Abstract:
In this paper we exploit the use of the proposed RKPCA method ([1], [2], [3]) for sensor fault detection, localisation and reconstruction. To this end, a set of structure...Show MoreMetadata
Abstract:
In this paper we exploit the use of the proposed RKPCA method ([1], [2], [3]) for sensor fault detection, localisation and reconstruction. To this end, a set of structured residues is generated by using partial RKPCA technique. Also to identify fault, the Reconstruction Based Contribution RBC approach [4] was used. The relevance of the evaluated techniques partial RKPCA and RBC is revealed on Continuous Stirred Tank Reactor (CSTR).
Date of Conference: 19-21 January 2017
Date Added to IEEE Xplore: 23 October 2017
ISBN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Nonlinear Systems ,
- Continuous Stirred Tank Reactor ,
- Training Data ,
- Confidence Level ,
- Eigenvalues ,
- Eigenvectors ,
- Partial Model ,
- Feature Space ,
- Radial Basis Function ,
- Null Space ,
- Incidence Matrix ,
- Observation Vector ,
- Square Error Of Prediction ,
- Fault Isolation ,
- Kernel Principal Component Analysis ,
- Experimental Signatures ,
- Kernel Principal Component
- Author Keywords
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Nonlinear Systems ,
- Continuous Stirred Tank Reactor ,
- Training Data ,
- Confidence Level ,
- Eigenvalues ,
- Eigenvectors ,
- Partial Model ,
- Feature Space ,
- Radial Basis Function ,
- Null Space ,
- Incidence Matrix ,
- Observation Vector ,
- Square Error Of Prediction ,
- Fault Isolation ,
- Kernel Principal Component Analysis ,
- Experimental Signatures ,
- Kernel Principal Component
- Author Keywords
