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A Multivariate Statistics-Based Approach for Detecting Diesel Engine Faults with Weak Signatures

Author

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  • Jinxin Wang

    (College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China)

  • Chi Zhang

    (College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China)

  • Xiuzhen Ma

    (College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China)

  • Zhongwei Wang

    (College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China)

  • Yuandong Xu

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

  • Robert Cattley

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

Abstract

The problem of timely detecting the engine faults that make engine operating parameters exceed their control limits has been well-solved. However, in practice, a fault of a diesel engine can be present with weak signatures, with the parameters fluctuating within their control limits when the fault occurs. The weak signatures of engine faults bring considerable difficulties to the effective condition monitoring of diesel engines. In this paper, a multivariate statistics-based fault detection approach is proposed to monitor engine faults with weak signatures by taking the correlation of various parameters into consideration. This approach firstly uses principal component analysis (PCA) to project the engine observations into a principal component subspace (PCS) and a residual subspace (RS). Two statistics, i.e., Hotelling’s T 2 and Q statistics, are then introduced to detect deviations in the PCS and the RS, respectively. The Hotelling’s T 2 and Q statistics are constructed by taking the correlation of various parameters into consideration, so that faults with weak signatures can be effectively detected via these two statistics. In order to reasonably determine the control limits of the statistics, adaptive kernel density estimation (KDE) is utilized to estimate the probability density functions (PDFs) of Hotelling’s T 2 and Q statistics. The control limits are accordingly derived from the PDFs by giving a desired confidence level. The proposed approach is demonstrated by using a marine diesel engine. Experimental results show that the proposed approach can effectively detect engine faults with weak signatures.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:873-:d:321437
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    References listed on IDEAS

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    Cited by:

    1. Włodzimierz Kamiński, 2022. "Marine Slow-Speed Engines’ Cylinder Oil Lubrication Feed Rate Optimization in Real Operational Conditions," Energies, MDPI, vol. 15(22), pages 1-14, November.
    2. Mirosław Kornatka & Anna Gawlak, 2021. "An Analysis of the Operation of Distribution Networks Using Kernel Density Estimators," Energies, MDPI, vol. 14(21), pages 1-12, October.
    3. Włodzimierz Kamiński & Iwona Michalska-Pożoga, 2023. "Possibility of Marine Low-Speed Engine Piston Ring Wear Prediction during Real Operational Conditions," Energies, MDPI, vol. 16(3), pages 1-13, February.

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