A nonlinear quality-related fault detection approach based on modified kernel partial least squares
Jianfang Jiao1, Ning Zhao2, Guang Wang1
1Bohai University, Jinzhou 121013, China.
ISA Transactions
|November 8, 2016
Summary
A new nonlinear quality-related fault detection method uses kernel partial least squares (KPLS) to analyze process variables. This approach offers simple diagnosis logic and stable performance for improved fault detection in industrial settings.
Area of Science:
- Chemical Engineering
- Process Control
- Data Analytics
Background:
- Industrial processes often exhibit complex nonlinear relationships between variables.
- Accurate fault detection is crucial for maintaining product quality and operational efficiency.
- Existing nonlinear methods may lack simplicity or robust performance.
Purpose of the Study:
- To propose a novel nonlinear quality-related fault detection method.
- To address the challenges posed by nonlinear characteristics in process variables.
- To enhance the simplicity and stability of fault detection systems.
Main Methods:
- Kernel Partial Least Squares (KPLS) model to map variables into a feature space.
- Decomposition of the kernel matrix using Singular Value Decomposition (SVD).
- Determination of statistics from orthogonal parts for fault detection.
Main Results:
- The KPLS-based method effectively handles nonlinear process variables.
- Singular Value Decomposition aids in robust fault detection.
- The proposed method demonstrates simpler diagnostic logic compared to existing nonlinear approaches.
Conclusions:
- The developed KPLS method provides a stable and effective solution for nonlinear quality-related fault detection.
- The approach is validated through a literature example and an industrial process.
- This technique offers practical advantages for real-world process monitoring.
Keywords:
Data-drivenFault detectionKernel partial least squaresNonlinear monitoringQuality-relatedSingular Value DecompositionMore Related Videos
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