Bibliographic Details
| Title: |
Transfer Diagnosis Model of Internal Combustion Engine With Embedded Vibration Signal Impact Decomposition |
| Authors: |
He Li, Liangyu Dong, Yang Peng, Zhixiang Dai, Yang Jiang, Jinjie Zhang |
| Source: |
IEEE Access, Vol 12, Pp 62779-62792 (2024) |
| Publisher Information: |
IEEE, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
Artificial neural networks, deep learning, diesel engines, fault detection, feature extraction, internal combustion engines, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: |
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 frameworkâs 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%. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/10506935/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3392768 |
| Access URL: |
https://doaj.org/article/f74972fb817e4c358b65a9ab8e6a5284 |
| Accession Number: |
edsdoj.f74972fb817e4c358b65a9ab8e6a5284 |
| Database: |
Directory of Open Access Journals |
