on Product Reliability

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.

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:

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.

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.

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.

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.

In the bustling Boeing factory in Seattle Engineers work diligently on the 737 Max. Alex Carter an engineer notices discrepancies in the AOA sensors test results. His concerns are overlooked amid the pressure to meet production deadlines. In a Sleek boardroom, senior executive Mark Thompson stresses the importance of production targets. Discussions over the MCOS system reveal it relies on a single AOA sensor prompting safety Advocate Sarah Lee to voice her concern.

To design more reliable electromechanical components and delay wear out, several strategies can be employed, focusing on material selection, design principles, and reliability testing.

Material Selection:

– Hardness: Selecting materials with high hardness can resist wear from abrasion and erosion. For example, ceramics like alumina (Al2O3) and silicon carbide (SiC) are highly resistant to wear and are suitable for components exposed to abrasive environments. [Read more…]

The Mars Climate Orbiter was a NASA spacecraft launched on December 11th 1998 as part of the Mars Surveyor ’98 program. Its primary mission was to study the Martian climate and atmosphere, map surface changes, and act as a communications relay for the Mars Polar Lander. The Orbiter aimed to monitor daily weather and atmospheric conditions record surface changes and search for water evidence to better understand Mars climate history. Unfortunately, a unit conversion error led to its destruction upon entering Mars atmosphere in September 1999.

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.