Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of articles by:
When faced with recurring equipment failures, identifying the root cause is only half the battle. The real challenge lies in implementing an effective solution that addresses the issue while considering various stakeholder perspectives. In this article, we’ll explore a comprehensive approach to optimizing decision-making for equipment failure solutions, involving key teams across the organization.
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In Part 1 of Beyond the Numbers, I reflected on why Human Factors matter in reliability engineering and how the human element of the system can be overlooked by traditional hardware-focussed approaches.
This article explores how Human Factors principles can be integrated into traditional reliability analyses such as Reliability Block Diagrams (RBDs), Fault Tree Analysis (FTA) and Failure Mode, Effects and Criticality Analysis (FMECA) and without reinventing the wheel or introducing additional complexity.
The short answer is that we are already doing much of this implicitly. Applying Human Factors principles makes those assumptions explicit and therefore visible, challengeable and open to improvement. [Read more…]
Yes, reliability testing can be done in parallel with design validation (DV). This approach has both advantages and disadvantages, which are important to consider in the context of product development and testing.
We were chatting with some coworkers about stand-up comedians, and someone mentioned that even the most popular comedians try out their new material in small venues before doing a big show for a larger audience. They do this to collect feedback and fine-tune their performance before reaching thousands of people across different cities.
My reliability engineering brain immediately reacted: “This means even stand-up comedians understand the importance of sampling.” They perform a small-scale version of their show to understand how the audience reacts. Based on that feedback, they decide whether the show is ready for a wider audience. In other words, they want to estimate the population’s reaction based on a sample. To me, this is a perfect example of statistical sampling.
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Site Reliability Engineering vs Hardware Reliability Engineering: Distinct Disciplines with Shared Goals
In the world of engineering, reliability is a crucial aspect that spans various domains. Two fields that often get confused due to their similar names are Site Reliability Engineering (SRE) and Hardware Reliability Engineering. While both aim to ensure the dependability of systems, they focus on vastly different areas and employ distinct methodologies. Let’s explore the key differences between these two disciplines and delve into the history behind the SRE naming convention.
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In the realm of mechanical engineering, the reliability and longevity of pumps are critical for numerous industrial applications. Predicting wear-out of pumps can significantly enhance maintenance strategies, reduce downtime, and optimize operational efficiency. One effective approach to predicting wear-out is by monitoring the revolutions per minute (RPM) of pumps over a large population and extended time periods. This article delves into the methodology of creating a wear-out prediction model based on RPM measurements.
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Coordinating multiple improvement programs effectively within an organization requires a structured approach that ensures alignment, communication, and collaboration across various teams and departments. Here are some key strategies and techniques to achieve this:
– Align Teams Around a Common Purpose: Ensure that all teams understand and are committed to the overarching goals of the organization. This shared sense of purpose helps keep everyone focused and motivated.
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In the realm of complex electromechanical systems, reliability allocation is a critical process that demands a delicate balance between safety, cost, and performance. As engineers, we are tasked with creating systems that not only meet functional requirements but also operate safely, efficiently, and cost-effectively over their intended lifespan. This article explores key strategies for optimal reliability allocation, emphasizing the importance of safety while considering economic factors and system performance.
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For reliability engineers, numbers can be our comfort zone. Predictions, modelling, analysis and results, work that is rigorous, data-driven, and essential for complex systems – but does it always tell the full story?
Recently completing Level 1 of the CIEHF Cross-Sector Learning Pathway has led me to consider system reliability in a different light. Technical performance alone does not guarantee success and to truly understand reliability, we must also account for the human component – the operators, maintainers, and decision-makers whose actions are inseparable from system outcomes.
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In today’s data-driven business landscape, two roles have emerged as critical to improving company strategy through data analysis and system optimization: data scientists and reliability engineers. While these roles have distinct focuses, they share common skills and often work together to drive organizational success. This article will explore the similarities and differences between data scientists and reliability engineers, highlighting how their skills complement each other in day-to-day activities and contribute to data-driven decision-making.
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Newton’s 3rd Law and Systems Thinking are two of the most important concepts every engineer needs to understand deeply. I hear you saying, “Come on, Ayaz, which engineer does not know about Newton’s 3rd Law?”
My response? Quite a lot.
I am not talking about memorizing the formula and applying it in a calculation. I am talking about truly understanding its philosophy. It is a philosophy that explains the nature of interrelated, complex systems. There is no absolute win in this world. We live in a complex reality where everything, literally everything, is connected.
Do you remember the concept of the Butterfly Effect? It is the idea that a small change in one place, like a butterfly flapping its wings in the Savanna, can eventually cause a tornado somewhere else. It illustrates that a change in one part of a system inevitably causes a change in another part. Good engineering is to be aware of these impacts and manage them.
Engineering works the same way. You push on one parameter, and another one reacts.
In the world of reliability and maintainability, we have a concept called RAM (Reliability, Availability, and Maintainability) analysis. It fits perfectly into the systems thinking mindset because it forces us to find a reasonable balance between competing demands.
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Achieving 99% reliability versus 99.9999% reliability (1 part per million failure rate) represents a massive leap in quality and performance, especially for electromechanical devices in the automotive and aviation industries. The efforts required to reach these levels of reliability differ significantly:
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After conducting a thorough root cause analysis and identifying the underlying issues behind a problem, the next critical step is developing and implementing effective solutions. However, this process requires careful planning and consideration of various factors to ensure the solution addresses the root cause and can be successfully deployed. This article explores key considerations when implementing solutions, particularly for products already in the field.
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The 5 Whys technique is a powerful tool for root cause analysis, originally developed by Sakichi Toyoda and later popularized by Taiichi Ohno within the Toyota Production System. This method involves asking “why” five times to drill down to the root cause of a problem, rather than just addressing its symptoms. In the context of product development, particularly in the automotive industry, the 5 Whys approach can significantly improve overall reliability by uncovering the true sources of failure modes.
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In the field of reliability engineering, distinguishing between correlation and causation is essential for accurately identifying the root causes of system failures and ensuring the reliability of engineered systems.
Correlation refers to a statistical relationship between two variables where changes in one variable are associated with changes in another. However, this relationship does not imply that one variable causes the other to change. For example, an increase in ice cream sales and shark attacks are correlated because both increase during the summer, but one does not cause the other.
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