Significance Statement
Quality improvement is a routine mission of the product engineering, and the optimization of product infant failure rate is usually the most important and hard work for quality engineers.How to identify and confirm the mechanism of product infant failure from the lifecycle quality and reliability data is a prerequisite for continuous reliability improvement. Traditional method is confined to reliability data and only depends on the Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) tools to flow down product infant failure roughly. The paper puts forward a novel technical approach for mechanism analysis of product infant failure based on the quality and reliability data from product lifecycle, which could intelligently decompose fault symptoms into critical design and production parameters based on the relational tree, specifically, this approach emphasizes application of the artificial intelligence techniques. In order to specify the object of product infant failure analysis, the connotation of product infant failure based on the product reliability evolution model in the life cycle and analysis framework of inherent reliability in production are presented firstly. Then, along the product design and production processes a decomposition method for relational tree of product infant failure is studied based on the functional, physical and process domain in Axiomatic Design (AD). And the failure relation weight computation of its nodes by means of Rough Set and fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is expounded. Finally, the validity of the proposed method is verified by a case study of analyzing a car infant failure about body noise vibration harshness (NVH) complaint, and the result shows that the proposed approach is conducive to develop intelligent mechanism analysis of complex product infant failure.
Journal Reference
Engineering Applications of Artificial Intelligence,Volume 47, January 2016, Pages 25-37.
Yi-Hai He1,2 , Lin-Bo Wang1,, Zhen-Zhen He1,, Min Xie2
[expand title=”Show Affiliations”]- School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
- Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong Tat Chee Avenue, China
Abstract
Root causes identification of product infant failure is nowadays one of the critical topics in product quality improvements. This paper puts forward a novel technical approach for mechanism analysis of product infant failure based on domain mapping in Axiomatic Design and the quality and reliability data from product lifecycle in the form of relational tree. The proposed method could intelligently decompose the early fault symptoms into root causes of critical functional parameters in function domain, design parameters in physical domain and process parameters in process domain successively. More specifically, both qualitative and quantitative attributes of quality and reliability types are considered for solving the root causes weight computation problem of product infant failure, this approach emphasizes the integrated application of artificial intelligence techniques of Rough Set and fuzzy TOPSIS to compute the weight of root causes. In order to enumerate the latent root causes of product infant failure, connotation of product infant failure based on the product reliability evolution model in the life cycle and data integration model of quality and reliability in production based on the extended QR chain are presented firstly. Then, a decomposition method for relational tree of product infant failure is studied based on domains of functional, physical and process in Axiomatic Design. The failure relation weight computation of root causes (nodes of relational tree) is considered as multi-criteria decision making problem (MCDM) by integrated application of Rough Set and fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), which the Rough Set is used to mining the quality data and fuzzy TOPSIS is adopted to model the computation process of failure relation weight. Finally, the validity of the proposed method is verified by a case study of analyzing a car infant failure about body noise vibration harshness complaint, and the result proves that the proposed approach is conducive to improve the intelligent level of root causes identification for complex product infant failure.
Go To Engineering Applications of Artificial Intelligence