A Hybrid Neural Network Approach for the Development of Friction Component Dynamic Model

M. Cao; K. W. Wang; Y. Fujii; W. E. Tobler

doi:10.1115/1.1649980

Journal of Dynamic Systems, Measurement, and Control | Volume 126 | Issue 1 | TECHNICAL PAPERS

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TECHNICAL PAPERS

A Hybrid Neural Network Approach for the Development of Friction Component Dynamic Model

M. Cao, K. W. Wang, Y. Fujii and W. E. Tobler

[+] Author and Article Information

M. Cao, K. W. Wang

Department of Mechanical and Nuclear Engineering,The Pennsylvania State University, University Park, PA 16802

Y. Fujii, W. E. Tobler

Research and Advanced Engineering, Ford Motor Company, Dearborn, MI 48121

J. Dyn. Sys., Meas., Control 126(1), 144-153 (Apr 12, 2004) (10 pages) doi:10.1115/1.1649980 History: Received May 14, 2002; Revised August 25, 2003; Online April 12, 2004

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References

Figures

Baseline Network Architecture z⁻¹=One step time delay

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Measured Band Brake Torque with Different Oil Temperature, Apply Pressure and Slip Profiles

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Hybrid Network Architecture

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Friction Component Engagement Test Stand

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Training Results of Baseline Neural Network Model Initial Slip: 157.1 rad/sec (1500 rpm), Final Apply Pressure: 6.206e+4 N/m² (9 psi), Initial Oil Temperature: 394.1 K (250°F), Oil Flow Rate: 2.273e−5 m³/sec (0.3 gallon/min)

View Large | Save Figure | Download Slide (.ppt)

Training Results of Baseline Neural Network Model Initial Slip: 157.1 rad/s (1500 rpm), Final Apply Pressure: 6.206e+4 N/m² (9 psi), Initial Oil Temperature: 271.9 K (30°F), Oil Flow Rate: 2.273e−5 m³/s (0.3 gallon/min)

View Large | Save Figure | Download Slide (.ppt)

Training Results of Baseline Neural Network Model Initial Slip: 314.2 rad/s (3000 rpm), Final Apply Pressure: 1.034e+5 N/m² (15 psi), Initial Oil Temperature: 271.9 K (30°F), Oil Flow Rate: 2.273e−5 m³/s (0.3 gallon/min)

View Large | Save Figure | Download Slide (.ppt)

Testing Results of Baseline Neural Network Model –Measured Torque (N.m) [[dashed_line]]Network Estimated Torque (N.m) Initial Slip: 314.2 rad/s (3000 rpm), Final Apply Pressure: 1.034e+5 N/m² (15 psi), Initial Oil Temperature: 310.8 K (100°F), Oil Flow Rate: 4.546e−5 m³/s (0.6 gallon/min)

View Large | Save Figure | Download Slide (.ppt)

Training Results of Hybrid Neural Network Model: –Measured Torque (N.m) [[dashed_line]]Network Estimated (N.m)

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Training Results of Hybrid Neural Network Model: –Measured Torque (N.m) [[dashed_line]]Network Estimated Torque (N.m)

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Training Results of Hybrid Neural Network Model: –Measured Torque (N.m) [[dashed_line]]Network Estimated Torque (N.m)

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Testing Results of Hybrid Neural Network Model: –Measured Torque [[dashed_line]]Network Estimated Torque [[dotted_line]] Torque Estimated by the First Principle Model

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Cao MM, Wang KW, Fujii YY, Tobler WE. A Hybrid Neural Network Approach for the Development of Friction Component Dynamic Model. ASME. J. Dyn. Sys., Meas., Control. 2004;126(1):144-153. doi:10.1115/1.1649980.

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A Hybrid Neural Network Approach for the Development of Friction Component Dynamic Model

References

Figures

Baseline Network Architecture z⁻¹=One step time delay

Measured Band Brake Torque with Different Oil Temperature, Apply Pressure and Slip Profiles

Hybrid Network Architecture

Friction Component Engagement Test Stand

Training Results of Baseline Neural Network Model Initial Slip: 157.1 rad/sec (1500 rpm), Final Apply Pressure: 6.206e+4 N/m² (9 psi), Initial Oil Temperature: 394.1 K (250°F), Oil Flow Rate: 2.273e−5 m³/sec (0.3 gallon/min)

Training Results of Baseline Neural Network Model Initial Slip: 157.1 rad/s (1500 rpm), Final Apply Pressure: 6.206e+4 N/m² (9 psi), Initial Oil Temperature: 271.9 K (30°F), Oil Flow Rate: 2.273e−5 m³/s (0.3 gallon/min)

Training Results of Baseline Neural Network Model Initial Slip: 314.2 rad/s (3000 rpm), Final Apply Pressure: 1.034e+5 N/m² (15 psi), Initial Oil Temperature: 271.9 K (30°F), Oil Flow Rate: 2.273e−5 m³/s (0.3 gallon/min)

Testing Results of Baseline Neural Network Model –Measured Torque (N.m) [[dashed_line]]Network Estimated Torque (N.m) Initial Slip: 314.2 rad/s (3000 rpm), Final Apply Pressure: 1.034e+5 N/m² (15 psi), Initial Oil Temperature: 310.8 K (100°F), Oil Flow Rate: 4.546e−5 m³/s (0.6 gallon/min)

Training Results of Hybrid Neural Network Model: –Measured Torque (N.m) [[dashed_line]]Network Estimated (N.m)

Training Results of Hybrid Neural Network Model: –Measured Torque (N.m) [[dashed_line]]Network Estimated Torque (N.m)

Training Results of Hybrid Neural Network Model: –Measured Torque (N.m) [[dashed_line]]Network Estimated Torque (N.m)

Testing Results of Hybrid Neural Network Model: –Measured Torque [[dashed_line]]Network Estimated Torque [[dotted_line]] Torque Estimated by the First Principle Model

CPU Time Comparison: Hybrid ANN versus First Principle Model

Cylindrical Co-ordinates for Reynolds Equation: Plate-type Friction Component

X-Y-Z Co-ordinates for Reynolds Equation: Band-type Friction Component

Tables

Errata

Discussions

Related Content

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Conference Proceedings

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A Hybrid Neural Network Approach for the Development of Friction Component Dynamic Model

References

Figures

Baseline Network Architecture z−1=One step time delay

Measured Band Brake Torque with Different Oil Temperature, Apply Pressure and Slip Profiles

Hybrid Network Architecture

Friction Component Engagement Test Stand

Training Results of Baseline Neural Network Model Initial Slip: 157.1 rad/sec (1500 rpm), Final Apply Pressure: 6.206e+4 N/m2 (9 psi), Initial Oil Temperature: 394.1 K (250°F), Oil Flow Rate: 2.273e−5 m3/sec (0.3 gallon/min)

Training Results of Baseline Neural Network Model Initial Slip: 157.1 rad/s (1500 rpm), Final Apply Pressure: 6.206e+4 N/m2 (9 psi), Initial Oil Temperature: 271.9 K (30°F), Oil Flow Rate: 2.273e−5 m3/s (0.3 gallon/min)

Training Results of Baseline Neural Network Model Initial Slip: 314.2 rad/s (3000 rpm), Final Apply Pressure: 1.034e+5 N/m2 (15 psi), Initial Oil Temperature: 271.9 K (30°F), Oil Flow Rate: 2.273e−5 m3/s (0.3 gallon/min)

Testing Results of Baseline Neural Network Model –Measured Torque (N.m) [[dashed_line]]Network Estimated Torque (N.m) Initial Slip: 314.2 rad/s (3000 rpm), Final Apply Pressure: 1.034e+5 N/m2 (15 psi), Initial Oil Temperature: 310.8 K (100°F), Oil Flow Rate: 4.546e−5 m3/s (0.6 gallon/min)

Training Results of Hybrid Neural Network Model: –Measured Torque (N.m) [[dashed_line]]Network Estimated (N.m)

Training Results of Hybrid Neural Network Model: –Measured Torque (N.m) [[dashed_line]]Network Estimated Torque (N.m)

Training Results of Hybrid Neural Network Model: –Measured Torque (N.m) [[dashed_line]]Network Estimated Torque (N.m)

Testing Results of Hybrid Neural Network Model: –Measured Torque [[dashed_line]]Network Estimated Torque [[dotted_line]] Torque Estimated by the First Principle Model

CPU Time Comparison: Hybrid ANN versus First Principle Model

Cylindrical Co-ordinates for Reynolds Equation: Plate-type Friction Component

X-Y-Z Co-ordinates for Reynolds Equation: Band-type Friction Component

Tables

Errata

Discussions

Related Content

Journals

Conference Proceedings

eBooks

Baseline Network Architecture z⁻¹=One step time delay

Training Results of Baseline Neural Network Model Initial Slip: 157.1 rad/sec (1500 rpm), Final Apply Pressure: 6.206e+4 N/m² (9 psi), Initial Oil Temperature: 394.1 K (250°F), Oil Flow Rate: 2.273e−5 m³/sec (0.3 gallon/min)

Training Results of Baseline Neural Network Model Initial Slip: 157.1 rad/s (1500 rpm), Final Apply Pressure: 6.206e+4 N/m² (9 psi), Initial Oil Temperature: 271.9 K (30°F), Oil Flow Rate: 2.273e−5 m³/s (0.3 gallon/min)

Training Results of Baseline Neural Network Model Initial Slip: 314.2 rad/s (3000 rpm), Final Apply Pressure: 1.034e+5 N/m² (15 psi), Initial Oil Temperature: 271.9 K (30°F), Oil Flow Rate: 2.273e−5 m³/s (0.3 gallon/min)

Testing Results of Baseline Neural Network Model –Measured Torque (N.m) [[dashed_line]]Network Estimated Torque (N.m) Initial Slip: 314.2 rad/s (3000 rpm), Final Apply Pressure: 1.034e+5 N/m² (15 psi), Initial Oil Temperature: 310.8 K (100°F), Oil Flow Rate: 4.546e−5 m³/s (0.6 gallon/min)