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Concept of the multidimensional diagnostic tool based on exhaust gas composition for marine engines

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  • Kowalski, Jerzy

Abstract

This paper presents the concept of a multi-dimensional marine engine diagnostic tool. The dimensions of the tool are diagnostic signals, which form a vector in affine space. The distance of the resulting vector from reference vectors for considered technical states of the engine is the result of diagnosis. Moreover, diagnostic signals, derived from the composition of the exhaust gas, are also considered. The chosen diagnostic signals are the nitric oxide, carbon oxide, carbon dioxide and oxygen contents in the exhaust gas and the temperatures behind all engine cylinders of the marine engine. Analyses were based on laboratory tests of a 4-stroke marine engine. The operation of the proposed diagnostic tool has been partly verified by a passive experiment under sea operation conditions of a main propulsion marine engine.

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  • Kowalski, Jerzy, 2015. "Concept of the multidimensional diagnostic tool based on exhaust gas composition for marine engines," Applied Energy, Elsevier, vol. 150(C), pages 1-8.
    • Handle: RePEc:eee:appene:v:150:y:2015:i:c:p:1-8
    DOI: 10.1016/j.apenergy.2015.04.013
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    References listed on IDEAS

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    1. Tan, Mengquan & Chen, Lingen & Jin, Jiashan & Sun, Fengrui & Wu, Chih, 2005. "Bond-graph-based fault-diagnosis for a marine condensate-booster-feedwater system," Applied Energy, Elsevier, vol. 81(4), pages 449-458, August.
    2. Zhao, Yang & Wang, Shengwei & Xiao, Fu, 2013. "Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD)," Applied Energy, Elsevier, vol. 112(C), pages 1041-1048.
    3. Du, Zhimin & Jin, Xinqiao & Yang, Yunyu, 2009. "Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network," Applied Energy, Elsevier, vol. 86(9), pages 1624-1631, September.
    4. Bonvini, Marco & Sohn, Michael D. & Granderson, Jessica & Wetter, Michael & Piette, Mary Ann, 2014. "Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques," Applied Energy, Elsevier, vol. 124(C), pages 156-166.
    5. Ogaji, S. O. T. & Singh, R. & Probert, S. D., 2002. "Multiple-sensor fault-diagnoses for a 2-shaft stationary gas-turbine," Applied Energy, Elsevier, vol. 71(4), pages 321-339, April.
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    Cited by:

    1. Yao, Zhi-Min & Qian, Zuo-Qin & Li, Rong & Hu, Eric, 2019. "Energy efficiency analysis of marine high-powered medium-speed diesel engine base on energy balance and exergy," Energy, Elsevier, vol. 176(C), pages 991-1006.
    2. Ling-Chin, Janie & Roskilly, Anthony P., 2016. "Investigating the implications of a new-build hybrid power system for Roll-on/Roll-off cargo ships from a sustainability perspective – A life cycle assessment case study," Applied Energy, Elsevier, vol. 181(C), pages 416-434.

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