Fault feature enhancement for rotating machinery based on quality factor analysis and manifold learning

Meng Gan (), Cong Wang () and Chang’an Zhu ()
Additional contact information
Meng Gan: University of Science and Technology of China (USTC)
Cong Wang: University of Science and Technology of China (USTC)
Chang’an Zhu: University of Science and Technology of China (USTC)

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 2, No 14, 463-480

Abstract: Abstract This paper explores an improved time-frequency signature to enhance the periodic transient shocks of the signal, called impulse-enhanced signature (IES) for identifying rotating machine faults. IES is extracted in the following steps: first, phase space reconstruction is applied to the analyzed signal to present its dynamic signature in high-dimensional space; second, employ quality factor (Q-factor) based decomposition on the phase space to separate the fault transient component from the vibration signal; third, utilize the continuous wavelet transform to present nonstationary information embedded in the signal and finally, IES is obtained by optimizing the low-dimension structure, which is extracted from the phase space using manifold learning. The IES significantly improves the fault information with a highly regular representation, especially for weak fault-induced impulses, and its advantages over other approaches include noise suppression and energy concentration. One simple IES based curve, time marginal amplitude (TMA), is extracted to further detect the fault characteristic frequency and evaluate the performance of IES. Simulation and experiments confirm the effectiveness of the proposed method. Results indicate that IES outperforms traditional empirical mode decomposition envelop analysis for diagnosing rotating machine faults.

Keywords: Oscillatory waveform; Quality factor based signal decomposition; Manifold learning; Impulse-enhanced signature (IES); Machinery fault diagnosis (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-015-1125-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:29:y:2018:i:2:d:10.1007_s10845-015-1125-6

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-015-1125-6

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().