Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTM
To ensure the implementation of the marine electric propulsion self-healing strategy after faults, it is necessary to diagnose and accurately classify the faults. Considering the characteristics of the residual network (ResNet) and bidirectional long short-term memory (BiLSTM), the Res-BiLSTM deep l...
Main Authors: | Jialing Xie, Weifeng Shi, Yuqi Shi |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-08-01
|
Series: | Machines |
Subjects: | |
Online Access: | https://www.mdpi.com/2075-1702/10/9/736 |
Similar Items
-
Non-Intrusive Air Traffic Control Speech Quality Assessment with ResNet-BiLSTM
by: Yuezhou Wu, et al.
Published: (2023-09-01) -
Res-BiANet: A Hybrid Deep Learning Model for Arrhythmia Detection Based on PPG Signal
by: Yankun Wu, et al.
Published: (2024-02-01) -
3D-ResNet-BiLSTM Model: A Deep Learning Model for County-Level Soybean Yield Prediction with Time-Series Sentinel-1, Sentinel-2 Imagery, and Daymet Data
by: Mahdiyeh Fathi, et al.
Published: (2023-11-01) -
Comparison of machine learning and deep learning techniques for the prediction of air pollution: a case study from China
by: Ishan Ayus, et al.
Published: (2023-05-01) -
Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data
by: Antreas Pogiatzis, et al.
Published: (2020-12-01)