Systems, methods and computer-readable media for dynamic process monitoring and/or generating a principal predictor model

A system for generating principal predictor models from multi-dimensional time series data, comprising:

• a plurality of sensors, configured to detect multi-dimensional time series data corresponding to a plurality of original variables;
• and
• a processing unit coupled to the plurality of sensors, the processing unit configured to:
• transform the multi-dimensional time series data to a lower dimension to define reduced-dimensional time series data;
• extract, by a controller, a plurality of latent variables from the reduced-dimensional time series data and determine values of the plurality of latent variables in a first time period;
• initialize, by the controller, a loadings matrix corresponding to a set of latent variables of the plurality of latent variables;
• determine, by the controller, one or more principal predictor model parameters, by performing an iterative process which comprises:
• (a) predicting values of the plurality of latent variables based on the reduced-dimensional time series data and the loadings matrix, by using an estimation process which maximizes a covariance between the values of the plurality of latent variables and the predicted values of the plurality of latent variables;
• (b) calculating a new loadings matrix from the loadings matrix and the predicted values of the latent variables, and
• (c) updating the loadings matrix based on the calculated new loadings matrix;
• and
• (d) iteratively repeating (a) to (c) until the one or more principal predictor model parameters reach convergence.

A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to:

• receive, from a plurality of sensors, multi-dimensional time series data corresponding to a plurality of original variables;
• transform the multi-dimensional time series data to a lower dimension to define reduced-dimensional time series data;
• extract a plurality of latent variables from the reduced-dimensional time series data and determine values of the plurality of latent variables in a first time period;
• initialize a loadings matrix corresponding to a set of latent variables of the plurality of latent variables;
• determine one or more principal predictor model parameters by performing an iterative process which comprises:
• (a) predicting values of the plurality of latent variables based on the reduced-dimensional time series data and the loadings matrix, by using an estimation process which maximizes a covariance between the values of the plurality of latent variables and the predicted values of the plurality of latent variables;
• (b) calculating a new loadings matrix from the loadings matrix and the predicted values of the latent variables, and
• (c) updating the loadings matrix based on the calculated new loadings matrix;
• and
• (d) iteratively repeating (a) to (c) until the one or more principal predictor model parameters reach convergence.