A Novel Dynamic Process Monitoring Algorithm: Dynamic Orthonormal Subspace Analysis

Orthonormal subspace analysis (OSA) is proposed for handling the decomposition issue and principal component selection in traditional key performance indicator (KPI)-related process monitoring methods such as partial least squares (PLS) canonical correlation (CCA). However, it not appropriate to apply static OSA algorithm a dynamic since pays no attention auto-correlation relationships variables. Therefore, novel (DOSA) capture auto-correlative behavior of variables on basis KPIs accurately. This study also discusses whether necessary expand dimension both matrix KPI DOSA. The test results mathematical model Tennessee Eastman (TE) show that DOSA can address retain advantages OSA.