Application of Lubricants in the AH-64D Helicopter Gearboxes for Improvement of Condition-based Maintenance Practices

Date of Award

2015

Document Type

Open Access Dissertation

Department

Mechanical Engineering

First Advisor

Abdel-Moez E. Bayoumi

Abstract

This dissertation aims to develop new condition-based maintenance (CBM) tools for increasing performance of Apache helicopter drivetrain gearboxes, through lubrication and signal analysis. An extensive consumer of CBM is the Apache helicopter that involves testing critical components to demonstrate reliability and performance, measured through data-driven condition indicators (CIs). These indicators provide vital information about the condition of a mechanical component and are derived from onboard sensors using signal processing methods. A challenge known among researchers in the Apache community is leakage of grease lubricant from drivetrain gearboxes with performance of some CIs unachieved. Although extensive maintenance operations are used in the field to minimize this effect by ground inspections, failure of grease lubricant still occurs due to heavy loads. Based on this motivation, oil nanofluids are presented here as a new lubricant approach for the intermediate gearbox (IGB) of the Apache helicopter. Furthermore, a signal-based approach utilizing wavelet analysis is adapted to develop a new CI. The goal of this work can be achieved through addressing the following research studies.

The first study qualifies the improved thermophysical properties of two turbine jet oil nanofluid samples and eight Mobil Aviation Gear Lubricant (AGL) oil nanofluid samples with different particle concentrations and chemical compositions. Numerous fluid properties such as thermal conductivity, dynamic viscosity, and viscosity index are measured using off-line experimental tools. Based on the experimental results, four AGL oils with graphite nanoparticles have displayed excellent thermophysical properties and are used as prospective lubricants for mechanical testing in the IGB. Then, this study investigates key nanoparticle mechanisms to provide a better understanding of the nanofluids through developing a new nanofluid model using the effective medium approach. It is found that experimental results closely agree with theoretical predictions (eg. R2 =0.988). Experimental data and existing models from the literature are also used to validate the accuracy of the proposed model. Results help in improving model predictions and conclude that the flake-like morphology of the nanoparticles as well as its dynamic behavior in the fluid contribute significantly to increasing thermophysical properties.

The second study investigates the impact of nanolubricants in an actual IGB. Two Apache helicopter drivetrain test stands at the CBM research center, University of South Carolina are presented to optimize performance of the lubricant. First, preliminary testing is performed on a drivetrain test stand with no load being applied. The four selected samples: 1%, 1.5%, 2% and 2.5% graphite-based AGL additives, against a control AGL sample are tested as gearbox lubricants. Preliminary condition monitoring results show that the 2% sample demonstrates optimum gearbox performance with the lowest temperature and vibration responses, compared to all tested lubricants. This improvement can be attributed to the capability of the nanolubricant in maintaining an effective fluid film between gear surfaces, absorbing load, heat, and friction. The highest concentration of 2.5% graphite additive fails to make an impact and yields the worst gearbox performance due to possible rheological changes in oil. Second, full-load mechanical testing on the tail rotor drivetrain test stand is demonstrated. The 2% nanofluid sample, along with base oil, are tested in the IGB. Vibration results based on spectral and wavelet analysis demonstrate promising attributes of this new lubrication approach. A key finding from this study is the improvement of temperature-based CI due to the incorporation of nanolubricants with approximately 40 degree F-50 degree F lower response, compared to that of base AGL.

The third study presents a new CI using wavelet analysis for the purpose of fault detection in an AH-64 gearbox. Historically, vibration-based CIs from employed component monitoring equipment are derived from both temporal and spectral domain analysis. However, these indicators failed to accurately capture high order correlations for the gearbox study addressed. An improved approach is necessary to overcome limitations of traditional vibrational monitoring techniques. The proposed condition indicator is derived from the Morlet continuous wavelet transform. The power spectra obtained from the wavelet transform coefficients at a certain scale or frequency are added together and then are normalised to one composite signal, denoted by a numeric index. Concepts of the wavelet index (WI) are discussed. This index is applied using real-world vibration data from a tail rotor gearbox with an output seal leak as part of CBM practices. Results demonstrate potential of the proposed WI to more effectively capture the fault when compared to gearbox CIs. Statistical analysis is demonstrated using a wavelet denoising thresholding approach to reduce redundancy in the data. Predicted results yielded significant improvement in WI with less variability. Finally, a statistical test with an 85% confidence interval is applied on different wavelet power distribution samples. WI results from these samples are found to be statistically valid.

Research results described in this dissertation establish a step forward towards the development of new CBM tools in system-based applications. This work ends with conclusions and recommendations for future research.

Rights

© 2015, Kareem Moustafa Gouda

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