Using frequency domain analysis techniques for diagnosis of planetary bearing defect in a CH-46E helicopter aft gearbox

Abstract Condition monitoring for helicopters has always been one of the most critical technologies to guarantee the integrity of the rotorcrafts, enhance operational and personnel safety, and reduce the overall maintenance costs. Over the past decades, health and usage monitoring system (HUMS) has been developed and implemented in helicopters to monitor the health status for the main gearbox (MGB) and other key components of the transmission system, improving condition-based maintenance for helicopters. However, many studies have indicated that current HUMS has a limited sensitivity to MGB planetary bearing defects. To enhance HUMS' performance, this paper presents an approach based on frequency domain analysis techniques to diagnose planetary bearing defects using real helicopter data collected from a CH-46E helicopter aft MGB. Vibration data was processed using signal processing techniques including self-adaptive noise cancellation (SANC), discrete-random separation (DRS), cepstrum editing, kurtogram, envelope analysis and iterative envelope cancellation. Processing results conclude that frequency domain analysis techniques can provide distinct and intuitive indications of the seeded defects at both the inner race and the outer race of the faulty planetary bearing.

[1]  Robert B. Randall,et al.  A history of cepstrum analysis and its application to mechanical problems , 2017 .

[2]  Robert B. Randall,et al.  Unsupervised noise cancellation for vibration signals: part II—a novel frequency-domain algorithm , 2004 .

[3]  Fengshou Gu,et al.  A robust detector for rolling element bearing condition monitoring based on the modulation signal bispectrum , 2016 .

[4]  R. Dwyer Use of the kurtosis statistic in the frequency domain as an aid in detecting random signals , 1984 .

[5]  Robert H. Badgley,et al.  Application of High-Frequency Resonance Techniques for Bearing Diagnostics in Helicopter Gearboxes , 1974 .

[6]  Bryan D. Rex Non-Invasive Detection of CH-46 AFT Gearbox Faults Using Digital Pattern Recognition and Classification Techniques , 1999 .

[7]  R. Hess,et al.  The IMD HUMS as a tool for rotorcraft health management and diagnostics , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[8]  David,et al.  Vibration Health or Alternative Monitoring Technologies for Helicopters , 2017 .

[9]  B. Widrow,et al.  Adaptive noise cancelling: Principles and applications , 1975 .

[10]  Cristobal Ruiz-Carcel,et al.  Application of Linear Prediction, Self-Adaptive Noise Cancellation, and Spectral Kurtosis in Identifying Natural Damage of Rolling Element Bearing in a Gearbox , 2015 .

[11]  Robert B. Randall,et al.  Unsupervised noise cancellation for vibration signals: part I—evaluation of adaptive algorithms , 2004 .

[12]  J. E. Land HUMS-the benefits-past, present and future , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[13]  Fulei Chu,et al.  Envelope calculation of the multi-component signal and its application to the deterministic component cancellation in bearing fault diagnosis , 2015 .

[14]  Cristobal Ruiz-Carcel,et al.  A Comparative Study of the Effectiveness of Adaptive Filter Algorithms, Spectral Kurtosis and Linear Prediction in Detection of a Naturally Degraded Bearing in a Gearbox , 2014, Journal of Failure Analysis and Prevention.

[15]  D. Ho EFFECTS OF TIME DELAY , ORDER OF FIR FILTER AND CONVERGENCE FACTOR ON SELF ADAPTIVE NOISE CANCELLATION , 1997 .

[17]  R. Randall,et al.  OPTIMISATION OF BEARING DIAGNOSTIC TECHNIQUES USING SIMULATED AND ACTUAL BEARING FAULT SIGNALS , 2000 .

[18]  Saudi Arabia,et al.  Localized fault detection and diagnosis in rolling element bearings: A collection of the state of art processing algorithms , 2013 .

[19]  J. Antoni The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .

[20]  Robert B. Randall,et al.  A comparison of methods for separation of deterministic and random signals , 2011 .

[21]  K. F. Fraser An Overview of Health and Usage Monitoring Systems (HUMS) for Military Helicopters , 1994 .

[22]  Tomasz Barszcz Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and Identification , 2009, Int. J. Appl. Math. Comput. Sci..

[23]  J. Antoni Fast computation of the kurtogram for the detection of transient faults , 2007 .

[24]  Robert B. Randall,et al.  Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions , 2013 .

[25]  Robert B. Randall,et al.  Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications , 2011 .

[26]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[27]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[28]  Valeriu Vrabie,et al.  Spectral kurtosis: from definition to application , 2003 .