Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes

By Zuyu Yin, Jian Hou. Neurocomputing, 2016

315 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/780ba7d2c2a04ae38f0a570b82bf85e00a50d9ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Liang Gao
2. Xinyu Li
3. Long Wen
4. Dhiraj Neupane
5. Jong-Hoon Seok

In Mechanical Engineering

1. Liang Gao
2. Xinyu Li
3. Long Wen

Citations in the corpus

• A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis (2019, background): “Yin and Hou [17] investigated the recent advantages on SVM-based fault diagnosis and process monitoring.”
• The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities (2021, unknown): “A variety of technologies used in fault diagnosis like Support Vector Machines [36], Bayesian Networks [37], Deep Learning [38]–[40], and many others [41]–[44] are capable of enhanced fault diagnosis.”