公开(公告)号：US20240193483A1

公开(公告)日：2024-06-13

申请号：US18536017

申请日：2023-12-11

Applicant: 3M INNOVATIVE PROPERTIES COMPANY

Inventor： Nitsan Ben-Gal Nguyen ,  Haleh Hagh-Shenas ,  Karthik Subramanian ,  Jesse T. Pikturna ,  Janna M. Keeler ,  Elizabeth A. Oliver

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Method for detecting anomalous data in a manufacturing line or live sensing application. The method includes computing a projection of new incoming data on a trained model and identifying potential anomalies by comparing a window for the incoming data to normal representation criteria based upon user-specified thresholds. The trained model is created by applying hoteling T2 statistics and Q-residual to clean up outliers from an historic time interval of data and calculating principal components of the data and choosing a subset of components which represent a variability in the data. A model deployment pipeline is generated from the trained model and which is capable of deploying machine learning or statistical models to an edge and cloud infrastructure associated with the manufacturing line or live sensing application.

公开(公告)号：US20230359933A1

公开(公告)日：2023-11-09

申请号：US18141628

申请日：2023-05-01

Applicant: 3M INNOVATIVE PROPERTIES COMPANY

Inventor： Karthik Subramanian ,  Anish R. Kunduru ,  Nicholas John Blum

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A method includes obtaining annotated seed data comprising one or more tags associated with corresponding timing data and a respective label, training a semi supervised learning algorithm (SSLA) using the annotated seed data to form a trained SSL model, executing the trained SSL model using unlabeled time series process data as an input, wherein the unlabeled time series process data includes tags different from the tags of the annotated seed data to output a pre-validation labeled time series process dataset, obtaining output evaluation data associated with the pre-validation labeled time series process dataset, iteratively retraining the trained SSL model using the output evaluation data, determining that the trained SSL model has reached convergence based on the output evaluation data indicating that the trained SSL model outputs validated labeled time series data, and in response to determining that the trained SSL model has reached convergence, deploying the trained SSL model.