Misfire Fault Diagnosis of Range Extender Based on Harmonic Analysis

Xu, Xiaowei; Liu, Zhenxing; Wu, Jiangdong; Jiaming, Xing; Wang, Xiaoqing

doi:10.1007/s12239-019-0009-8

Cited by 12 publications

(4 citation statements)

References 26 publications

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“…The current trend is to model vehicular propulsion in the context of hybrid electric-IC engines, as well as smaller engines acting as range extenders (such as single, twin or three-cylinder configurations). [194][195][196] With the emergence of dual-fuel engines, such as hydrogen-diesel, particularly with potential use in marine and heavy vehicle applications, multi-body analysis will continue to play a role in the prediction of performance of various propulsion systems. Some recent analyses include.…”

Section: Tribo-elasto-multi-body Dynamics Of Ic Enginesmentioning

confidence: 99%

Multi-body dynamics in vehicle engineering

Rahnejat

Johns-Rahnejat

Dolatabadi

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part K:

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Since Euler's original gyro-dynamic analysis nearly two and a half centuries ago, the use of multi-body dynamics (MBD) has spread widely in application scope from large displacement rigid body dynamics to infinitesimal amplitude elastodynamics. In some cases, MBD has become a multi-physics multi-scale analysis, comprising contact mechanics, tribo-dynamics, terramechanics, thermodynamics, biomechanics, etc. It is an essential part of all analyses in many engineering disciplines, including vehicle engineering. This paper provides an overview of historical developments with emphasis on vehicle development and investigation of observed phenomena, including noise, vibration and harshness. The approach undertaken is comprehensive and provides a uniquely focused perspective, one which has not hitherto been reported in the literature.

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Section: Tribo-elasto-multi-body Dynamics Of Ic Enginesmentioning

confidence: 99%

Multi-body dynamics in vehicle engineering

Rahnejat

Johns-Rahnejat

Dolatabadi

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part K:

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show abstract

“…Misfire of combustion is a typical and serious problem a diesel engine often meets, which can directly lead to working condition deterioration, additional vibration and noise, and then reduce the stability and reliability of the engine [27][28][29][30]. Through the identification of the generalized force at the engine center of gravity, Xu et al [31] proposed a method on detecting the misfire fault, in which the acceleration signals at the mounts were analysed by discrete spectrum interpolation method.…”

Section: Introductionmentioning

confidence: 99%

Torsional vibration responses of the engine crankshaft-gearbox coupled system with misfire and breathing slant crack based on instantaneous angular speed

Liu

Ding

et al. 2022

Mechanical Systems and Signal Processing

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“…Conventional FD methods mainly consist of analytic model-based, knowledge-based and data-driven approaches [4]. For the diesel engine, early studies of FD have been researched, such as those on artificial neural networks (ANN) [5], the support vector machine (SVM) [6], variational mode decomposition, and Bayesian networks (BN) [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Multi-branch convolutional neural networks with integrated cross-entropy for fault diagnosis in diesel engines

Zhao

Mao

Zhang

et al. 2021

Meas. Sci. Technol.

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Fault diagnosis based on deep learning has become a hot research topic because of the successful application of deep learning in other fields. Due to variable operating conditions and a harsh operating environment, it is extremely difficult to effectively diagnose some typical faults of diesel engines. When operating conditions and environmental factors change, the performance of deep learning models also become extremely unstable. In order to solve these problems, this paper proposes a novel deep learning model, called multi-branch convolutional neural networks (MBCNNs) with an integrated cross-entropy. MBCNN can be embedded in the proposed model and simultaneously equipped with four auxiliary classifiers. The proposed model is trained on two different datasets separately, which consist of six diesel engine faults. The trained network model was compared with other methods to prove the superiority of this network model. Meanwhile, by adding Gaussian white noise, the performance of the MBCNN in different noise environments is investigated. The final results show that the MBCNN with integrated cross-entropy can effectively diagnose different diesel engine faults under variable operating conditions.

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Misfire Fault Diagnosis of Range Extender Based on Harmonic Analysis

Cited by 12 publications

References 26 publications

Multi-body dynamics in vehicle engineering

Multi-body dynamics in vehicle engineering

Torsional vibration responses of the engine crankshaft-gearbox coupled system with misfire and breathing slant crack based on instantaneous angular speed

Multi-branch convolutional neural networks with integrated cross-entropy for fault diagnosis in diesel engines

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