Bin Lan Lu

7777516, Aug 17, 2010

Jun 3, 2008

12/132056

Wei Zhou - Los Angeles CA, US
Bin Lu - Kenosha WI, US
Thomas G. Habetler - Snellville GA, US
Ronald G. Harley - Lawrenceville GA, US
Peter J. Theisen - West Bend WI, US

Eaton Corporation - Cleveland OH

G01R 31/34

324772, 3241581

A system and method for detecting incipient mechanical motor faults by way of current noise cancellation is disclosed. The system includes a controller configured to detect indicia of incipient mechanical motor faults. The controller further includes a processor programmed to receive a baseline set of current data from an operating motor and define a noise component in the baseline set of current data. The processor is also programmed to repeatedly receive real-time operating current data from the operating motor and remove the noise component from the operating current data in real-time to isolate any fault components present in the operating current data. The processor is then programmed to generate a fault index for the operating current data based on any isolated fault components.

7847580, Dec 7, 2010

Jan 8, 2009

12/350287

Wei Zhou - Los Angeles CA, US
Bin Lu - Kenosha WI, US
Michael P. Nowak - Menomonee Falls WI, US
Steven A. Dimino - Wauwatosa WI, US

Eaton Corporation - Cleveland OH

G01R 31/34

324772, 3241581

A system and method for detecting incipient mechanical motor faults by way of current noise cancellation is disclosed. The system includes a controller configured to detect indicia of incipient mechanical motor faults. The controller further includes a processor programmed to receive a baseline set of current data from an operating motor and define a noise component in the baseline set of current data. The processor is also programmed to acquire at least on additional set of real-time operating current data from the motor during operation, redefine the noise component present in each additional set of real-time operating current data, and remove the noise component from the operating current data in real-time to isolate any fault components present in the operating current data. The processor is then programmed to generate a fault index for the operating current data based on any isolated fault components.

20220270353, Aug 25, 2022

May 9, 2022

17/740211

- Grand Cayman, KY
Jieneng Chen - Beijing, CN
Shuyang Sun - Beijing, CN
Ju He - Beijing, CN
Bin Lu - Los Angeles CA, US

G06V 10/77
G06V 10/774
G06V 10/48
G06V 10/26
G06N 20/00

Implementations of the present disclosure relate to methods, devices, and computer program products for data augmentation. In the method, mixed data is generated from first data and second data, and the mixed data comprises a first portion from the first data and a second portion from the second data. An attention map is obtained for the mixed data based on distributions of the first and second portions in the mixed data, here the attention map describes contributions of the first and second data to the mixed data. A label is determined for the mixed data based on the attention map and a first label for the first data and a second label for the second data. With these implementations, the label is determined based on the contributions of the first and second images in an accurate and effective way, and thus has a value that is much closer to the ground true.