A key performance indicator-based fault detection scheme for marine diesel turbocharging systems

He, Zhichen; Chen, Xiaolu; Han, Huayun; Wang, Jia; Zhang, Yuhan; He, Li

doi:10.1016/j.jfranklin.2021.09.024

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Tsitsilonis [30] used the instantaneous engine crankshaft torque as a thermal parameter feature for diesel engine fault diagnosis. He et al [31] verified the effectiveness of this scheme by specifically identifying the fault states of the turbocharger through the system criticality metrics. Hou [32] was able to achieve an accuracy of 95% using the improved genetic algorithm (GA) and the multi-layer perceptron (MLP) for the diesel engine cylinder faults.…”

Section: Introductionmentioning

confidence: 97%

Intelligent Fault Diagnosis of Marine Diesel Engines Based on Efficient Channel Attention-Improved Convolutional Neural Networks

Wang,

Cao,

Cui

et al. 2023

Processes

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With the rapid development of smart ships, the ship maintenance model is also changing. In order to extract the fault characteristics of diesel engine thermal parameters more easily, reduce the model’s complexity and improve the model’s accuracy, a new approach is proposed: first, the traditional convolutional neural networks (improved convolutional neural networks (ICNN)) are improved by using Meta-ACON as the activation function, improved AdamP as the optimizer, and label smoothing regularization (LSR) as the loss function, which enhances the stability of the model. Secondly, efficient channel attention (ECA) is added to achieve the mastery of global feature information, reduce the complexity of the traditional self-attention module, and enhance the model’s feature extraction ability. Lastly, the accuracy and reliability of the model are verified through ablation and comparison experiments. The accuracy rate reaches 97.6%, which is significantly improved by 32.1% compared with the original model, and the robustness of the model is verified through the introduction of noise. The experimental results demonstrate the applicability of the model in the field of diesel engine fault diagnosis.

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Section: Introductionmentioning

confidence: 97%