Knowledge System Based Design-for-Reliability for Developing Connected Intelligent Products



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Authors Abstract
Connectivity and artificial intelligence will be major features of many upcoming products. The need for accurate estimation of the state of these products and their operational environments, and, the intricacy of their decision, planning, and control algorithms, will cause unprecedented growth in their design complexity as well as their software content. The failures of complex software-intensive electronically controlled products of today can often be traced to the interfaces between different subsystems and to the intersection between different engineering disciplines, i.e., mechanical, electrical, and software. Experts who possess intuition regarding the failure modes and robust design of complex electronically controlled products are few and consequently, information management solutions that can help capture and reuse the product failure modes are crucial for delivering dependable, software-intensive products. Today, enterprise level reliability engineering tools and systems engineering tools are disconnected, creating many possibilities for erroneous information transfer and loss of crucial understanding. This problem is further accentuated when we consider the silos of expertise in mechanical, electrical, and software disciplines. Additionally, failure knowledge capture and reuse tools are neither commercially offered nor frequently deployed in the context of systems engineering. This paper proposes a map of the connection between commonly used tools in reliability engineering and the technical processes of systems engineering. The paper also presents a brief overview of the ontology based product failure knowledge capture and reuse systems to assess the potential for realizing a Knowledge System Based Design-for-Reliability capability in the industry.
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Agaram, V., "Knowledge System Based Design-for-Reliability for Developing Connected Intelligent Products," SAE Technical Paper 2017-01-0196, 2017,
Additional Details
Mar 28, 2017
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Technical Paper