scholarly journals Process Monitoring and Fault Diagnosis for Shell Rolling Production of Seamless Tube

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Dong Xiao ◽  
Xuyang Gao ◽  
Jichun Wang ◽  
Yachun Mao
Keyword(s):  
Fault Diagnosis ◽  
Production Process ◽  
Process Monitoring ◽  
High Reliability ◽  
Strong Nonlinearity ◽  
Seamless Tube ◽  
Continuous Rolling ◽  
Model Based ◽  
Rolling Production ◽  
Diagnosis Model

Continuous rolling production process of seamless tube has many characteristics, including multiperiod and strong nonlinearity, and quickly changing dynamic characteristics. It is difficult to build its mechanism model. In this paper we divide production data into several subperiods byK-means clustering algorithm combined with production process; then we establish a continuous rolling production monitoring and fault diagnosis model based on multistage MPCA method. Simulation experiments show that the rolling production process monitoring and fault diagnosis model based on multistage MPCA method is effective, and it has a good real-time performance, high reliability, and precision.

scholarly journals Optimization of a Dynamic Fault Diagnosis Model Based on Machine Learning

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 65065-65077 ◽  
Author(s):  
Shigang Zhang ◽  
Xu Luo ◽  
Yongmin Yang ◽  
Long Wang ◽  
Xiaofei Zhang
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Model Based ◽  
Diagnosis Model ◽  
Dynamic Fault Diagnosis

scholarly journals Photovoltaic fault diagnosis model based on dynamic switching

2021 ◽  
Vol 2005 (1) ◽  
pp. 012150
Author(s):  
Nanzhou Chen ◽  
Shan Hu ◽  
Wenhao Zhu ◽  
Fei Wang
Keyword(s):  
Fault Diagnosis ◽  
Dynamic Switching ◽  
Model Based ◽  
Diagnosis Model

A Bearing Fault Diagnosis Model Based on Deformable Atrous Convolution and Squeeze-and-Excitation Aggregation

2021 ◽  
Vol 70 ◽  
pp. 1-10
Author(s):  
Yang Wang ◽  
Miaomiao Yang ◽  
Yupeng Zhang ◽  
Zeda Xu ◽  
Jigang Huang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Model Based ◽  
Diagnosis Model

scholarly journals A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 960 ◽  
Author(s):  
Fang Yuan ◽  
Jiang Guo ◽  
Zhihuai Xiao ◽  
Bing Zeng ◽  
Wenqiang Zhu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Chemical Reaction ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Chemical Reaction Optimization ◽  
Model Based ◽  
Reaction Optimization ◽  
Diagnosis Model ◽  
Transformer Fault

The condition monitoring and fault diagnosis of power transformers plays a significant role in the safe, stable and reliable operation of the whole power system. Dissolved gas analysis (DGA) methods are widely used for fault diagnosis, however, their accuracy is limited by the selection of DGA features and the performance of fault diagnosis models, for example, the classical support vector machine (SVM), is easily affected by unbalanced training samples. This paper presents a transformer fault diagnosis model based on chemical reaction optimization and a twin support vector machine. Twin support vector machines (TWSVMs) are used as classifiers for solving problems involving unbalanced and insufficient samples. Restricted Boltzmann machines (RBMs) are used for data preprocessing to ensure the effective identification of feature parameters and improve the efficiency and accuracy of fault diagnosis. The chemical reaction optimization (CRO) algorithm is used to optimize TWSVM parameters to select the optimal training parameters. The cross-validation (CV) method is used to ensure the reliability and generalization ability of the diagnostic model. Finally, the validity of the model is verified using real fault samples and random testing.

Sign in / Sign up

Export Citation Format

Share Document