Research on Fault Feature Extraction of Diesel Engine Cylinder Based on MMD and KICA

Wang, Min; Guo, J.; Peng, Qiong; Cao, Shen

doi:10.1109/imasbd57215.2022.00014

Cited by 1 publication

(1 citation statement)

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“…Therefore, monitoring the vibration signals of the cylinder head shell of the internal combustion engine can obtain quasi-periodic multi-impact coupling signals in the time domain, thus reflecting the working status of different components. There are many studies extracting fault characteristics from the vibration signals of the cylinder head shell of internal combustion engines and constructing diagnostic models [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Transfer Diagnosis Model of Internal Combustion Engine With Embedded Vibration Signal Impact Decomposition

Li,

Dong,

Peng

et al. 2024

IEEE Access

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Traditional transfer diagnosis models for internal combustion engines show a decrease in generalization ability due to the multisource features aliasing in vibration signals and the effect of variable operating conditions. To address this problem, this paper proposes a transfer diagnosis model based on the deep subdomain adaptive network framework. To address feature aliasing, based on minimizing amplitude moment and reconstruction loss, a new adaptive decomposition layer is designed and embedded into the framework to decompose complex signals into single-impact components in time domain. To alleviate the effect of operating conditions, a new constraint for minimizing signal feature variance loss is designed and introduced into the framework's loss function. This constraint calculates the variance of the sample features of the same fault label under variable operating conditions, aiming to excavate invariant features of operating conditions and complete feature mapping of domain adaptation. Validation with experimental data yields an accuracy of 94.81%.

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Section: Introductionmentioning

confidence: 99%