An informative frequency band identification framework for gearbox fault diagnosis under time-varying operating conditions. (September 2021)

• Record Type: Journal Article
• Authors: Schmidt, Stephan; Heyns, P. Stephan; Gryllias, Konstantinos C.
• Is Part Of: Mechanical systems and signal processing. Volume 158(2021)
• Publication Date: 2021-09
• Subjects: AF Angle-Frequency -- AO Angle-Order -- BPOO Ball-Pass Order of the Outer race -- BPOI Ball-Pass Order of the Inner race -- CMS Cyclic Modulation Spectrum -- CPW Cepstrum Pre-Whitening -- DGD Distributed Gear Damage -- FBI Frequency Band Identification -- FF Frequency–Frequency -- ICS2 Indicator that measures the degree of second-order CycloStationarity -- IPS Instantaneous Power Spectrum -- LGD Localised Gear Damage -- OF Order-Frequency -- OO Order-Order -- RMS Root-Mean-Square -- SCoh Spectral Coherence -- SES Squared Envelope Spectrum -- STFT Short-Time Fourier Transform -- TF Time–Frequency; Vibration-based condition monitoring -- Time-varying operating conditions -- Frequency band identification -- IFBIαgram; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.107771
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A frequency band identification framework is proposed for varying operating conditions. A new estimator of the IFBI α gram is proposed using this framework. Numerical and experimental data, acquired from varying operating conditions, are used. This IFBI α gram performs the best among the considered methods. Abstract: The application of informative frequency band identification methods makes it possible to enhance weak damage components in the vibration signals acquired from rotating machines. Some rotating machines (e.g. wind turbines) operate inherently under time-varying operating conditions, however, very few frequency band identification methods have been developed with varying operating conditions in mind. Therefore, in this work, a systematic framework for obtaining consistent feature planes under time-varying operating conditions is proposed. This framework utilises the angle-frequency instantaneous power spectrum and the order-frequency cyclic modulation spectrum to construct feature planes. The kurtogram, the sparsogram, the infogram, the ICS2gram and the log-cycligram are obtained on numerical and experimental datasets acquired under time-varying operating conditions using this framework. In addition to this, we also implement the Informative Frequency Band Identification method using targeted cyclic orders, abbreviated to IFBI α gram, in this framework and compare the performance of this method against the other frequency band identification methods. Ultimately, we found that the feature used in the construction of the IFBI α gram is very well-suited for gear and bearing fault diagnosis under time-varying operating conditions.