公开(公告)号：US20240210935A1

公开(公告)日：2024-06-27

申请号：US18087633

申请日：2022-12-22

Applicant: Delaware Capital Formation, Inc.

Inventor： Bodhayan Dev ,  Prem Swaroop ,  Richard Buteau ,  Girish Juneja ,  Sreedhar Patnala

IPC: G05B23/02

CPC classification number: G05B23/0283 ,  G05B23/0216 ,  G05B23/024

Abstract: Among other things, systems and techniques are described for a predictive model for determining overall equipment effectiveness (OEE) in industrial equipment. Data including spectral features is obtained. A probability of survival is determined by fitting at least one degradation function to degradation data associated with the industrial equipment. An overall equipment effectiveness metric is predicted as a product of predicted planned production time, predicted performance, and predicted quality output by trained machine learning models.

公开(公告)号：US20240211798A1

公开(公告)日：2024-06-27

申请号：US18087630

申请日：2022-12-22

Applicant: Delaware Capital Formation, Inc.

Inventor： Prem Swaroop ,  Bodhayan Dev ,  Sreedhar Patnala ,  Girish Juneja

IPC: G06N20/00 ,  G06F11/34

CPC classification number: G06N20/00 ,  G06F11/3495

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer for an industrial machine-learning operation model monitoring system, that include the actions of receiving monitoring data for an industrial machine-learning operations model, determining, from the monitoring data, to retrain the industrial machine-learning operations model, where the determining includes computing drift parameters, each of the drift parameters being indicative of a type of observable drift of the industrial machine-learning operations model, where the drift parameters include (i) a usage drift, (ii) a performance drift, (iii) a data drift, and (iv) a prediction drift, and where each drift parameter includes a respective retraining criteria, and confirming, from the drift parameters, the respective retraining criteria is met by at least one of the drift parameters, and triggering, in response to the determining to retrain the industrial machine-learning operations model, an update of the industrial machine-learning operations model.

公开(公告)号：US20240193615A1

公开(公告)日：2024-06-13

申请号：US18287624

申请日：2022-04-20

Applicant: Delaware Capital Formation, Inc.

Inventor： Prem Swaroop ,  Bodhayan Dev ,  Atish P. Kamble ,  Girish Juneja ,  Jonah Somers

IPC: G06Q30/012

CPC classification number: G06Q30/012

Abstract: Among other things, techniques are described for an after-market service process digitization. Service data is obtained that is associated with at least one asset and comprises at least historical warranty data and current IoT data. Predictive analysis to generate an asset survival prediction is performed based on current data associated with a first asset and the service data. Troubleshooting data associated with the first asset from at least one knowledge data source is received. A warranty coverage metric is determined based on the asset survival prediction and the troubleshooting data, wherein the warranty coverage metric is calculated in real time according to the asset survival prediction and the troubleshooting data. The warranty coverage metric is transformed at a device into human-readable form.