Fault diagnosis based on Fisher discriminant analysis and support vector machines

By Leo H. Chiang, M. Kotanchek, A. Kordon. Computers and Chemical Engineering, 2004

516 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/7fde7be3ac9c1015aace38c553dd67a8f8e5086a

How researchers see this paper

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

In Analytical Chemistry

1. Haitao Zhao
2. Bo Jin
3. Shaoyuan Sun

In Control and Systems Engineering

1. Haitao Zhao
2. Bo Jin
3. Shaoyuan Sun

In Mechanical Engineering

1. Haitao Zhao
2. Bo Jin
3. Shaoyuan Sun

Citations in the corpus

• A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm (2017, methodology): “Chiang et al. (2004) proposed a fault diagnosis method based on Fisher discriminant analysis and support vector machines.”
• Data Mining and Analytics in the Process Industry: The Role of Machine Learning (2017, unknown): “[162]”
• Sequential Fault Diagnosis Based on LSTM Neural Network (2018, background): “, the diagnosis results of DLDA + SVM are largely affected by the dimension of the reduced features.”