Deep Q‐learning recommender algorithm with update policy for a real steam turbine system
Abstract In modern industrial systems, diagnosing faults in time and using the best methods becomes increasingly crucial. It is possible to fail a system or to waste resources if faults are not detected or are detected late. Machine learning and deep learning (DL) have proposed various methods for d...
Main Authors: | Mohammad Hossein Modirrousta, Mahdi Aliyari Shoorehdeli, Mostafa Yari, Arash Ghahremani |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2023-09-01
|
Series: | IET Collaborative Intelligent Manufacturing |
Subjects: | |
Online Access: | https://doi.org/10.1049/cim2.12081 |
Similar Items
-
PT-Informer: A Deep Learning Framework for Nuclear Steam Turbine Fault Diagnosis and Prediction
by: Jiajing Zhou, et al.
Published: (2023-08-01) -
An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis
by: Denis Leite, et al.
Published: (2022-08-01) -
A Review of Fault Diagnosis, Status Prediction, and Evaluation Technology for Wind Turbines
by: Fanghong Zhang, et al.
Published: (2023-01-01) -
A Novel Intelligent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost
by: Zhiguo Liang, et al.
Published: (2023-02-01) -
Real-Time Motor Fault Diagnosis Based on TCN and Attention
by: Hui Zhang, et al.
Published: (2022-03-01)