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Research Papers

Fatigue Design Load Identification Using Engineering Data Analytics

[+] Author and Article Information
Ha-Rok Bae

Assistant Professor
Mem. ASME
Department of Mechanical
and Materials Engineering,
Wright State University,
Dayton, OH 45435

Hiroaki Ando

Engineering Specialist
Advanced VPD, PD&GT,
Caterpillar, Inc.,
1901 S. First Street,
Champaign, IL 61874

Sangjeong Nam

Staff Analyst
Information Analytics, PD&GT,
Caterpillar, Inc.,
1901 S. First Street,
Champaign, IL 61874

Sangkyum Kim

Engineering Team Lead
Information Analytics, PD&GT,
Caterpillar, Inc.,
1901 S. First Street,
Champaign, IL 61874

Christopher Ha

Manager
Information Analytics, PD&GT,
Caterpillar, Inc.,
1901 S. First Street,
Champaign, IL 61874

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 12, 2013; final manuscript received June 3, 2014; published online November 14, 2014. Assoc. Editor: Xiaoping Du.

J. Mech. Des 137(1), 011001 (Jan 01, 2015) (12 pages) Paper No: MD-13-1405; doi: 10.1115/1.4027849 History: Received September 12, 2013; Revised June 03, 2014; Online November 14, 2014

Selecting an appropriate concept design in the early stage of the product development process is crucial for successful machine development. As one of the important design requirements, a structural design needs to validate the structural fatigue life against physical tests of actual field operations. Traditionally, the fatigue design loads for the concept design evaluations are generated by hand calculations based on past experience to capture envelopes of expected system responses. But such an approach often does not capture actual loading behaviors in the field. An alternative approach of leveraging observed data from physical tests is highly desired, and a new method of this approach is introduced in this study. Our goal is to develop a new methodology of identifying fundamental fatigue load (FFL) contents from observed data by using engineering data analytics (EDA) techniques. The proposed methodology is applied to determine a new sensor layout which enables us to capture fundamental structural damage load patterns. Numerical demonstrations and case studies of the proposed method are presented with a common structural component, an I-section cantilever beam, and an industrial large-scale structure, a front linkage of a hydraulic excavator.

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References

Dieter, G. E., and Schmidt, L. C., 2013, Engineering Design, 5th ed., McGraw-Hill, New York.
Wang, L., Shen, W., Xie, H., Neelamkavil, J., and Pardasani, A., 2002, “Collaborative Conceptual Design—State of the Art and Future Trends,” Comput. -Aided Des., 34, pp. 981–996. [CrossRef]
Worden, K., and Burrows, A., 2001, “Optimal Sensor Placement for Fault Detection,” Eng. Struct., 23(8), pp. 885–901. [CrossRef]
Padula, S. L., Palumbo, D. L., Kincaid, R. K., Asme, T. A., and Ahs, A., 1998 “Optimal Sensor/Actuator Locations for Active Structural Acoustic Control,” Proceedings of the 39th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, AIAA-98-1865, Long Beach, CA. [CrossRef]
Sohn, H., Farrar, C. R., Hemez, F. M., Czarnecki, J. J., Shunk, D. D., Stinemates, D. W., and Nadler, B. R., 2003, “A Review of Structural Health Monitoring Literature: 1996–2001,” Los Alamos National Laboratory Report, Report No. LA-13976-MS.
Meo, M., and Zumpano, G., 2005, “On the Optimal Sensor Placement Techniques for a Bridge Structure,” Eng. Struct., 27(10), pp. 1488–1497. [CrossRef]
Kirkegaard, P. H., and Brincker, R., 1994, “On the Optimal Location of Sensors for Parametric Identification of Linear Structural Systems,” Mech. Syst. Signal Process., 8, pp. 639–647. [CrossRef]
Breitfeld, T. A., 1995, “Method for Identification of a Set of Optimal Points for Experimental Modal Analysis,” J. Anal. Exp. Modal Anal., 11(1), pp. 1–9.
Kammer, D. C., and Brillhart, R. D., 1996, “Optimal Sensor Placement for Modal Identification Using System-Realization Methods,” J. Guid., Control Dyn., 19, pp. 729–31. [CrossRef]
Heo, G., Wang, M. L., and Satpathi, D., 1997, “Optimal Transducer Placement for Health Monitoring of Long Span Bridge,” Soil Dyn. Earthquake Eng., 16, pp. 495–502. [CrossRef]
Lam, H.-F., and Yin, T., 2011, “Dynamic Reduction-Based Structural Damage Detection of Transmission Towers: Practical Issues and Experimental Verification,” Eng. Struct., 33(5), pp. 1459–1478. [CrossRef]
Rezaei, D., and Taheri, F., 2010, “Damage Identification in Beams Using Empirical Mode Decomposition,” Struct. Health Monit., 10(3), pp. 261–274. [CrossRef]
Cheraghi, N., and Taheri, F., 2007, “A Damage Index for Structural Health Monitoring Based on the Empirical Mode Decomposition,” J. Mech. Mater. Struct., 2(1), pp. 43–62. [CrossRef]
Wentzel, H., 2013, “Fatigue Test Load Identification Using Weighted Model Filtering Based on Stress,” Mech. Syst. Signal Process., 40, pp. 618–627. [CrossRef]
Kang, B. S., Park, G. J., and Arora, J. S., 2005, “Optimization of Flexible Multibody Dynamic Systems Using Equivalent Static Load Method,” AIAA J., 43(4), pp. 846–852. [CrossRef]
Park, K. J., Lee, J. N., and Park, G. J., 2005, “Structural Shape Optimization Using Equivalent Static Loads Transformed From Dynamic Loads,” Int. J. Numer. Methods Eng., 63, pp. 589–602. [CrossRef]
Wickham, M. J., Riley, D. R., and Nachtsheim, C. J., 1995, “Integrating Optimal Experimental Design Into the Design of a Multi-Axis Load Transducer,” J. Eng. Ind., 117, pp. 400–405. [CrossRef]
Oh, C. S., 2001, “Application of Wavelet Transform in Fatigue History Editing,” Int. J. Fatigue, 23, pp. 241–250. [CrossRef]
Abdullah, S., Choi, J. C., Giacomin, J. A., and Yates, J. R., 2006, “Bump Extraction Algorithm for Variable Amplitude Fatigue Loading,” Int. J. Fatigue, 28, pp. 675–691. [CrossRef]
Miner, M. A., 1945, “Cumulative Damage in Fatigue,” ASME J. Appl. Mech, 12(3), pp. A159–A164.
Downing, S. D., and Socie, D. F., 1982, “Simple Rainflow Counting Algorithms,” Int. J. Fatigue, 4(1), pp. 31–40. [CrossRef]
Haiba, M., Barton, D. C., Brooks, P. C., and Levesley, M. C., 2002, “Review of Life Assessment Techniques Applied to Dynamically Loaded Automotive Components,” Comput. Struct., 80, pp. 481–494. [CrossRef]
Huang, L., Agrawal, H., and Kurudiyara, P., 1998, “Dynamic Durability Analysis of Automotive Structures,” Society of Automotive Engineering, SAE Paper No. 980695. [CrossRef]
Conle, F. A., and Mousseau, C. W., 1991, “Using Vehicle Dynamics Simulations and Finite-Element Results to Generate Fatigue Life Contours for Chassis Components,” Int. J. Fatigue, 13(3), pp. 195–205. [CrossRef]
Socie, D. F., and Marquis, G. B., “Multiaxial Fatigue,” SAE International, ed, Warrendale, PA.
Fayyad, U., Piatetsky-shapiro, G., and Smyth, P., 1996, “From Data Mining to Knowledge Discovery in Databases,” Artif. Intell. (AI) Mag., 17(3), pp. 37–54. [CrossRef]
Lan, H., 2011, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kaufman, Boston.
Allemang, R. J., 2002, “The Modal Assurance Criterion (MAC): Twenty Years of Use and Abuse,” Proceedings of the International Modal Analysis Conference IMAC 20, Los Angeles, CA, pp. 397–405.
Gen, M., and Cheng, R., 2000, Genetic Algorithms and Engineering Optimization, Wiley, New York. [CrossRef]
“Fe-Safe User's Manual,” Safe Technology Limited, Sheffield, UK, 2011.

Figures

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Fig. 1

Stress amplitude and life curve

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Fig. 2

Overall process of the proposed sensor layout method

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Fig. 3

Worst cycle identified with time points, wc1k and wc2k, via Rainflow cycle counting

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Fig. 4

I-section cantilever beam

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Fig. 5

Stress plot with bending unit load

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Fig. 6

Axial and bending load history data

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Fig. 7

SAE-1020 Steel SN plot

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Fig. 8

Cantilever beam fatigue analysis result

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Fig. 9

Five FFLs and their NVC values

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Fig. 10

Pattern-amplified FFLs and their NVC values

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Fig. 11

Candidate sensor locations (marked with black squares)

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Fig. 12

Sensor placement result w/fatigue life coverage 98%

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Fig. 13

Case study 1—sensor placement result w/fatigue life coverage 90%

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Fig. 14

Case study 2—sensor placement result w/fatigue life coverage 98%

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Fig. 15

General hydraulic excavator and its front linkage boom structure

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Fig. 16

Load history for truck loading application

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Fig. 17

Fatigue life contour with the given load history

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Fig. 18

Initial candidate sensor locations (marked with black squares)

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Fig. 19

Optimum sensor layout for capturing all 16 FFLs

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Fig. 20

Stress contours and deformed shapes with FFL01, FFL04, and FFL08

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Fig. 21

High correlations among the FFL groups

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Fig. 22

Normalized forces and moments of 16 FFLs

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