Multimodal Decoupled Representation With Compatibility Learning for Explicit Nonstationary Process Monitoring

Frequent switching of operating conditions in industrial processes tends to make the data distribution time varying and variable correlations nonuniform, which brings considerable challenges for explicit representation and monitoring of nonstationary processes. This study addresses the abovementioned problem based on the following recognitions: 1) despite changes in operating conditions, there exist time-invariant process mechanisms, which are nonlinearly coupled with nonstationarity; 2) such a coupling relationship is not uniform but may show diverse modes changing with time, defined as multimodal coupling here; 3) diverse coupling relations can be derived from the superposition of nonstationarity induced by definite driving forces, i.e., changeable operating condition settings, reflecting their intrinsic association under complex changes. Thereupon, a cascaded deep information separation (CDIS) architecture with a compatibility learning algorithm is proposed to extract multimodal decoupled representations. We design an information refinement module (IRM) to capture the basic coupling source (BCS) under the influence of driving forces, where a shortcut connection is incorporated into the nonlinear autoencoding structure. Multiple IRMs can be flexibly cascaded to achieve the superposition of BCSs, thus, portraying diverse multimodal couplings. Furthermore, by balancing the stationarity requirement and reconstruction constraint, the designed compatibility learning algorithm induces the cascaded IRMs to capture and filter out nonstationarity, thus, obtaining stationary refined data. In this way, stationarity and nonstationarity with multimodal couplings can be fully separated. The validity of CDIS is illustrated through a simulated example and a real condensing system experimental rig.