2016•Unpublished venueRequires access

Data driven design for reliability

Celine Geiger, Georgios Sarakakis

Open publisher page 15 citations

Abstract

Reliability is one of the driving factors towards perceived product value and brand image. In addition, reliability in the automotive industry plays a crucial role for product safety. During fast-paced product development, design for reliability is essential to guarantee product reliability while maintaining short iteration times. This requires detailed knowledge of relevant failure modes, loads and use cases. However, most product and test design is based on assumptions, estimations and experience. Here we show how to leverage field data to design reliability into the product along its whole development cycle. We use field reliability data to derive the reliability allocation which drives the reliability targets for the systems and subsystems. Based on actual user data, we provide design engineers with requirements for relevant use cases. Furthermore, the data helps us to develop user-specific test profiles. Our approach shows how data driven product development can reduce development times and leads to more user centered product designs. Additionally, using field data derived test profiles reveals relevant failure modes during testing and simulation. We anticipate our data driven design for reliability (DfR) process to positively influence the way reliability is implemented.

About this research paper

What this paper is about

Reliability is one of the driving factors towards perceived product value and brand image. In addition, reliability in the automotive industry plays a crucial role for product safety. During fast-paced product development, design for reliability is essential to guarantee product reliability while maintaining short iteration times. This requires detailed knowledge of relevant failure modes, loads and use cases. However, most product and test design is based on assumptions, estimations and experience. Here we show how to leverage field data to design reliability into the product along its whole development cycle. We use field reliability data to derive the reliability allocation which drives the reliability targets for the systems and subsystems. Based on actual user data, we provide design engineers with requirements for relevant use cases. Furthermore, the data helps us to develop user-specific test profiles. Our approach shows how data driven product development can reduce development times and leads to more user centered product designs. Additionally, using field data derived test profiles reveals relevant failure modes during testing and simulation. We anticipate our data driven design for reliability (DfR) process to positively influence the way reliability is implemented.

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Available abstract

Reliability is one of the driving factors towards perceived product value and brand image. In addition, reliability in the automotive industry plays a crucial role for product safety. During fast-paced product development, design for reliability is essential to guarantee product reliability while maintaining short iteration times. This requires detailed knowledge of relevant failure modes, loads and use cases. However, most product and test design is based on assumptions, estimations and experience. Here we show how to leverage field data to design reliability into the product along its whole development cycle. We use field reliability data to derive the reliability allocation which drives the reliability targets for the systems and subsystems. Based on actual user data, we provide design engineers with requirements for relevant use cases. Furthermore, the data helps us to develop user-specific test profiles. Our approach shows how data driven product development can reduce development times and leads to more user centered product designs. Additionally, using field data derived test profiles reveals relevant failure modes during testing and simulation. We anticipate our data driven design for reliability (DfR) process to positively influence the way reliability is implemented.

Key concepts: Reliability engineering, Reliability (semiconductor), Leverage (statistics), New product development, Computer science, Product design, Product (mathematics), Automotive industry

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