Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of articles by:
The duty cycle of a fuel cell engine, particularly in the context of acceleration and deceleration of a vehicle, is a critical aspect that influences the performance, efficiency, and longevity of the fuel cell system. Understanding the effects of these dynamic conditions on fuel cell engines is essential for optimizing their operation and addressing potential challenges.
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Most customers aren’t happy with products that break down a lot.
Product unreliability has been behind a lot of losses, cancellations and bankruptcies. A great example is the Yugo – a small, inexpensive car from the former Yugoslavia that gained notoriety in the 1980s and 1990s. It was about half as expensive as similar cars when it hit the US market. And despite a lot of initial ‘excited’ purchasers, it simply stopped selling when people realized how comically unreliable and unsafe it was.
Now there is Rivian. When Rivian burst onto the electric vehicle (EV) scene, it quickly gained a devoted following. The Rivian R1T truck and R1S SUV were praised for their funky designs and innovative features. Owners and enthusiasts love the company’s approach to off-road lifestyles, reinforced by early partnerships with Amazon and promises of building a nationwide ‘outdoor-centric’ charging network.
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Generally speaking, pushing value through functional silos usually creates inefficiencies, low buy-in, and a lack of ownership. Resistance to change is almost guaranteed, not to mention the problems caused by missed or poor communication. The same applies to reliability engineering activities during product or process development, which often face strong resistance from design teams, to say the least.
In my 12 years of reliability experience, I’ve seen that reliability engineering is often viewed as just another function — another group asking designers to do extra work because their “baby” (design) is “ugly.” This mindset exists largely because traditional engineering education focuses more on how things function given the boundary conditions, not how they degrade, specifically, how they degrade over time. Let me explain.
Meantime between failures MTF is a widely used reliability metric in various Industries, particularly in electronics and manufacturing. While it can be a valuable tool in certain scenarios, its misuse or misinterpretation can lead to costly mistakes. Let’s explore when MTBF is applicable and when it might be inappropriate to use.
By understanding when to use MTB and when to seek alternative methods, organizations can make more informed decisions about product design, maintenance strategies, and reliability improvements, ultimately leading to better products and more satisfied customers.
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In the world of reliability engineering, few tools are as powerful – or as misunderstood – as Weibull analysis. It is a statistical tool, which can turn people ‘off’ straight away, as we can immediately leap to a list of equations and tables of numbers that we need to memorize and embrace. But Weibull analysis is different. You actually don’t need equations.
Check out the chart below, which is called a ‘Weibull plot.’ The different shapes and colours representing the failures of different components can tell us so many things without having to evaluate a single equation.
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I usually write articles about topics I personally struggled to understand from the sources available to us such as books, online resources, and so on. I believe most technical concepts are fairly straightforward at their core, but the way we express ideas and translate our understanding into writing often makes them harder for others to grasp. That’s an area where we can all continue to improve.
As part of that journey, my goal with the Breaking Bad for Reliability newsletter is to be a communicator of Reliability Engineering principles, and I am doing this mainly for two categories of people: [Read more…]
Quantitative and qualitative risk analyses are two fundamental approaches in risk management, each with its distinct advantages, limitations, and applicability to different scenarios, especially in the context of product development
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Modern militaries don’t win many wars these days. The most dominant, well-funded, highly-trained armies have consistently lost (or at least not won) the Korean War, Vietnam War, Afghanistan War, arguably the Iraq War, and plenty of others. And many dominant, well-funded, highly-trained companies are consistently spitting out unreliable or unimaginative products that smaller and less dogmatic companies have no problem bettering.
To understand why, let’s look at a man called Robert McNamara.
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September 2013. That was the date I accepted an offer for a Maintenance and Reliability Engineer role in one of the largest oil and gas service companies in the world. I had no clue what the role really meant. Since it was about industrial equipment, I thought, I am a Mechanical Engineer, I will figure it out.
Later I realized it was not only me who did not know what reliability engineers do. More or less all my stakeholders, including my manager, were not very clear about it either. So, I had to learn on my own, and later find ways to add value to the business.
If you are a fresh graduate engineer looking to build a career in reliability engineering, or a company unsure about what type of engineering service to seek or who to hire to improve product or process reliability, you are in the right place.
By spending just 10 minutes reading this article, you will learn:
As industries increasingly rely on complex electromechanical systems, the importance of Return Parts Analysis (RPA) cannot be overstated. This crucial process involves forensic examination of failed components to determine root causes and prevent future issues. Let’s explore why RPA is essential, particularly for components like valves, sensors, pumps, and electrical boards.
Return Parts Analysis provides invaluable insights into component failures offering benefits that extend far beyond simple troubleshooting.
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Statistical tools play a critical role in product validation, particularly in the automotive industry where ensuring quality, safety, performance, and reliability is paramount. The search results provide a wealth of information on various statistical tools and methods that are commonly used during the different stages of product validation. Here are some of the most frequently used statistical tools based on the provided sources.
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In the automotive industry, particularly when dealing with electromechanical components, the validation process is a critical phase that ensures the safety, reliability, and performance of products before they reach the market. However, there is a growing Trend among companies to avoid failures during this process. Often leading to panic when they occur.
This article explores why encountering failures during validation should be seen as a blessing rather than a disaster.
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Burn-in testing is a critical reliability testing method used extensively in the electronics and electromechanical industries to identify and eliminate early-life failures in components. This process involves subjecting components or systems to normal or elevated stress conditions to accelerate the occurrence of latent defects. Here’s a detailed exploration of how burn-in testing works, its methodologies, and its importance in ensuring product reliability:
The primary goal of burn-in testing is to detect early failures, also known as infant mortality failures, in components before they are integrated into final products or shipped to customers. These failures typically occur due to defects introduced during the manufacturing process or inherent weaknesses in materials that manifest under operational stress. [Read more…]
The power of historical failure data is a gold mine of information for reliability engineers. It provides a window into the life cycle of products, revealing patterns and trends that can inform future designs and manufacturing processes. By analyzing this data, we can identify common failure modes, detect early life failures indicating quality or production issues, determine the onset of wear-out stages, and predict time to failure for similar products.
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Predicting all failures in any system, including the automotive industry, is an inherently complex and challenging task. The inherent unpredictability of certain failure modes, coupled with the vast array of variables in operational environments, makes it impossible to foresee every potential failure. However, through strategic approaches and methodologies, it is possible to minimize major failures and reduce the infant mortality failure rate, thereby enhancing the reliability and safety of automotive products.
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