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
Your Session has timed out. Please sign back in to continue.


Meng, X. H. , Xie, Y. B. , and Dai, X. D. , 2010, “ Methodology of Designing for Time-Varying Performance of Complex Products,” Chin. J. Mech. Eng., 46(1), pp. 128–133. [CrossRef]
Vichare, N. , Rodgers, P. , Eveloy, V. , and Pecht, M. , 2007, “ Environment and Usage Monitoring of Electronic Products for Health Assessment and Product Design,” Qual. Technol. Quant. Manage., 4(2), pp. 235–250. [CrossRef]
Chen, L. H. , and Ko, W. C. , 2009, “ Fuzzy Linear Programming Models for New Product Design Using QFD With FMEA,” Appl. Math. Model., 33(2), pp. 633–647. [CrossRef]
Ma, H. Z. , Chu, X. N. , Xue, D. Y. , and Chen, D. P. , 2016, “ Identification of To-Be-Improved Components for Redesign of Complex Products and Systems Based on Fuzzy QFD and FMEA,” J. Intell. Manuf., epub.
Stone, R. B. , Tumer, I. Y. , and Wie, M. V. , 2005, “ The Function-Failure Design Method,” ASME J. Mech. Des., 127(3), pp. 397–407. [CrossRef]
Isermann, R. , 2005, “ Model-Based Fault-Detection and Diagnosis-Status and Applications,” Annu. Rev. Control, 29(1), pp. 71–85. [CrossRef]
Gargama, H. , and Chaturvedi, S. K. , 2011, “ Criticality Assessment Models for Failure Mode Effects and Criticality Analysis Using Fuzzy Logic,” IEEE Trans. Reliab., 60(1), pp. 102–110. [CrossRef]
Shin, J. H. , Kiritsis, D. , and Xirouchakis, P. , 2015, “ Design Modification Supporting Method Based on Product Usage Data in Closed-Loop PLM,” Int. J. Comput. Integr. Manuf., 28(6), pp. 551–568. [CrossRef]
Carlson, J. , and Murphy, R. R. , 2003, “ Reliability Analysis of Mobile Robots,” IEEE International Conference on Robotics and Automation (ICRA), Taipei, Taiwan, Sept. 14–19, pp. 274–281.
Searls, D. , Dishongh, T. , and Dujari, P. , 2001, “ A Strategy for Enabling Data Driven Product Decisions Through a Comprehensive Understanding of the Usage Environment,” The Pacific Rim/ASME International Electronic Packaging Technical Conference and Exhibition, Maui, HI, July 8–13, pp. 8–13.
Rybak, J. M. , 2006, “ Remote Condition Monitoring Using Open-System Wireless Technologies,” Sound Vib., 40(2), pp. 16–20.
Malhi, A. , and Gao, R. X. , 2004, “ PCA-Based Feature Selection Scheme for Machine Defect Classification,” IEEE Trans. Instrum. Meas., 53(6), pp. 1517–1525. [CrossRef]
Boschetti, F. , 2005, “ Dimensionality Reduction and Visualization of Geoscientific Images Via Locally Linear Embedding,” Comput. Geosci., 31(6), pp. 689–697. [CrossRef]
Belkin, M. , and Niyogi, P. , 2003, “ Laplacian Eigenmaps for Dimensionality Reduction and Data Representation,” Neural Comput., 15(6), pp. 1373–1396. [CrossRef]
Yang, K. , and Yang, G. , 1997, “ Performance Degradation Analysis Using Principal Component Method,” Annual Reliability and Maintainability Symposium (RAMS), Philadelphia, PA, Jan. 13–16, pp. 136–141.
Wu, J. , Wang, J. , and Liu, L. , 2007, “ Feature Extraction Via KPCA for Classification of Gait Patterns,” Hum. Mov. Sci., 26(3), pp. 393–411. [CrossRef] [PubMed]
Cao, L. J. , Chua, K. S. , Chong, W. K. , Lee, H. P. , and Gu, Q. M. , 2003, “ A Comparison of PCA, KPCA and ICA for Dimensionality Reduction in Support Vector Machine,” Neurocomputing, 55(1–2), pp. 321–336.
Qiu, L. , Yuan, S. , Chang, F. K. , Bao, Q. , and Mei, H. , 2014, “ On-Line Updating Gaussian Mixture Model for Aircraft Wing Spar Damage Evaluation Under Time-Varying Boundary Condition,” Smart Mater. Struct., 23(12), p. 125001. [CrossRef]
Si, X. S. , Wang, W. B. , Chang, H. H. , and Zhou, D. H. , 2011, “ Remaining Useful Life Estimation—A Review on the Statistical Data Driven Approaches,” Eur. J. Oper. Res., 213(1), pp. 1–14. [CrossRef]
Ertunc, H. M. , Loparo, K. A. , and Ocak, H. , 2001, “ Tool Wear Condition Monitoring in Drilling Operations Using Hidden Markov Models (HMMs),” Int. J. Mach. Tools Manuf., 41(9), pp. 1363–1384. [CrossRef]
Huang, R. , Xi, L. , Li, X. , Liu, C. R. , Qiu, H. , and Lee, J. , 2006, “ Residual Life Predictions for Ball Bearings Based on Self-Organizing Map and Back Propagation Neural Network Methods,” Mech. Syst. Signal Process., 21(1), pp. 193–207. [CrossRef]
Pan, Y. , Chen, J. , and Guo, L. , 2009, “ Robust Bearing Performance Degradation Assessment Method Based on Improved Wavelet Packet–Support Vector Data Description,” Mech. Syst. Signal Process., 23(3), pp. 669–681. [CrossRef]
Yu, J. , 2011, “ Bearing Performance Degradation Assessment Using Locality Preserving Projections and Gaussian Mixture Models,” Mech. Syst. Signal Process., 25(7), pp. 2573–2588. [CrossRef]
Dong, Y. , Fang, F. , and Gu, Y. , 2013, “ Dynamic Evaluation of Wind Turbine Health Condition Based on Gaussian Mixture Model and Evidential Reasoning,” J. Renewable Sustainable Energy, 5(3), p. 033117. [CrossRef]
Banfield, J. D. , and Raftery, A. E. , 1989, “ Model-Based Gaussian and Non-Gaussian Clustering,” Biometrics, 49(3), pp. 803–821. [CrossRef]
Zhao, Y. , Hong, H. , Jiang, G. , Chen, W. , and Wang, H. , 2014, “ Conflict Resolution for Product Performance Requirements Based on Propagation Analysis in the Extension Theory,” Adv. Mech. Eng., 6, p. 589345.
Gero, J. S. , and Kannengiesser, U. , 2004, “ The Situated Function–Behaviour–Structure Framework,” Des. Stud., 25(4), pp. 373–391. [CrossRef]
Xie, Y. B. , 2007, “ Some Basic Concepts in Modern Design Theory,” Chin. J. Mech. Eng., 43(11), pp. 7–16. [CrossRef]
Dempster, A. P. , Laird, N. M. , and Rubin, D. B. , 1977, “ Maximum Likelihood From Incomplete Data Via the EM Algorithm,” J. R. Stat. Soc.: Ser. B, 39(1), pp. 1–38.
Goldberger, J. , Gordon, S. , and Greenspan, H. , 2003, “ An Efficient Image Similarity Measure Based on Approximations of KL-Divergence Between Two Gaussian Mixtures,” Ninth IEEE International Conference on Computer Vision (ICCV), Nice, France, Oct. 13–16, pp. 487–493.
Bezdek, J. C. , Ehrlich, R. , and Full, W. , 1984, “ FCM: The Fuzzy C-Means Clustering Algorithm,” Comput. Geosci., 10(2–3), pp. 191–203. [CrossRef]
Tsekouras, G. E. , and Sarimveis, H. , 2004, “ A New Approach for Measuring the Validity of the Fuzzy C-Means Algorithm,” Adv. Eng. Software, 35(8–9), pp. 567–575. [CrossRef]


Grahic Jump Location
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. 2

The procedure for analysis of functional performance degradation

Grahic Jump Location
Fig. 1

An example hierarchical function model

Grahic Jump Location
Fig. 5

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

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. 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

Grahic Jump Location
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




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In