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research-article

An Integrated Approach for Design Improvement Based on Analysis of Time-Dependent Product Usage Data

[+] Author and Article Information
Hongzhan Ma

School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China, 200240
mahongzhan@sjtu.edu.cn

Xuening Chu

School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China, 200240
xnchu@sjtu.edu.cn

Guolin Lyu

Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Alberta, Canada
guolin.lyu@ucalgary.ca

Deyi Xue

Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Alberta, Canada
dxue@ucalgary.ca

1Corresponding author.

ASME doi:10.1115/1.4037246 History: Received November 03, 2016; Revised April 25, 2017

Abstract

With the recent advances in information gathering techniques, product usage data, including time-dependent product performance feature data and field data (i.e., working conditions), can be continuously collected during the product usage stage. Product reliability can be improved by incorporating product usage data for making design decisions. Since influences of product usage data on design quality are seldom studied in the past, a new decision-making approach is introduced in this research to improve quality of design based on analysis of product usage data. In this approach, a hierarchical product function model is built first to describe the relationships among functions and multiple performance features for each function. Second, the time-dependent data of performance features for each function are explored to assess function health degradation using the Gaussian mixed model, and functions with rapid and severe degradation are identified. Third, the abnormal field data that cause the severe function degradation are found by clustering of field data. Finally, a redesign necessity index (RNI) is defined for each design parameter related to severely degraded functions based on the relationships between design parameters and abnormal field data. An associate relationship matrix is constructed to calculate the RNI of each design parameter for identifying the to-be-modified design parameters. The effectiveness of this new approach is demonstrated through a case study for the redesign of a large tonnage crawler crane.

Copyright (c) 2017 by ASME
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