Abstract
A method for monitoring a nonlinear dynamic process and a computing system are provided.
The method includes training a model based on sample data, wherein the model comprises: a first layer, wherein the first layer is configured to linearize the sample data using one or more dimension lifting techniques; a second layer, wherein the second layer is configured to extract reduced-dimension dynamic latent variables (DLVs) from the linearized sample data using a reduced-dimension model; and a third layer, wherein the third layer is configured to parameterize the extracted reduced-dimension DLVs using a latent state space model; and inputting data from the nonlinear dynamic process into the trained model for monitoring the nonlinear dynamic process.
The method includes training a model based on sample data, wherein the model comprises: a first layer, wherein the first layer is configured to linearize the sample data using one or more dimension lifting techniques; a second layer, wherein the second layer is configured to extract reduced-dimension dynamic latent variables (DLVs) from the linearized sample data using a reduced-dimension model; and a third layer, wherein the third layer is configured to parameterize the extracted reduced-dimension DLVs using a latent state space model; and inputting data from the nonlinear dynamic process into the trained model for monitoring the nonlinear dynamic process.
| Original language | English |
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| Filing date | 28/01/25 |
| Publication status | Accepted/In press - 28 Jan 2025 |