Interconnected gears and circuits symbolizing system reliability.

Is Your Tech Built to Last? How to Navigate Reliability in a Complex World

Soraya Malik in Tech & Innovation September 2025 • 4 min read.

"Understanding dependent failure processes can help engineers design more robust and reliable systems for the future."

In today's world, we rely on technology more than ever. From the smartphones in our pockets to the complex machinery that powers industries, we expect these systems to work reliably, day in and day out. However, as technology becomes more intricate, ensuring this reliability becomes increasingly challenging. Traditional methods of testing and predicting failures are no longer sufficient for the complex systems we depend on.

One of the biggest challenges is that many systems are subject to multiple failure mechanisms that can interact with each other. For example, a micro-electro-mechanical system (MEMS) device might fail due to both gradual wear and tear (a 'soft' failure) and sudden shocks or stresses (a 'hard' failure). These failures aren't always independent; a shock might accelerate the degradation process, making it even harder to predict when the system will fail. This concept is known as dependent competing failure processes (DCFPs).

To tackle this challenge, researchers are developing new reliability assessment models that take into account the dependencies between different failure processes. These models use advanced statistical techniques, such as copulas, to capture the correlations between factors like random shocks and gradual degradation. By understanding these relationships, engineers can design more robust systems and predict failures more accurately.

Decoding Dependent Failure Processes: Why Traditional Reliability Models Fall Short

Interconnected gears and circuits symbolizing system reliability.

Traditional reliability models often assume that different failure modes in a system are independent of each other. This assumption simplifies the analysis, but it can lead to inaccurate predictions when dealing with complex systems where failures are interconnected. In reality, many systems experience dependent competing failure processes (DCFPs), where one failure mode can influence the likelihood or severity of another.

Consider the example of a railway axle. It is subject to both gradual degradation due to fatigue from rolling loads and sudden shocks from impacts as the train travels. These two failure processes are not independent. A severe shock can cause an immediate hard failure if the stress exceeds the axle's strength. Shocks also cause additional abrupt degradation to the axle. Ignoring this correlation can lead to underestimating the overall failure risk.

Ignoring Interdependencies: Traditional models often treat failure modes as separate entities, which doesn't reflect real-world scenarios.
Oversimplification: Assuming independence simplifies calculations but sacrifices accuracy in complex systems.
Inaccurate Predictions: Failing to account for correlations can lead to unreliable estimates of system lifespan and maintenance needs.

To address these limitations, new reliability assessment models are needed that can capture the dependencies between different failure processes. These models must be able to handle complex relationships and provide more accurate predictions of system reliability.

The Future of Reliability: Embracing Complexity for Safer, More Durable Technology

As technology continues to advance, ensuring the reliability of complex systems will become even more critical. By embracing new modeling techniques that account for dependent failure processes, engineers can design systems that are more robust, durable, and safe. This approach will not only improve the performance and lifespan of individual products but also contribute to a more reliable and sustainable technological future.

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Everything You Need To Know

Why are traditional reliability models becoming insufficient for assessing modern technology?

Traditional reliability models often assume that different failure modes are independent. However, in complex systems, failures are frequently interconnected, exhibiting dependent competing failure processes (DCFPs). These processes involve interactions between different failure mechanisms, such as gradual wear and sudden shocks, making traditional models inadequate. For example, micro-electro-mechanical system (MEMS) devices may fail due to wear and shocks that accelerate degradation. Addressing this requires models that capture these dependencies using advanced statistical techniques like copulas.

What are dependent competing failure processes (DCFPs), and why are they important in reliability engineering?

Dependent competing failure processes (DCFPs) occur when different failure modes within a system influence each other. Unlike traditional models that assume independence, DCFPs recognize that one failure mode can affect the likelihood or severity of another. This is vital in reliability engineering because ignoring these correlations can lead to inaccurate predictions about system lifespan and maintenance needs. For example, in a railway axle, fatigue from rolling loads and shocks from impacts are not independent; a severe shock can cause a hard failure and accelerate degradation, both of which must be accounted for.

How can engineers design more robust systems by understanding the relationships between failure processes?

By understanding the relationships between different failure processes, engineers can employ advanced statistical techniques like copulas in reliability assessment models. These models capture the correlations between factors like random shocks and gradual degradation. For example, considering the interplay between soft failures (gradual wear) and hard failures (sudden shocks) in systems like micro-electro-mechanical system (MEMS) devices allows for designing systems that are more resilient to both types of failures, improving overall reliability and durability.

What is the impact of ignoring interdependencies between failure modes in complex systems?

Ignoring interdependencies between failure modes in complex systems can lead to several negative outcomes. Traditional models often treat failure modes as separate entities, which oversimplifies real-world scenarios where failures are interconnected. This oversimplification results in inaccurate predictions of system lifespan and maintenance needs. For instance, the failure of a railway axle due to fatigue and shocks, if treated independently, can underestimate the overall failure risk. Addressing these limitations requires adopting new reliability assessment models capable of handling complex relationships.

How do new reliability assessment models improve upon traditional models in predicting system failures?

New reliability assessment models improve upon traditional models by accounting for the dependencies between different failure processes, which is crucial for complex systems. Traditional models often assume independence between failure modes, leading to inaccurate predictions. The new models incorporate advanced statistical techniques, such as copulas, to capture correlations between factors like random shocks and gradual degradation, providing more accurate predictions of system reliability. For example, in systems subject to dependent competing failure processes (DCFPs), these models can better estimate the combined impact of wear and sudden stresses.