Systeme, Verfahren und Computerlesbare Medien zur Dynamischen Prozessüberwachung Und/oder Erzeugung eines Hauptprädiktormodells

Details

Veröffentlichungs-Nr.

EP4718290

Anmeldetag

18. September 2025

Veröffentlichung

1. April 2026

Rechtsraum

EP

IPC

G06F17/18, G06F30/00, G06N3/00

Abstract

A method for generating a principal predictor model from multi-dimensional time series data, comprises: receiving, from a plurality of sensors, multi-dimensional time series data corresponding to a plurality of original variables; transforming the multi-dimensional time series data to a lower dimension to define reduced-dimensional time series data; extracting, by a controller, a plurality of latent variables from the reduced-dimensional time series data and determining values of the plurality of latent variables in a first time period; initializing, by the controller, a loadings matrix corresponding to a set of latent variables of the plurality of latent variables; and determining, by the controller, one or more principal predictor model parameters, by performing an iterative process. The iterative process 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; (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.

Anmelder

Firma

Lingnan University

Land

🇭🇰 Hongkong

Fachgebiete