Reliability design and analysis method under digital development environment

Abstract

Most modern products need be designed to operate without failure for years, decades, or longer. The traditional approach to reliability design and analysis is based on commercial reliability models or the knowledge from the company's designer. Because the traditional reliability design and analysis method is not relevant to product design parameters, it has inherent limitations. So it does not solve the problem about higher reliable product. In order to achieve higher reliable product, a new reliability design and analysis method need be investigated during the product development lifecycle. At the same time, in today's context of global competition, manufacturers are facing greater challenges than ever before. Customers demand more complex and reliable products to be developed with shorter lead times and more cost effectiveness; Environmental and regulatory constraints as well as market expectations demand more efficient product behavior. In order to provide the customer with equipment that works when needed and continues operating for a defined period of time, manufacturers need to better understand materials and process conditions, and their effects on product reliability. Finding new reliability design and analysis method to address the challenges that we face during the product development lifecycle is needed. In this paper, first, using the geometric data, material data and structure data provided by the geometric digital prototype of the product, we analyses the component reliability of the product by structure simulation based on physics of failure theory. Then taking the results of structure simulation as inputs to the functional digital prototype and digital performance prototype, we analyze the product reliability by functional simulation. It is shown that this new reliability design and analysis method can enhance the product reliability by addressing the root cause mechanisms and driving forces responsible for product failures.

Prognostics Health Management: Perspectives in engineering systems reliability prognostics

“…The model-based advantages are: damages estimation that may occur during all stages of storage and transport, taking into account the degradation caused by environmental conditions, such as thermal loads, humidity, vibration and impact. The knowledge of the failure mechanisms coupled with the monitoring system loads and parametric data, allow the identification of the nature and extent of the fault (Kulkarni et al 2013, Zhanyong & Xuegang 2014. The model-based approach also has a number of constraints: the specific and detailed knowledge of the system, geometry and material composition, and the physical processes that lead to failure are needed, which are not always available.…”

“…The alarms can be armed to warn the operating system failure eminence, based on the value of the RUL reported. It can be timely provided the system repair or replacement, depending on the critical application index (Shao et al 2014, Zhanyong & Xuegang 2014. The authors Lui et al (2011) propose a recognition condition of complex systems based on Multi-fractal Analysis and Demp-ster-Shafer evidence theory to extract and utilize the multi-source fusion information implied in monitoring highly non-linear data properly the industrial safety systems, or equipment with highly non-linear load conditions.…”