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Technical Brief

Stability Monitoring of Rotorcraft Systems: A Dynamic Data-Driven Approach

[+] Author and Article Information
Siddharth Sonti

Mechanical Engineering Department,
Pennsylvania State University,
University Park, PA 16802
e-mail: sus39@psu.edu

Eric Keller

Mechanical Engineering Department,
Pennsylvania State University,
University Park, PA 16802
e-mail: keller.ee@gmail.com

Joseph Horn

Aerospace Engineering Department,
Pennsylvania State University,
University Park, PA 16802
e-mail: joehorn@psu.edu

Asok Ray

Fellow ASME
Mechanical Engineering Department,
Pennsylvania State University,
University Park, PA 16802
e-mail: axr2@psu.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 6, 2013; final manuscript received November 5, 2013; published online January 8, 2014. Assoc. Editor: Jiong Tang.

J. Dyn. Sys., Meas., Control 136(2), 024505 (Jan 08, 2014) (6 pages) Paper No: DS-13-1103; doi: 10.1115/1.4025988 History: Received March 06, 2013; Revised November 05, 2013

This brief paper proposes a dynamic data-driven method for stability monitoring of rotorcraft systems, where the underlying concept is built upon the principles of symbolic dynamics. The stability monitoring algorithm involves wavelet-packet-based preprocessing to remove spurious disturbances and to improve the signal-to-noise ratio (SNR) of the sensor time series. A quantified measure, called Instability Measure, is constructed from the processed time series data to obtain an estimate of the relative instability of the dynamic modes of interest on the rotorcraft system. The efficacy of the proposed method has been established with numerical simulations where correlations between the instability measure and the damping parameter(s) of selected dynamic mode(s) of the rotor blade are established.

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References

Figures

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Fig. 1

Typical wavelet and scaling functions of Meyer basis

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Fig. 2

Effects of wavelet packet preprocessing

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Fig. 4

Profile of Instability Measure for the T1 mode at 15 Hz (a) ensemble of plots with ζ in. % and (b) confidence interval with ζ in. %

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Fig. 5

Profile of Instability Measure for the T1 mode at 17 Hz (a) ensemble of plots with ζ in. % and (b) confidence interval with ζ in. %

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Fig. 6

Profile of Instability Measure with the exponent ε=0.5 (see Eq. (3)) (a) ensemble of plots with D in. % and (b) confidence interval with D in. %

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