Objective: This work aims to use slow features extraction of time-varying signals
to solve the unfavorable influences of traditional principal component analysis (PCA) method
on feature extraction in Tennessee Eastman (TE) process.
Methods: Slow feature principal component analysis (SFPCA) method can obtain the slow
features information of the observed data while considering variance maximization. The
monitoring statistical indices are built on SFPCA method, and their confidence limits are
computed by kernel density estimation (KDE), respectively.
Results: All the monitoring results of SFPCA are presented. The confidence limit for fault
detection is set to 95%. The fault exists all the time from 161st sample by SFPCA method.
Stochastic occurrence appears with relatively smaller amplitude in temperature of reactant
feeding in fault 10. Monitoring chart based on SFPCA performs better with fault detection
rate for T2 index reaching 93.13% and Q index 56.50%. In Table 2, the proposed method can
detect most faults than PCA, especially for faults (4), (5), (8), (10), (11), (16), (17), (18), (19),
(20), (21). In Table 3, for fault (2), (8), (10), (11), (13), (16), (17), (19), (21), SFPCA shows
better detection performance than PCA. In fault 5, the positive step change in condenser cooling
water temperature leads to a sharp increase in its flow rate which is measured by the 52nd
Conclusion: SFPCA method demonstrates better performance than the traditional PCA method
from the perspective of both fault diagnosis rate and fault diagnosis time in TE process.