Fault Prediction Methods for Locomotive Turbochargers

Yu Xing, Weida Wei, He Li () and Junyu Guo ()
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Yu Xing: Dalian CRRC Diesel Engine Co., LTD.
Weida Wei: Dalian University of Technology, Institute of Internal Combustion Engine
He Li: Universidade de Lisboa, Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico
Junyu Guo: Southwest Petroleum University, School of Mechatronic Engineering

A chapter in Data-Driven Methods for Reliability and Safety Engineering: Applications in Industrial Systems, 2026, pp 701-728 from Springer

Abstract: Abstract Fault prediction technology predicts failures based on data collected from various locomotive sensors, achieving the effect of “predictive maintenance” and effectively improving the reliability and safety of equipment. The core of fault prediction is to establish an accurate model to ensure that the prediction results closely match the actual results. Early fault prediction methods mainly use single mathematical models for equipment fault prediction. Although the accuracy is satisfactory, the general applicability is limited. This paper conducts a detailed analysis of the multivariable grey prediction model, introduces neural networks and genetic algorithms to optimize the model, and applies it to the fault prediction of locomotive turbochargers. By fully exploiting the advantages of each method and organically combining them, the operating condition of locomotive turbochargers can be accurately and efficiently predicted, ensuring efficient operation and enhancing the reliability of the locomotive.

Keywords: Fault prediction; Multivariable grey prediction model; Neural network; Genetic algorithm; Turbocharger (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-032-22873-4_47

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DOI: 10.1007/978-3-032-22873-4_47

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