Research Papers: D3 Methods

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 200240, China
e-mail: mahongzhan@sjtu.edu.cn

Xuening Chu

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

Guolin Lyu

Department of Mechanical and
Manufacturing Engineering,
University of Calgary,
Calgary, AB T2N 1N4, Canada
e-mail: guolin.lyu@ucalgary.ca

Deyi Xue

Department of Mechanical and
Manufacturing Engineering,
University of Calgary,
Calgary, AB T2N 1N4, Canada
e-mail: dxue@ucalgary.ca

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received November 3, 2016; final manuscript received April 25, 2017; published online October 2, 2017. Assoc. Editor: Yan Wang.

J. Mech. Des 139(11), 111401 (Oct 02, 2017) (13 pages) Paper No: MD-16-1735; doi: 10.1115/1.4037246 History: Received November 03, 2016; Revised April 25, 2017

With the recent advances in information gathering techniques, product performances and environment/operation conditions can be monitored, and product usage data, including time-dependent product performance feature data and field data (i.e., environmental/operational data), can be continuously collected during the product usage stage. These technologies provide opportunities to improve product design considering product functional performance degradation. The challenge lies in how to assess data of product functional performance degradation for identifying relevant field factors and changing design parameters. An integrated approach for design improvement is developed in this research to transform time-dependent usage data to design information. Many data modeling and analysis techniques such as hierarchal function model, performance feature dimension reduction method, Gaussian mixed model (GMM), and data clustering method are employed in this approach. These methods are used to extract principal features from collected performance features, assess product functional performance degradation, and group field data into meaningful data clusters. The abnormal field data causing severe and rapid product function degradation are obtained based on the field data clusters. A redesign necessity index (RNI) is defined for each design parameter related to severely degraded functions based on the relationships between this design parameter and abnormal field data. An associate relationship matrix (ARM) is constructed to calculate the RNI of each design parameter for identifying the to-be-modified design parameters with high priorities for product improvement. The effectiveness of this new approach is demonstrated through a case study for the redesign of a large tonnage crawler crane.

Copyright © 2017 by ASME
Topics: Design , Dimensions
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Grahic Jump Location
Fig. 1

An example hierarchical function model

Grahic Jump Location
Fig. 2

The procedure for analysis of functional performance degradation

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Fig. 3

A GMM model for multiple function performance features with multimodal distribution: (a) 3D view and (b) 2D contour map

Grahic Jump Location
Fig. 4

An example of the ARM for the calculation of RNI: (a) relation matrix of functions and (b) ARM between design parameter and abnormal field data

Grahic Jump Location
Fig. 5

(a) Components in the operation device and (b) its hierarchical function model

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Fig. 6

The changes of GMMs with performance degradation of F13: (a) the baseline GMM Ω(0), (b) the updated GMM Ω(1), (c) the GMM Ω(6000), and (d) the GMM Ω(8000)

Grahic Jump Location
Fig. 7

The performance degradation tendencies of functions: (a) HDI of F13 and (b) HDIs of all functions

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Fig. 8

The ARM for identification of to-be-modified design parameters: (a) relation matrix of functions and (b) ARM between design parameter and abnormal field data




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