Very often processes associated with rotating machinery entail two general types of random processes: (i) quasi-periodic processes linked directly to the machine rotational frequency (including harmonics and sub-harmonics), and (ii) regular processes such as those associated with random vibration of machinery elements. Variability of the rotational frequency and slow-time variability can both be induced by factors such as time-varying external loading and changing thermodynamics (e.g. the influence of road grade and engine temperature on automobiles using cruise control). In this work we incorporate and extended Kalman filter (EKF) model guided by estimates of the family of AR and MV spectral estimates to arrive at a method of decomposing and tracking these two types of processes, along with related parameters (e.g. frequency jitter bandwidth/intensity and the strength, damping and natural frequency of randomly excited narrowband resonances). This work may be viewed in some ways as an extension of the work presented in [1]. Our findings include (i) for the case of constant nominal speed, the EKF estimates of the time-varying frequency, sine and colored noise processes are quite good; whereas the estimate of the time-varying amplitude is not as good, and (ii) in the case of linearly increasing speed (where the colored noise resonance behavior is only excited in that portion of the observation time where the sine frequency is in close proximity), the estimated root mean-squared errors related to both the frequency and the noise increase linearly with the nominal frequency rate of change.
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ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis
July 25–27, 2014
Copenhagen, Denmark
Conference Sponsors:
- International
ISBN:
978-0-7918-4583-7
PROCEEDINGS PAPER
Application of Extended Kalman Filtering and Novel Spectral Methods in Stochastic Characterization of Quasi-Periodic Slow-Time Varying Rotating Machinery Processes
Peter J. Sherman
Peter J. Sherman
Iowa State University, Ames, IA
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Peter J. Sherman
Iowa State University, Ames, IA
Paper No:
ESDA2014-20545, V001T02A020; 10 pages
Published Online:
October 23, 2014
Citation
Sherman, PJ. "Application of Extended Kalman Filtering and Novel Spectral Methods in Stochastic Characterization of Quasi-Periodic Slow-Time Varying Rotating Machinery Processes." Proceedings of the ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis. Volume 1: Applied Mechanics; Automotive Systems; Biomedical Biotechnology Engineering; Computational Mechanics; Design; Digital Manufacturing; Education; Marine and Aerospace Applications. Copenhagen, Denmark. July 25–27, 2014. V001T02A020. ASME. https://doi.org/10.1115/ESDA2014-20545
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