Monitoring dynamic process with orthonormal subspace analysis

Abstract

Traditional multivariate statistics-based process monitoring (MSPM) methods are static algorithms, and the “time lag shift” method (TLSM) is the most commonly used approach to handle the dynamic issue. This paper proves in theory that two drawbacks exist in TLSM-based dynamic approaches: information unrelated to the real-time data is also analyzed, and information that can be predicted by historical data is counted repeatedly in both real-time and historical data. This paper adopts orthonormal subspace analysis (OSA) to handle these issues. OSA can successfully separate real-time data into information that can be predicted by historical data (the dynamic component) and cannot be predicted for process monitoring (the static component), so the detection result is not influenced by redundant information and is more sensitive to process faults than TLSM-based dynamic methods.