Abstract
Bearing faults detection at the earliest stages is vital in avoiding future catastrophic failures. Many traditional techniques have been established and utilized in detecting bearing faults, though, these diagnostic techniques are not always successful when the bearing faults take place in gearboxes where the vibration signal is complex; under such circumstances it may be necessary to separate the bearing signal from the complex signal. The objective of this paper is to assess the effectiveness of an adaptive filter algorithms compared to a Spectral Kurtosis (SK) algorithm in diagnosing a bearing defects in a gearbox. Two adaptive filters have been used for the purpose of bearing signal separation, these algorithms were Least Mean Square (LMS) and Fast Block LMS (FBLMS) algorithms. These algorithms were applied to identify a bearing defects in a gearbox employed for an aircraft control system for which endurance tests were performed. The results show that the LMS algorithm is capable of detecting the bearing fault earlier in comparison to the other algorithms.
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References
Randall RB, Antoni J (2011) Rolling element bearing diagnostics—a tutorial. Mech Syst Signal Process 25(2):485–520
Tan CK, Irving P, Mba D (2007) A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears. Mech Syst Signal Process 21(1):208–233
Li Z, Yan X, Tian Z, Yuan C, Peng Z, Li L (2013) Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis. Measurement 46(1):259–271
Al-Ghamd AM, Mba D (2006) A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mech Syst Signal Process 20(7):1537–1571
McFadden PD, Toozhy MM (2000) Application of synchronous averaging to vibration monitoring of rolling elements bearings. Mech Syst Signal Process 14(6):891–906
Fu-cheng Z (2010) Research on online monitoring and diagnosis of bearing fault of wind turbine gearbox based on undecimated wavelet transformation. In: 2010 IEEE youth conference on information computing and telecommunications (YC-ICT), p 251
Sawalhi N, Randall RB, Endo H (2007) The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mech Syst Signal Process 21(6):2616–2633
Randall RB (2004) Detection and diagnosis of incipient bearing failure in helicopter gearboxes. Eng Fail Anal 11(2):177–190
McFadden PD, Smith JD (1984) Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review. Tribol Int 17(1):3–10
Barszcz T (2009) Decomposition of vibration signals into deterministic and nondeterministic components and its capabilities of fault detection and identification. Int J Appl Math Comput Sci 19(2):327–335
Douglas SC (1999) Introduction to adaptive filters. CRC Press, Boca Raton
Douglas SC, Rupp M (1999) Convergence issues in the LMS adaptive filter. In: Madisetti VK (ed) The digital signal processing handbook, 2nd edn. CRC Press, Atlanta
Widrow B, Glover JR Jr, McCool JM, Kaunitz J, Williams CS, Hearn RH, Zeidler JR, Eugene Dong J, Goodlin RC (1975) Adaptive noise cancelling: principles and applications. Proc IEEE 63(12):1692–1716
Widrow B, McCool J, Ball M (1975) The complex LMS algorithm. Proc IEEE 63(4):719–720
Dentino M, McCool J, Widrow B (1978) Adaptive filtering in the frequency domain. Proc IEEE 66(12):1658–1659
Ferrara ER (1980) Fast implementations of LMS adaptive filters. In: IEEE transactions on acoustics, speech and signal processing, vol 28, no 4, p 474–475
Antoni J (2007) Fast computation of the kurtogram for the detection of transient faults. Mech Syst Signal Process 21(1):108–124
Randall RB (2011) Vibration-based condition monitoring, 1st edn. Wiley, Chichester
Antoni J, Randall R (2006) The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech Syst Signal Process 20(2):308–331
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Elasha, F., Mba, D., Ruiz-Carcel, C. (2015). Effectiveness of Adaptive Filter Algorithms and Spectral Kurtosis in Bearing Faults Detection in a Gearbox. In: Sinha, J. (eds) Vibration Engineering and Technology of Machinery. Mechanisms and Machine Science, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-09918-7_19
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DOI: https://doi.org/10.1007/978-3-319-09918-7_19
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