1 research outputs found
Hidden Markov Model-Based Statistics Pattern Analysis for Multimode Process Monitoring: An Index-Switching Scheme
Multiple
operating modes pose a challenge for process monitoring
in industry. Although many monitoring approaches have achieved quite
success, most of them neglected the dependency of sampled data and
only dealt with samples in a separate fashion. This paper proposes
a sequential framework for multimode process monitoring with hidden
Markov model-based statistics pattern analysis (HMM-SPA). To begin
with, a hidden Markov model is trained on the basis of the historical
data. Statistics pattern analysis mixture models (SPAMM) are constructed
to characterize the distinctive statistical pattern of each operating
mode. Then, during online monitoring period, the mode vector is obtained
using the Viterbi algorithm, and the differential mode vector is calculated.
At last, the proposed method switches to an appropriate monitoring
index automatically, according to the norm of the differential mode
vector. The effectiveness of the proposed method is demonstrated by
a numerical simulation, a continuous stirred tank heater (CSTH) process,
and the Tennessee Eastman process