Process Monitoring Based on Orthogonal Locality Preserving Projection with Maximum Likelihood Estimation
| Autor: | Maoyin Chen, Xia Hong, Hao Chen, Zhang Jingxin, Donghua Zhou |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science General Chemical Engineering Maximum likelihood Density reduction Locality Process (computing) 02 engineering and technology General Chemistry 021001 nanoscience & nanotechnology Dimensionality estimation Industrial and Manufacturing Engineering Machine Learning (cs.LG) Methodology (stat.ME) 020401 chemical engineering 0204 chemical engineering 0210 nano-technology Projection (set theory) Algorithm Statistics - Methodology |
| Zdroj: | Industrial & Engineering Chemistry Research. 58:5579-5587 |
| ISSN: | 1520-5045 0888-5885 |
| DOI: | 10.1021/acs.iecr.8b05875 |
| Popis: | By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, the MLE is adopted to estimate intrinsic dimensionality of OLPP. Within the proposed OLPP-MLE, two new static measures for fault detection $T_{\scriptscriptstyle {OLPP}}^2$ and ${\rm SPE}_{\scriptscriptstyle {OLPP}}$ are defined. In order to reduce algorithm complexity and ignore data distribution, kernel density estimation is employed to compute thresholds for fault diagnosis. The effectiveness of the proposed method is demonstrated by three case studies. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|