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You are here: Home / Archives for Articles / on Product Reliability

on Product Reliability

A listing in reverse chronological order of articles by:



  • Kirk Grey — Accelerated Reliability series

  • Les Warrington — Achieving the Benefits of Reliability series

  • Adam Bahret — Apex Ridge series

  • Michael Pfeifer — Metals Engineering and Product Reliability series

  • Fred Schenkelberg — Musings on Reliability and Maintenance series

  • Arthur Hart — Reliability Engineering Insights series

  • Chris Jackson — Reliability in Emerging Technology series

by Semion Gengrinovich Leave a Comment

ALT: An in Depth Description

ALT: An in Depth Description

Accelerated Life Testing (ALT) is a method used in reliability engineering to assess the lifespan and performance of a product under accelerated stress conditions. The goal is to uncover potential faults and failures in a shorter time frame than would be possible under normal operating conditions. ALT is particularly useful when the product’s expected lifespan is long, and waiting for failures to occur naturally is not feasible.

In the context of Design Verification (DV), ALT can help shorten the testing time by inducing failures more quickly than under normal conditions. This allows engineers to identify and address potential issues earlier in the product development process, thereby improving the product’s reliability and reducing the time to market.

For example, consider an aluminum part used in automotive applications. The primary stressor for this part would be cyclic mechanical loading, which can cause fatigue over time. An ALT would subject the part to this stress at an accelerated rate, causing it to fail more quickly than it would under normal conditions. By analyzing the part’s response to the test, engineers can predict its lifespan and maintenance intervals under normal service parameters.

Aluminum 6061 is a widely used alloy in the automotive sector due to its excellent strength-to-weight ratio, corrosion resistance, and weldability, making it suitable for various applications including frame components, wheels, and suspension parts.

Yield Strength and Stress Levels

The yield tensile strength of 6061 aluminum alloy is 276 MPa (40,000 psi) please see (Fig. 1)

Based on this, we define three stress levels for the ALT:

  1. First Stress Level (90% of Yield Strength): This level is set at 90% of the yield strength, which calculates to 248.4 MPa.
  2. Second Stress Level (80% of Yield Strength): This level is set at 80% of the yield strength, which calculates to 220.8 MPa.
  3. Third Stress Level (70-75% of Yield Strength): For a range, we consider the average, setting this level at 72.5% of the yield strength, which calculates to 200.1 MPa.
Fig.1 Stress-Strain, Aluminium 6061

For every stress level 3 samples was failed, fitting the Weibull distribution and choosing different Reliability and confidence level, (pay attention axes are in Logarithmic scale, see Fig. 2):

Fig.2 Stress Amplitude vs. time to failure.

The blue step function (see Fig.3) appears to represent the stress amplitude levels and the corresponding number of cycles at each level that the part is expected to experience throughout its life. The steps in the function indicate changes in stress amplitude, with each horizontal segment corresponding to a period where the stress amplitude remains constant.

Visually, the blue step function does not intersect or cross the black solid line labeled as R99.9 C99.9. This suggests that the stress levels and the number of cycles at each level are within the bounds of the R99.9 C99.9 reliability and confidence level.

In other words, the part is expected to withstand the stress levels represented by the blue line without reaching the failure criteria associated with the R99.9 C99.9 line.

According to Miner’s rule, the cumulative damage is calculated by summing the ratios of the number of cycles experienced at each stress level to the number of cycles to failure at that stress level. Since the blue line does not cross the black line, it implies that the cumulative damage DD is less than 1, meaning the part is not expected to fail within the life cycle represented by the blue line, given the reliability and confidence level of R99.9 C99.9.

Fig. 3 Stress amplitude vs cycle to failure, and the stress amplitude usage stress (blue line)

The ability to predict failures and estimate product lifespan through ALT enables companies to enhance product design, improve quality, and ensure reliability before market release. It also facilitates the identification of potential failure modes and the development of maintenance schedules, which are crucial for safety-critical components.

Furthermore, ALT can be tailored to specific reliability and confidence level targets, such as R99.9 C99.9, allowing engineers to validate that products meet stringent reliability standards.

By leveraging the insights gained from ALT, companies can achieve a competitive edge by reducing time-to-market and minimizing the risk of costly recalls or customer dissatisfaction due to product failures.

The process of conducting an ALT typically involves the following steps:

Define the test objectives: This could be to identify failure modes, estimate the part’s life expectancy, or verify design margins.

Select the stressors: These are the types of stresses the part will experience in service. For an aluminum part, the primary stressor would be cyclic mechanical loading.

Develop the test plan: This involves designing the test to apply the selected stressors in a controlled and measurable way.

Conduct the test: The test is run according to the plan, and the part is monitored for signs of fatigue. The test continues until the part fails or reaches a predetermined number of cycles.

Analyze the data: The results are used to estimate the part’s life under normal conditions. This can involve statistical analysis and modeling.

In conclusion, ALT is a valuable tool in reliability engineering and DV, allowing for the prediction of product lifespan and the identification of potential issues in a shorter time frame than under normal conditions. This can lead to improved product reliability, reduced testing time, and faster time to market.

Filed Under: Articles, on Product Reliability, Reliability Knowledge

by Semion Gengrinovich Leave a Comment

Importance of Failure Repeatability

Importance of Failure Repeatability

The importance of failure repeatability lies in its role in understanding and improving systems, processes, and products. Failure repeatability refers to the consistency with which a system or device can reproduce an outcome under unchanged conditions. In the context of product design engineering and testing, being able to consistently replicate a failure means that the underlying cause can be more accurately identified and addressed.

[Read more…]

Filed Under: Articles, on Product Reliability, Reliability Knowledge

by Semion Gengrinovich Leave a Comment

Degradation test and Diagnostics

Degradation test and Diagnostics

Degradation testing for electromechanical components such as pumps, valves, and sensors involves a series of steps to identify wear and tear that could lead to system failure. The goal is to detect these signs of degradation early enough to replace the part and prevent system failure.

For pumps, degradation can be monitored by sensors. A study on gear pumps used an accelerated life test (ALT) to monitor the degradation state. The volumetric efficiency of the pumps was measured over time, and the wear clearances were recorded. As the wear gap increased, the flow rate gradually decreased, indicating wear degradation.

[Read more…]

Filed Under: Articles, on Product Reliability, Reliability Knowledge

by Semion Gengrinovich Leave a Comment

Basics of 5 Whys

Basics of 5 Whys

The 5-Whys approach in product development enhances reliability by understanding failure modes. The 5-wys technique is a powerful tool for root cause analysis. Originally developed by Sakichi Toyoda and later popularized by Keiichi Ono.

[Read more…]

Filed Under: Articles, on Product Reliability, Reliability Knowledge

by Laxman Pangeni Leave a Comment

Power-law vs. Exponential Acceleration Models

Power-law vs. Exponential Acceleration Models

Making the Right Choice in Reliability Engineering

In reliability engineering, we often need to extrapolate test data collected under accelerated stress conditions to predict performance under normal operating conditions. Two mathematical models commonly used for this purpose are the power-law model and the exponential model. But which one should you choose when your data fits both models equally well? This article explores the differences between these models and provides practical guidance on making this critical decision.

/more

Understanding Acceleration Factor ModelsAcceleration factor (AF) models allow us to relate the Time-to-Failure (TF) under accelerated test conditions to the expected lifetime under normal operating conditions. These models are essential for predicting product reliability without waiting for failures to occur under normal use conditions.The two primary models are:

  1. Power-law model: TF = A × S^(-n) Where S is the stress level, A is a constant, and n is the power-law exponentExponential model: TF = B × exp(-C×S) Where S is the stress level, B is a constant, and C is the exponential coefficient

Both models can often fit the same accelerated test data with similar statistical goodness of fit, yet they may predict dramatically different lifetimes when extrapolated to lower stress levels.The Conservative Approach: When Both Models FitWhen faced with data that can be reasonably fitted by either model, reliability engineers should consider a fundamental principle: choose the model that provides the more conservative prediction.Based on extensive empirical evidence, including the example shown in the image, the exponential model typically produces:

  • Smaller TF (Time-to-Failure) valuesSmaller AF (Acceleration Factor) values

When extrapolating from stress conditions to use conditions, these smaller values represent a more conservative approach. This is why the exponential model is often referred to as the “conservative model” in reliability engineering.

Physics of Failure Considerations

While the conservative approach is generally advisable, understanding the underlying physics of failure can provide additional insights for model selection:

  • If there’s a clear physical mechanism that supports one model over the other, that model should be preferred
  • Different failure mechanisms may be better represented by different models
  • Temperature or stress thresholds may exist where the dominant failure mechanism changes

For example, in a servo motor with integrated planetary gearbox, the failure mechanism transitions from mechanical fatigue at normal temperatures (with activation energy Q_fatigue) to lubricant oxidation at higher temperatures (with activation energy Q_oxidation, where Q_oxidation > Q_fatigue). This shift in failure physics significantly impacts model selection—power-law models may better represent mechanical wear at normal temperatures, while exponential models better capture the accelerated chemical degradation of lubricants at elevated temperatures.

A Practical Example: Visualizing the Difference

Let’s examine a practical example from robotics: accelerated testing of bearings in a servo motor used in industrial robots. Engineers need to predict bearing life under normal operation but can only collect data under accelerated conditions using increased loads. When no information about the failure mechanisms (or PoF) is know fitting both the models to the same TF data shows good fit. However, the values are significantly different when prediction at lower use-case stress is done.

For example, in a servo motor with integrated planetary gearbox, the failure mechanism transitions from mechanical fatigue at normal temperatures (with activation energy Q_fatigue) to lubricant oxidation at higher temperatures (with activation energy Q_oxidation, where Q_oxidation > Q_fatigue). This shift in failure physics significantly impacts model selection—power-law models may better represent mechanical wear at normal temperatures, while exponential models better capture the accelerated chemical degradation of lubricants at elevated temperatures.

Conclusion

When choosing between power-law and exponential models that fit your accelerated test data equally well:

  1. Default to the conservative approach: Use the exponential model for more conservative predictions unless there’s compelling evidence to do otherwise.
  2. Consider physics of failure: If you have a solid understanding of the underlying failure mechanisms, use this knowledge to guide your model selection.
  3. Perform sensitivity analysis: Evaluate how model selection affects your reliability predictions and risk assessments.
  4. Document assumptions: Clearly articulate your rationale for model selection in reliability reports and assessments.

By following these guidelines, reliability engineers can make more informed decisions about acceleration models, leading to more reliable products and systems.


What has been your experience with acceleration factor models? Have you encountered situations where model selection significantly impacted your reliability predictions? Share your thoughts in the comments below.

Filed Under: Articles, on Product Reliability, Reliability by Design

by Semion Gengrinovich Leave a Comment

Preventive maintenance

Preventive maintenance

Preventive maintenance is a proactive approach used by industries to ensure the longevity and optimal performance of their assets. It involves regular maintenance tasks such as cleaning, lubrication, parts replacement, and equipment repairs to prevent unplanned downtime and costly breakdowns.

[Read more…]

Filed Under: Articles, on Product Reliability, Reliability Knowledge

by Semion Gengrinovich Leave a Comment

SW reliability

SW reliability

Software reliability and Hardware reliability are two distinct Concepts within the field of engineering each with its own unique characteristics and measurement challenges.

[Read more…]

Filed Under: Articles, on Product Reliability, Reliability Knowledge

by Laxman Pangeni Leave a Comment

Markov Chain Analysis

Markov Chain Analysis

Markov Chain Analysis and Eigenvector/Eigenvalue Problem: A Powerful Tool for Reliability Engineering

In reliability engineering, predicting system behavior over time is crucial for maintenance planning and risk assessment. One powerful mathematical tool for this analysis is Markov Chain modelling. In this article, I’ll demonstrate how Markov Chains can predict device reliability using a real-world example: battery reliability in reliability testing facilities.

[Read more…]

Filed Under: Articles, on Product Reliability, Reliability by Design

by Semion Gengrinovich Leave a Comment

How to Fit Data to a Distribution

How to Fit Data to a Distribution

Understanding the different types of data and their respective uses is critical for product development, testing, and analysis. Each type of data plays a role in ensuring that products meet quality standards and fulfill user needs. As a mechanical engineer with a focus on R&D testing and data analysis, you would likely encounter and utilize these various data types throughout the product development and validation process.

[Read more…]

Filed Under: Articles, on Product Reliability, Reliability Knowledge

by Semion Gengrinovich Leave a Comment

How to Define Proper Product Reliability Goal Video

How to Define Proper Product Reliability Goal Video

Defining a proper product reliability goal is a critical step in ensuring that a product meets customer expectations and performs adequately throughout its intended lifespan. This also involves a careful balance between the required level of reliability and the associated costs and complexities of achieving that reliability.

[Read more…]

Filed Under: Articles, on Product Reliability, Reliability Knowledge

by Laxman Pangeni Leave a Comment

The Power of the Damage-Endurance Model

The Power of the Damage-Endurance Model

Unlocking Reliability: The Power of the Damage-Endurance Model in Product Development

Reliability is at the heart of robust product design. Engineers and reliability professionals continuously seek ways to predict, assess, and improve product longevity. One fundamental approach to achieving this is the Damage-Endurance Model, a powerful tool in reliability engineering that helps quantify failure risks and optimize designs.

[Read more…]

Filed Under: Articles, on Product Reliability, Reliability by Design

by Semion Gengrinovich Leave a Comment

How to Define Proper Product Reliability Goal

How to Define Proper Product Reliability Goal

Defining a proper product reliability goal is a critical step in ensuring that a product meets customer expectations and performs adequately throughout its intended lifespan. This also involves a careful balance between the required level of reliability and the associated costs and complexities of achieving that reliability.

[Read more…]

Filed Under: Articles, on Product Reliability, Reliability Knowledge

by Semion Gengrinovich Leave a Comment

What is GR&R (video)

What is GR&R (video)

Gage repeatability and reproducibility (GR&R) is a statistical tool used in quality control to assess a measurement system’s capability. It evaluates the amount of variation in the measurement data that is due to the measurement system itself rather than the product being measured. GR&R helps to determine if a measurement system is reliable and whether it’s producing repeatable and reproducible measurements.

[Read more…]

Filed Under: Articles, on Product Reliability, Reliability Knowledge

by Laxman Pangeni Leave a Comment

Leveraging Bayesian Statistics for Reliability Predictions with Limited Sample Data

Leveraging Bayesian Statistics for Reliability Predictions with Limited Sample Data

Example from BDC Motor Reliability Assessment

Reliability analysis is essential for ensuring long-term performance of hardware components. However, predicting failures with small sample sizes is a challenge. Traditional statistical methods often require large datasets, whereas Bayesian statistics can incorporate prior knowledge to improve predictions by updating beliefs as new data becomes available.

[Read more…]

Filed Under: Articles, on Product Reliability, Reliability by Design

by Semion Gengrinovich Leave a Comment

What is GR&R

What is GR&R

Gage Repeatability and Reproducibility (GR&R) is a statistical tool used in quality control to assess a measurement system’s capability. It evaluates the amount of variation in the measurement data that is due to the measurement system itself, rather than the product being measured.

[Read more…]

Filed Under: Articles, on Product Reliability, Reliability Knowledge

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