Practical Approach to Product Reliability Verification: From HALT to ALT in Accelerated Life Testing

1. Why Reliability Verification is the "Last Mile" of R&D Quality

Many companies' new products pass all functional tests, but experience continuous failures after mass production: some equipment exhibits motor noise after three months of use, some controllers fail in batches in high-temperature workshops, and some connectors experience poor contact within a year of use at customer sites. The root cause is that during the R&D phase, only the "whether it can be used" was verified, not the "how long it can be used" or "how well it can be used in harsh environments." Functional verification answers "whether it works well now," while reliability verification answers "whether it will fail in the future." The dividing line between these two is often the watershed for many quality issues.

Reliability verification is a systematic testing activity conducted before the official mass production of a product. It aims to expose design weaknesses and assess life characteristics through simulation or acceleration. It complements the functional and performance verification in the DVP (Design Verification Plan): functional verification proves that the product meets the specification requirements, while reliability verification proves that the product continues to meet these requirements over time and in various environments. Products lacking reliability verification are essentially handing over "unknown failure modes" to customers. This article will focus on the two core reliability tests—HALT (Highly Accelerated Life Test) and ALT (Accelerated Life Test)—providing a comprehensive method from test design, sample planning, data analysis to improvement closure.

2. Clarifying Concepts: The Roles of HALT, ALT, and Reliability Growth Testing

The reliability testing family is extensive, and beginners often confuse HALT, ALT, and reliability growth testing, which have different purposes, timing, and outputs.

HALT (Highly Accelerated Life Test) occurs early in the R&D phase, typically before the design is finalized. Its purpose is not to assess life but to "force out weaknesses": by applying stepwise stresses far exceeding the specifications, such as vibration, temperature, and voltage, it quickly exposes design flaws. HALT is characterized by high stress, a small sample size (usually 3 to 5 units), and a short cycle (a few days to two weeks). The output is a "list of design weaknesses" and "operational limit/destructive limit" data. The goal is to "fail as quickly as possible," as the earlier the failure, the lower the cost of improvement.

ALT (Accelerated Life Test) occurs after the design is basically finalized, aiming to quantitatively assess the product's life characteristics under normal use conditions. It accelerates failure by increasing stress levels and then extrapolates the life under normal stress using an acceleration model. The output includes metrics such as MTBF (Mean Time Between Failures) and B10 life (the time by which 10% of the products fail). ALT is characterized by controlled stress, a larger sample size, and a longer cycle, requiring rigorous statistical design.

Reliability growth testing (such as TAAF, Test-Analyze-And-Fix) falls between the two: through a cycle of "testing—analysis—improvement—retesting," it gradually eliminates identified weaknesses, continuously improving the product's inherent reliability. Together, these three form a clear path: HALT identifies weaknesses, improvements are made, ALT quantitatively assesses life, and growth testing solidifies the results. Companies do not need to cover all three stages, but they should at least understand which stage they are missing.

3. Practical HALT: Exposing Design Weaknesses in the Lab

The core idea of HALT is "testing with the purpose of destruction." Traditional reliability testing verifies that the product will not fail under specified stresses, while HALT does the opposite by using stresses far beyond the specifications to identify where the product will fail. It typically involves four steps.

Step 1: Determine the test profile. Typical HALT includes temperature stepping, six-degree-of-freedom random vibration, combined temperature and vibration, and voltage bias. Temperature stepping usually starts near the upper and lower limits of the specifications, increasing by 10°C to 20°C per step, with each step lasting 10 to 15 minutes. Vibration starts at 5 to 10Grms and gradually increases to over 50Grms or even higher. The stress levels are not arbitrarily set but are determined based on the actual usage environment of the product and industry experience.

Step 2: Identify the operational limit and destructive limit. During the test, continuously monitor the product's functionality and record two critical points: the operational limit—the stress value at which the product's function begins to malfunction but recovers when the stress is reduced; and the destructive limit—the stress value at which the product experiences permanent failure. These two limits serve as direct references for subsequent design margins. For example, a power supply company found through HALT that a power module failed to start at temperatures below -40°C, due to an electrolytic capacitor's unexpected capacity decay at low temperatures. After improving the component selection, the operational limit was extended from -35°C to -50°C, demonstrating the typical value of HALT.

Step 3: Improve each exposed weakness. Each failure identified in HALT requires root cause analysis to distinguish between design issues, component issues, or manufacturing issues. After improvements, retesting must be conducted to confirm the effectiveness, rather than assuming the issue is resolved. A common pitfall is recording failures without tracking the closure, leading to repeated weaknesses.

Step 4: Determine design margins and incorporate them into specifications. At the end of HALT, the gap between the operational limit and the specification requirements should be solidified as design margin requirements, such as "the operational temperature limit must be at least 15°C wider than the specification" or "the vibration limit must be at least 1.5 times the specification." These margin requirements should be included in the design inputs for future projects, allowing HALT experience to become organizational capability.

It is important to note that HALT is not suitable for batch product acceptance—its small sample size and extreme stress levels mean that the results cannot be directly extrapolated to mass production reliability indicators. Its sole mission is to expose design weaknesses at the least costly stage.

4. Practical ALT: Using Acceleration Models to Estimate Normal Life

If HALT answers "where it will fail," ALT answers "how long it will last." The challenge in ALT is that failures under normal stress are too slow to wait for, while excessively high stress can change the failure mechanism, leading to inaccurate extrapolation results. Therefore, the core of ALT design is "acceleration without altering the quality."

Step 1: Select acceleration stress and acceleration model. Common acceleration stresses include temperature, voltage, humidity, and vibration, each corresponding to different acceleration models: temperature stress uses the Arrhenius model, whose core is the activation energy Ea—the larger the Ea, the more sensitive the life is to temperature; combined temperature and humidity stress often uses the Eyring model or the Hallberg-Peck model; voltage stress commonly uses the inverse power law model. Model selection should be based on the failure mechanism: if chemical degradation is the primary cause at high temperatures, the Arrhenius model is appropriate; if mechanical fatigue is the primary cause, vibration stress and the inverse power law model should be considered.

Step 2: Design stress levels and sample allocation. Accelerated tests generally set 3 to 4 stress levels (including a low level close to normal stress for calibration), with appropriate samples allocated to each level. Stress levels should not be too low (as this would prolong the test time) or too high (as this could change the failure mechanism). An engineering guideline is to control the expected failure time at the highest stress level within the test cycle and ensure that the expected failure time at the lowest stress level does not exceed 5 to 10 times the test cycle, ensuring that failure data is available at each level.

Step 3: Execute the test and record failure times. During the test, strictly record the failure time, failure mode, and failure mechanism for each sample, and conduct regular failure analysis. Censored data (data from samples that did not fail by the end of the test) is common—this information, indicating that the "life is greater than the test time," is equally valuable and must be included in statistical analysis.

Step 4: Extrapolate life indicators under normal stress. Plot the characteristic life (such as median life) at each stress level against the acceleration stress, fit the acceleration model curve, and extrapolate to obtain the MTBF, B10 life, and other indicators under normal use conditions, along with their confidence intervals. For example, an automotive electronics company used an 85°C/85%RH humidity-temperature accelerated test to evaluate the life of an in-vehicle controller. By combining the Arrhenius model, they extrapolated the life characteristics under normal conditions, identifying the risk of waterproof failure due to sealant aging and completing material upgrades before mass production.

5. How to Use Reliability Data: MTBF, B10, and Weibull Analysis

Accelerated testing generates a set of failure time data, which must be statistically analyzed to become a basis for decision-making. The most commonly used tools are the Weibull distribution and MTBF/B10 indicators.

The Weibull distribution is the most flexible life distribution model in reliability engineering. Its shape parameter β describes the failure mode: β < 1 indicates early failures (the decreasing segment of the bathtub curve), β = 1 indicates random failures (a special case of the exponential distribution), and β > 1 indicates wear-out failures (the increasing segment of the bathtub curve). By fitting the Weibull distribution using probability plots or maximum likelihood estimation, the life point corresponding to any reliability level can be obtained. B10 life (the life at 90% reliability) is more practical than MTBF because it directly answers "when 10% of the products will fail," aligning more closely with customer expectations for "warranty periods."

MTBF (Mean Time Between Failures) is another commonly used indicator, but its limitations must be noted: MTBF is an "average" concept that masks the shape of the life distribution. Two products may have the same MTBF, but one may have a concentrated life span while the other may have more early failures, leading to different customer experiences. Therefore, it is recommended to report both MTBF and B10/B50 life, along with the lower confidence limit, rather than just a "beautiful" average value.

Several traps in data analysis must also be avoided: one is that a small sample size leads to a wide confidence interval, making the extrapolation results meaningless; another is treating censored data as failure data, underestimating the life; and a third is ignoring changes in the failure mechanism, forcing the extrapolation of high-temperature data to normal temperatures. The value of a reliability engineer often lies in controlling these details.

6. From Testing to Improvement: Closed-Loop Management in Reliability Verification

The purpose of reliability verification is not to "produce a report" but to "improve the product." A complete reliability verification should form a closed loop: test identifies weaknesses → root cause analysis → design improvement → retest verification → knowledge accumulation.

Step 1: Establish a failure review mechanism. Each failure in a reliability test should enter the failure analysis process, clearly identifying the failure mode, failure mechanism, responsible party, and corrective action, with a set closure deadline. It is recommended to create a "reliability issue list" and manage test failures as rigorously as customer complaints, tracking the closure rate weekly.

Step 2: Prioritize improvements. Not all weaknesses need immediate correction; they should be ranked based on the severity of the failure consequences, the probability of occurrence, and the cost of modification: failures affecting safety must be zero-tolerance; failures affecting primary functions should be prioritized; minor appearance-related failures can be documented and optimized as needed. For example, a construction machinery company found through HALT that a sensor occasionally misreported under extreme vibration. Although the trigger probability was low, it involved a safety shutdown function, and the issue was ultimately resolved through a structural vibration reduction solution.

Step 3: Retest to verify improvement effectiveness. The improved product must be retested to confirm that the original failure no longer occurs and that no new issues have been introduced. Improvement is not a "quick fix" but should involve "before and after" comparison data.

Step 4: Accumulate knowledge. Organize the typical failure modes, effective corrective actions, and applicable test methods identified in each reliability verification into the company's reliability knowledge base, forming a failure mode library and design specifications. This allows subsequent projects to "stand on the shoulders of giants." Reliability capability is a typical organizational asset that "grows thicker with use," and the better it is accumulated, the shorter and less costly the verification cycle for new projects will be.

7. Phased Implementation: Selecting Test Combinations for Different Product Types

Reliability verification efforts should be commensurate with product risk. Not all products require a full suite of tests, and companies can choose combinations based on product type and risk level.

High-risk, high-value products (such as automotive electronics, medical devices, and industrial control equipment): It is recommended to follow the "HALT + ALT + reliability growth" full process. HALT should be iterated early in R&D, ALT should quantitatively assess life before finalization, and reliability growth testing should span the entire development cycle. For these products, the cost of recalls and compensation far exceeds the cost of testing.

Medium-risk products (such as general consumer electronics, household appliances, and common components): It is recommended to use "simplified HALT + single-stress ALT." Use a concentrated HALT to identify weaknesses and a single-stress ALT (such as temperature or voltage) to assess life characteristics, with both the sample size and test cycle appropriately reduced.

Low-risk, mature products (such as conventional structural components and low-value consumables): Reliability sampling inspection or reference to similar products' reliability data may suffice, focusing resources on key characteristics. However, even for low-risk products, if the customer has specific reliability requirements (such as a contracted life indicator), verification must be conducted according to those requirements.

Regardless of the combination, two principles must be observed: first, reliability verification must be completed before mass production, not "tested while producing," as any issues identified during verification would incur much higher costs; second, verification results must be documented formally, serving as the basis for design reviews, customer audits, and product release.

8. Conclusion

Reliability verification is one of the highest return-on-investment activities in the R&D quality system: spending an extra week in the lab can save a whole vehicle from being returned in the market. HALT is responsible for exposing weaknesses, ALT for calculating life, Weibull and MTBF for clarifying the data, and closed-loop management for thoroughly implementing improvements. When reliability verification becomes a fixed checkpoint in the R&D process, rather than an optional add-on, the company's quality competitiveness is truly grounded in data and time.

In the lab, expose the weaknesses, and before mass production, calculate the life—reliability verification is the indispensable last mile of R&D quality.

Knowledge code: 8.2.3

Version: v20260804

Author: Quality Think Tank Quality Think Tank is dedicated to providing systematic professional knowledge, methodologies, and practical tools for quality management practitioners, helping companies continuously enhance their quality capabilities.