A novel targeted method of informative frequency band selection based on lagged information for diagnosis of gearbox single and compound faults. (1st May 2022)

• Record Type: Journal Article
• Authors: Alavi, Hassan; Ohadi, Abdolreza; Niaki, Soheil Tofighi
• Is Part Of: Mechanical systems and signal processing. Volume 170(2022)
• Publication Date: 2022-05-01
• Subjects: CFI cyclic feature index -- FCF fault characteristic frequency -- FFC fault feature content -- FK fast kurtogram -- FLOM fractional lower order moments -- FLOC fractional lower order correlation -- FLOAC fractional lower order autocorrelation -- FRF fault resonance frequency -- ICS2 indicator of second-order cyclo-stationarity -- LEAS logenvelope auto-spectrum -- LES log-envelope spectrum -- LII lagged information index -- LIS lagged information spectrum -- MODWPT maximal overlap discrete wavelet packet transform -- NSR noise to signal ratio -- RCC ratio of cyclic content -- SE squared envelope -- SES squared envelope spectrum -- SK spectral kurtosis -- WPT wavelet packet transform; Gearbox -- Fault diagnosis -- Compound faults -- Signal processing -- Filter-bank -- Informative frequency band -- Lagged Information Spectrum; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.108828
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Propose a novel accurate frequency band selection method for single and compound fault diagnosis. Investigate the performance of proposed method against various Gaussian and non-Gaussian noise levels via a simulated signal of faulty gearbox. Compare the effectiveness of proposed method with a wide range of chronologically important and/or state of the art frequency band selection methods. Investigate the performance of the proposed method under three experimental case studies. Study the quality of spectrum generated by the proposed method and make comparisons with other widely accepted spectra in envelope analysis. Abstract: Detection of compound faults in rotating machinery remained a challenge in the field of fault diagnosis since the presence of multiple faults at the same time acts as an interference in the isolation of a specific fault signature. One of the widely used methods in rotating machinery fault diagnosis is envelope analysis. The envelope spectrum of a properly band-pass filtered signal could be informative about the presence of a fault. The main challenge in envelope analysis is the selection of appropriate criteria to choose the most informative frequency band. A novel targeted informative frequency band selection method have been proposed in this work, which will be referred as Lagged Information Spectrum (LIS). Lagged information inspired by the fractional lower order autocorrelation and its logarithm-based derivation results in a close relation to the concept of negentropy. The proposed method can reduce the effect of both Gaussian and non-Gaussian noise in the signal, and in the same time, separate the signatures of compound faults. The performance of the proposed method has been evaluated under both simulated and experimental gearbox vibration signals and compared to a wide range of other previously established methods of informative frequency band selection. The results of simulated signal verify that the proposed method is more robust than other methods in the presence of Gaussian noise for noise to signal ratios up to 3 dB while its efficiency under moderate non-Gaussian impulsive noise is acceptable (up to 11 dB noise to signal ratio). One experimental case study of single chipping fault and two experimental case studies of compound wear/crack and chipping/spalling faults have been accomplished to show the performance of the proposed method in separation of fault signatures. The proposed method outperforms other methods in isolation of fault characteristic frequencies and generating higher quality spectra based on Fault Feature Coefficient (FFC) criterion, especially, in two compound fault case studies.