Fault diagnosis methods based on process history data have been studied widely in recent years, and several successful industrial applications have been reported. Improved data validation has resulted in more stable processes and better quality of the products. In this paper, an on-line fault detection and isolation system consisting of a combination of principal component analysis (PCA) and two neural networks (NNs), radial basis function network (RBFN) and self-organizing map (SOM), is presented. The system detects and isolates faulty operation of the analyzers in an ethylene cracking furnace. The test results with real-time process data are presented and discussed.
|Journal||Control Engineering Practice|
|Publication status||Published - 2008|
|MoE publication type||A1 Journal article-refereed|
- Ethylene cracking
- Process monitoring
- Fault detection
- Fault isolation
- Principal component analysis
- Self-organizing map
Kämpjärvi, P., Sourander, M., Komulainen, T., Nikus, M., Vatanski, N., & Jämsä-Jounela, S-L. (2008). Fault detection and isolation of an online analyzer for an ethylene cracking process. Control Engineering Practice, 16, 1-13.