The discussion focuses on harnessing real-world data to refine mission profiles, moving from rigid deterministic approaches to flexible stochastic methods. Environmental factors like temperature and humidity are crucial in determining component reliability. The speakers highlight the risks of selection bias in data analysis and emphasize the importance of accurate usage behavior to avoid over-engineering. They also navigate the balance between leveraging connectivity for data collection and protecting user privacy, advocating for critical thinking in data-driven decisions.
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question_answer ANECDOTE
Game Controller Button Usage Story
A game controller team used vendor claims on button durability without knowing actual user button press frequency.
Realizing user behavior diverged from assumptions highlights the need for real usage data in design.
insights INSIGHT
Embrace Stochastic Mission Profiles
Mission profiles should shift from deterministic fixed worst-case values to stochastic distributions reflecting real variability.
This enables better risk understanding and design optimization based on usage and environmental variance.
question_answer ANECDOTE
Humidity Sensor Benefit Example
Using humidity sensors in printers cut unnecessary delays for drying times in low-humidity areas.
This customer benefit stemmed from understanding environmental conditions beyond worst-case assumptions.
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How to use data from connected devices for derivation of mission profiles
Abstract
Enrico and Fred discuss the value of using real-world usage data to define mission profiles, advocating for a shift from deterministic to stochastic approaches. They touch on the impact of environmental conditions, the risk of selection bias in data analysis, and weigh the cost-benefit of using connected devices to improve design and reliability decisions.
Key Points
Topics include:
Importance of Evaluating Real Usage for Mission Profiles
Emphasized the need to base mission profiles on real-world usage rather than assumptions or specifications.
Highlighted how inaccurate assumptions can lead to over- or under-designed components.
Shift from Deterministic to Stochastic Approaches
Discussed the limitations of deterministic methods that rely on fixed worst-case values.
Advocated for using statistical distributions to represent real-world variability in loads and conditions.
Stochastic modeling helps in better understanding risk and optimizing designs.
Environmental Conditions Matter
Explored how temperature, humidity, and other environmental factors significantly influence component reliability.
Real usage data helps reveal operating conditions that differ from lab environments.
Selection Bias in Data Collection
Talked about the risk of drawing conclusions from biased datasets e.g., focusing only on failed products or a non-representative user group.
Stressed the importance of capturing a broad and representative sample for analysis.
Cost-Benefit of Connected Devices
Evaluated the investment in IoT-enabled devices for capturing usage and environmental data.
Weighed the upfront cost of connectivity and data management against long-term savings through better product design, reduced warranty costs, and predictive maintenance.
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