| Title | Multiple Fault Diagnosis Methods Based on Multilevel Multi-Granularity PCA |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Wu, Lan, Su, Sheyan, Wen, Chenglin |
| Conference Name | 2018 International Conference on Control, Automation and Information Sciences (ICCAIS) |
| Keywords | Correlation, Covariance matrices, cyber physical systems, Data models, Data processing, fault detection, fault diagnosis, human factors, linear correlation, Metrics, multi-granularity, multi-level, multilevel multigranularity PCA, multiple fault diagnosis, multiple fault diagnosis methods, multiple process variables, multivariate statistical analysis, principal component analysis, process fault diagnosis, process monitoring, production engineering computing, pubcrawl, Resiliency, traditional PCA fault diagnosis |
| Abstract | Principal Component Analysis (PCA) is a basic method of fault diagnosis based on multivariate statistical analysis. It utilizes the linear correlation between multiple process variables to implement process fault diagnosis and has been widely used. Traditional PCA fault diagnosis ignores the impact of faults with different magnitudes on detection accuracy. Based on a variety of data processing methods, this paper proposes a multi-level and multi-granularity principal component analysis method to make the detection results more accurate. |
| DOI | 10.1109/ICCAIS.2018.8570453 |
| Citation Key | wu_multiple_2018 |
Multiple Fault Diagnosis Methods Based on Multilevel Multi-Granularity PCA