Neural Component Analysis for Key Performance Indicator Monitoring.
ACS Omega
; 7(42): 37248-37255, 2022 Oct 25.
Article
in En
| MEDLINE
| ID: mdl-36312330
ABSTRACT
The partial least squares (PLS) algorithm is a commonly used key performance indicator (KPI)-related performance monitoring method. To address nonlinear features in the process, this paper proposes neural component analysis (NCA)-PLS, which combines PLS with NCA. (NCA)-PLS realizes all the principles of PLS by introducing a new loss function and a new principal component selection mechanism to NCA. Then, the gradient descent formulas for network training are rederived. NCA-PLS can extract components with large correlations with KPI variables and adopt them for data reconstruction. Simulation tests using a mathematical model and the Tennessee Eastman process show that NCA-PLS can successfully handle nonlinear relationships in process data and that it performs much better than PLS, KPLS, and NCA.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Prognostic_studies
Language:
En
Journal:
ACS Omega
Year:
2022
Document type:
Article
Affiliation country:
China