A Combined Polynomial Chirplet Transform and Synchroextracting Technique for Analyzing Nonstationary Signals of Rotating Machinery

Time-frequency analysis (TFA) technique is an effective approach to capture the changing dynamic in a nonstationary signal. However, the commonly adopted TFA techniques are inadequate in dealing with signals having a strong nonstationary characteristic or multicomponent signals having close frequenc...

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Veröffentlicht in:IEEE transactions on instrumentation and measurement Jg. 69; H. 4; S. 1505 - 1518
Hauptverfasser: Yu, Kun, Lin, Tian Ran, Ma, Hui, Li, Hongfei, Zeng, Jin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Chirp
Frequency modulation
Kernel
Nonstationary characteristic
polynomial chirplet transform (PCT)
Polynomials
Rotating machinery
Signal processing
Signal resolution
synchroextracting transform (SET)
Time-frequency analysis
time–frequency analysis (TFA)
Trajectory
Transforms
vibration signal
ISSN:0018-9456, 1557-9662
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Abstract Time-frequency analysis (TFA) technique is an effective approach to capture the changing dynamic in a nonstationary signal. However, the commonly adopted TFA techniques are inadequate in dealing with signals having a strong nonstationary characteristic or multicomponent signals having close frequency components. To overcome this shortcoming, a new TFA technique applying a polynomial chirplet transform (PCT) in association with a synchroextracting transform (SET) is proposed in this paper. It is shown that the energy concentration of the time-frequency representation (TFR) of a strong frequency-modulated signal from a PCT transform can be further enhanced by an SET transform. The technique can also be employed to accurately extract the signal components of a multicomponent nonstationary signal with close frequency components by adopting an iterative process. It is found that the TFR calculated from the proposed technique matches well with the ideal TFR, which demonstrates the superiority of the current technique in dealing with nonstationary signals having rapidly changing dynamics. Results from the analysis of the experimental data under varying speed conditions confirm the validity of the proposed technique in dealing with nonstationary signals from practical sources.
AbstractList Time–frequency analysis (TFA) technique is an effective approach to capture the changing dynamic in a nonstationary signal. However, the commonly adopted TFA techniques are inadequate in dealing with signals having a strong nonstationary characteristic or multicomponent signals having close frequency components. To overcome this shortcoming, a new TFA technique applying a polynomial chirplet transform (PCT) in association with a synchroextracting transform (SET) is proposed in this paper. It is shown that the energy concentration of the time–frequency representation (TFR) of a strong frequency-modulated signal from a PCT transform can be further enhanced by an SET transform. The technique can also be employed to accurately extract the signal components of a multicomponent nonstationary signal with close frequency components by adopting an iterative process. It is found that the TFR calculated from the proposed technique matches well with the ideal TFR, which demonstrates the superiority of the current technique in dealing with nonstationary signals having rapidly changing dynamics. Results from the analysis of the experimental data under varying speed conditions confirm the validity of the proposed technique in dealing with nonstationary signals from practical sources.
Author Yu, Kun
Ma, Hui
Zeng, Jin
Lin, Tian Ran
Li, Hongfei
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Snippet Time-frequency analysis (TFA) technique is an effective approach to capture the changing dynamic in a nonstationary signal. However, the commonly adopted TFA...
Time–frequency analysis (TFA) technique is an effective approach to capture the changing dynamic in a nonstationary signal. However, the commonly adopted TFA...
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SubjectTerms Chirp
Frequency modulation
Kernel
Nonstationary characteristic
polynomial chirplet transform (PCT)
Polynomials
Rotating machinery
Signal processing
Signal resolution
synchroextracting transform (SET)
Time-frequency analysis
time–frequency analysis (TFA)
Trajectory
Transforms
vibration signal
Title A Combined Polynomial Chirplet Transform and Synchroextracting Technique for Analyzing Nonstationary Signals of Rotating Machinery
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