Award Abstract #0540132
DDDAS - SMRP: A Framework For the Dynamic Data-Driven Fault Diagnosis of Wind Turbine Systems

NSF Org:	CMMI Division of Civil, Mechanical, and Manufacturing Innovation
Initial Amendment Date:	September 20, 2005
Latest Amendment Date:	September 20, 2005
Award Number:	0540132
Award Instrument:	Standard Grant
Program Manager:	Shih-Chi Liu CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering
Start Date:	June 1, 2006
Expires:	May 31, 2010 (Estimated)
Awarded Amount to Date:	$180001
Investigator(s):	Yu Ding yuding@iemail.tamu.edu (Principal Investigator)
Sponsor:	Texas Engineering Experiment Station TEES State Headquarters Bldg. College Station, TX 77843 979/862-1696
NSF Program(s):	ITR-DYNAMIC DATA DRIV APP SYS, DYNAMIC DATA DRIVEN APPL SYSTS
Field Application(s):
Program Reference Code(s):	CVIS, 1057
Program Element Code(s):	7581, 7481

ABSTRACT

CMS-0540132, PI: Yu Ding, Texas A&M University

CMS-0540278, PI: Jiong Tang, University of Connecticut

Abstract

This collaborative research (0540132, PI: Yu Ding, Texas A&M University; and 0540278, PI: Jiong Tang, University of Connecticut) will provide a dynamic data-driven framework for wind turbine diagnosis. This new methodology is fundamentally different from the current practice whose performance is limited due to the non-dynamic and non-robust nature in the modeling approaches and in the data collection and processing strategies. This framework consists of two robust data pre-processing modules for highlighting fault features and removing sensor anomaly, three interrelated, multi-level models that describe different details of the system behaviors, and one dynamic strategy for the robust local interrogation that allows for measurements to be adaptively taken according to specific physical conditions and the associated risk level. It incorporates both historical data and on-line signals into the system modeling, and enables the ability to adaptively alter data collection procedures to best capture the critical system features. Collectively, these components lead to a robust and sensitive diagnosis system for wind turbines. This research is strengthened by a close collaboration with industry that will provide abundant historical sensor data and detailed system characterization, and also offer in-field implementation opportunities.

The proposed research will have strategic importance on the utilization of wind energy that is currently the most viable clean energy alternative. Today, in the vast areas that have low wind speed, wind energy cannot compete with traditional energy sources as it has a higher cost, mainly owing to its high maintenance costs and low confidence in the diagnosis technology. This dynamic and data-driven fault diagnosis will play a key role in enabling a cost-effective generation of wind electricity. Progress in the fault diagnosis of blades and gearboxes will also benefit the power generation, automobile, aerospace, and engine industries. Meanwhile, the collaborative nature of this research will provide students with a multidisciplinary training and will bring industrial perspective to the universities. This project will have a long-term impact on education through the curriculum development and will promote the public awareness of clean energy concept through outreaches to high schools.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Chen, Y., Ding, Y., Jin, J., and Ceglarek, D.. "Integration of Tolerance and Maintenance Design for Multi-station Manufacturing Processes," IEEE Transactions on Automation Science and Engineering, v.3, 2006, p. 440.

Ding, Y., Byon, E., Park, C., Tang, J., Lu, Y. and Wang, X.. "Dynamic Data-Driven Fault Diagnosis of Wind Turbine Systems," Lecture Note in Computer Science, v.4487, 2007, p. 1197.

Ding, Y., Elsayed, E.A., Kumara, S., Lu, J-C., Feng, N., and Shi, J.. "Distributed Sensing for Quality and Productivity Improvements," IEEE Transactions on Automation Science and Engineering, v.3, 2006, p. 344.

Ding, Y., Zeng, L., and Zhou, S.. "Phase-I Analysis for Monitoring Nonlinear Profile Signals in Manufacturing Processes," Journal of Quality Technology, v.38, 2006, p. 199.

Lu, Y., Wang, X., Tang, J., and Ding, Y.. "Damage detection using piezoelectric transducers and Lamb wave approach: II robust and quantitative decision making," Smart Materials and Structures, v.17, 2008, p. 025034.

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