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An Investigation into the Acoustic Emissions of Internal Combustion Engines with Modelling and Wavelet Package Analysis for Monitoring Lubrication Conditions

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  • Nasha Wei

    (School of Economics and Management, Taiyuan University of Science and Technology, Shanxi 030024, China
    College of mechanical and vehicle engineering, Taiyuan University of Technology, Shanxi 030024, China)

  • James Xi Gu

    (School of Engineering, University of Bolton, Bolton BL3 5AB, UK)

  • Fengshou Gu

    (College of mechanical and vehicle engineering, Taiyuan University of Technology, Shanxi 030024, China
    School of Engineering, University of Bolton, Bolton BL3 5AB, UK)

  • Zhi Chen

    (School of Economics and Management, Taiyuan University of Science and Technology, Shanxi 030024, China)

  • Guoxing Li

    (College of mechanical and vehicle engineering, Taiyuan University of Technology, Shanxi 030024, China)

  • Tie Wang

    (College of mechanical and vehicle engineering, Taiyuan University of Technology, Shanxi 030024, China)

  • Andrew D. Ball

    (Centre for Efficiency and Performance Engineering, University of Huddersfield HD1 3DH, UK)

Abstract

Online monitoring of the lubrication and friction conditions in internal combustion engines can provide valuable information and thereby enables optimal maintenance actions to be undertaken to ensure safe and efficient operations. Acoustic emission (AE) has attracted significant attention in condition monitoring due to its high sensitivity to light defects on sliding surfaces. However, limited understanding of the AE mechanisms in fluid-lubricated conjunctions, such as piston rings and cylinder liners, confines the development of AE-based lubrication monitoring techniques. Therefore, this study focuses on developing new AE models and effective AE signal process methods in order to achieve accurate online lubrication monitoring. Based on the existing AE model for asperity–asperity collision (AAC), a new model for fluid–asperity shearing (FAS)-induced AE is proposed that will explain AE responses from the tribological conjunction of the piston ring and cylinder. These two AE models can then jointly demonstrate AE responses from the lubrication conjunction of engine ring–liner. In particular, FAS allows the observable AE responses in the middle of engine strokes to be characterised in association with engine speeds and lubricant viscosity. However, these AE components are relatively weak and noisy compared to others, with movements such as valve taring, fuel injection and combustions. To accurately extract these weaker AE’s for lubricant monitoring, an optimised wavelet packet transform (WPT) analysis is applied to the raw AE data from a running engine. This results in four distinctive narrow band indicators to describe the AE amplitude in the middle of an engine power stroke. Experimental evaluation shows the linear increasing trend of AE indicator with engine speeds allows a full separation of two baseline engine lubricants (CD-10W30 and CD-15W40), previously unused over a wide range of speeds. Moreover, the used oil can also be diagnosed by using the nonlinear and unstable behaviours of the indicator at various speeds. This model has demonstrated the high performance of using AE signals processed with the optimised WPT spectrum in monitoring the lubrication conditions between the ring and liner in IC engines.

Suggested Citation

  • Nasha Wei & James Xi Gu & Fengshou Gu & Zhi Chen & Guoxing Li & Tie Wang & Andrew D. Ball, 2019. "An Investigation into the Acoustic Emissions of Internal Combustion Engines with Modelling and Wavelet Package Analysis for Monitoring Lubrication Conditions," Energies, MDPI, vol. 12(4), pages 1-19, February.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:640-:d:206611
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    References listed on IDEAS

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    1. Yanling Lv & Yuting Gao & Jian Zhang & Chenmin Deng & Shiqiang Hou, 2018. "Symmetrical Loss of Excitation Fault Diagnosis in an Asynchronized High-Voltage Generator," Energies, MDPI, vol. 11(11), pages 1-18, November.
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    Cited by:

    1. Artur Bejger & Tomasz Piasecki, 2020. "The Use of Acoustic Emission Elastic Waves for Diagnosing High Pressure Mud Pumps Used on Drilling Rigs," Energies, MDPI, vol. 13(5), pages 1-16, March.
    2. Nasha Wei & Zhi Chen & Yuandong Xu & Fengshou Gu & Andrew Ball, 2021. "The Investigation into the Tribological Impact of Alternative Fuels on Engines Based on Acoustic Emission," Energies, MDPI, vol. 14(8), pages 1-20, April.
    3. Jinxin Wang & Chi Zhang & Xiuzhen Ma & Zhongwei Wang & Yuandong Xu & Robert Cattley, 2020. "A Multivariate Statistics-Based Approach for Detecting Diesel Engine Faults with Weak Signatures," Energies, MDPI, vol. 13(4), pages 1-14, February.

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