimation (MLE) is a robust method used to fit models to accelerated test data and estimate parameters. Bayesian methods, often utilizing Markov Chain Monte Carlo (MCMC) techniques, are also employed for posterior inference, especially when posteriors are mathematically intractable. These methods provide a more comprehensive understanding and allow for the generation of samples for predictive reliability calculation. Predictive reliability at normal use conditions can be evaluated using Monte Carlo averages of the Weibull reliability function at use stress levels.

    ◦ Predictive Reliability Adjustment: The choice of prior distributions in Bayesian models can influence predictive reliability results. Subjective priors can be used to adjust reliability estimates if initial assessments with flat priors are believed to be over- or underestimating.

• Integration with Other Reliability Tools: ALT can be integrated with other essential reliability engineering tools to maximize benefits. For example, it can validate findings from Failure Mode and Effects Analysis (FMEA) by testing identified potential failure modes. It also helps in developing optimized maintenance schedules for Reliability-Centered Maintenance (RCM) based on estimated reliability metrics.

Typical Life Cycle Bathtub Curve

Necessary Considerations:

• Appropriate Stress Selection: Stressors and their levels must be chosen carefully to accelerate only those failure modes that would occur under normal use conditions, without introducing unrealistic ones. Consulting design engineers and material scientists is crucial for this step.

• Uncertainty in Extrapolation: While ALT provides faster results, moving farther from use conditions increases the uncertainty in extrapolation, which is quantified by confidence intervals.

• Stress Loading Schemes: Stresses can be constant (time-independent) or time-dependent (e.g., step-stress, ramp-stress). Constant stress tests are generally simpler to quantify and extrapolate from more accurately. Time-varying stress models require cumulative damage or cumulative exposure models.

• Data Quality and Sample Size: The accuracy of any prediction directly correlates with the quality, accuracy, and completeness of the collected data. While various types of data, including complete and censored (right, interval, left), can be used, sufficient sample sizes and systematic, homogeneous data collection are vital for good model fits and reliable results.

• Managing Infant Mortality and Wear-Out: Proper ALT and burn-in testing aim to eliminate the "infant mortality" phase of a product's life (early failures due to manufacturing defects) without prematurely consuming its "useful life" or running into the "wear-out" phase. This involves correctly estimating the thresholds for these phases.

High Accelerated Testing (HALT)

Our Recommendation: Embrace Accelerated Life Testing

For manufacturers striving to enhance product reliability, reduce costs, and accelerate time to market, Accelerated Life Testing is an indispensable strategic tool. By meticulously designing ALT experiments, selecting appropriate life-stress relationships, and applying robust data analysis techniques like MLE and Bayesian methods, businesses can gain invaluable insights into product performance. This proactive approach, which allows for early identification and mitigation of potential failures, transforms reliability engineering from a speculative venture into a predictable engine of growth, ensuring your products operate as intended throughout their lifespan.

Ready to revolutionize your product reliability with a data-driven approach? Contact reliability experts today for a consultation on accelerated life testing strategies, data analysis, and predictive reliability modeling.