Cross-bispectral analysis for detection and diagnosis of helicopter trail-rotor drive-shaft problems

Based on cross-bispectrum, Cross-Quadratic-Coupling spectrum, CQC(f), is developed and used to assess different health conditions of an AH-64D (Apache) helicopter tail-rotor drive-shafts. CQC(f) is derived as a projection of the three-dimensional cross-bispectrum into two-dimensional spectrum that quantitatively describes quadratic coupling power at certain frequency in single-frequency space. Hence, the proposed CQC(f) simplifies the study of cross-bispectrum and inherits its useful characteristics such as high immunity to additive Gaussian noise and high capability of nonlinear-systems identification. Real-world vibration data are collected from a dedicated research test bed emulating full-scale AH-64D helicopter drive-train. Using the proposed index, different quadratic-nonlinear harmonic frequency patterns are detected and used to uniquely identify different cases of drive-shafts mechanical faults, such as angular misalignment and imbalance, compared to baseline case, which helps in gaining more diagnostic/prognostic capabilities. Magnitude response of the proposed CQC(f) is compared to the magnitude response of the conventional cross-power spectrum in terms of immunity to white Gaussian noise, and both analytical and experimental results shows superior performance of the proposed coupling spectrum.

[1]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[2]  Yahya I. Sharaf-Eldeen,et al.  A Review of Gearbox Condition Monitoring Based on vibration Analysis Techniques Diagnostics and Prognostics , 2011 .

[3]  Nicholas Goodman,et al.  CBM fundamental research at the university of south carolina: A systematic approach to U.S. army rotorcraft CBM and the resulting tangible benefits , 2009 .

[4]  Thomas L. Marzetta,et al.  Detection, Estimation, and Modulation Theory , 1976 .

[5]  Tom,et al.  The U.S. Army and National Guard Vibration Management Enhancement Program (VMEP): Data Analysis and Statistical Results , 2002 .

[6]  Joshua A. Tarbutton,et al.  Condition monitoring of helicopter drive shafts using quadratic-nonlinearity metric based on cross-bispectrum , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[8]  A. Bayoumi,et al.  Cost and effectiveness analysis of the AH-64 and UH-60 on-board vibrations monitoring system , 2005, 2005 IEEE Aerospace Conference.

[9]  Boualem Boashash,et al.  Higher-order statistical signal processing , 1995 .

[10]  O. L. Frost.  Power-Spectrum Estimation , 1977 .

[11]  A. Arapostathis,et al.  ASD System Condition Monitoring Using Cross Bicoherence , 2007, 2007 IEEE Electric Ship Technologies Symposium.

[12]  Yong-June Shin,et al.  Analysis of nonlinear vibration-interaction using higher order spectra to diagnose aerospace system faults , 2012, 2012 IEEE Aerospace Conference.

[13]  Tommy W. S. Chow,et al.  HOS-based nonparametric and parametric methodologies for machine fault detection , 2000, IEEE Trans. Ind. Electron..

[14]  Yong-June Shin,et al.  Quadratic-nonlinearity power-index spectrum and its application in condition based maintenance (CBM) of helicopter drive trains , 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[15]  Changyong Shin,et al.  Machine fault detection using bicoherence spectra , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).

[16]  Darryll J. Pines,et al.  A review of vibration-based techniques for helicopter transmission diagnostics , 2005 .

[17]  Saeed Vaseghi,et al.  Power Spectrum Estimation , 1995 .

[18]  F. Lad,et al.  Approximating the Distribution for Sums of Products of Normal Variables , 2003 .

[19]  Yong-June Shin,et al.  Quadratic-Nonlinearity Index Based on Bicoherence and its Application in Condition Monitoring of Drive-Train Components , 2014, IEEE Transactions on Instrumentation and Measurement.

[20]  H. V. Trees.  Detection, Estimation, And Modulation Theory , 2001 .