Invention Application
- Patent Title: FEDERATED LEARNING WITH SOUND TO DETECT ANOMALIES IN THE INDUSTRIAL EQUIPMENT
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Application No.: US17306004Application Date: 2021-05-03
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Publication No.: US20220351744A1Publication Date: 2022-11-03
- Inventor: Kavitha Krishnan , Nicholas John Nicoloudis , Luxi Li , Pai-Hung Chen , Anton Kroger
- Applicant: SAP SE
- Applicant Address: DE Walldorf
- Assignee: SAP SE
- Current Assignee: SAP SE
- Current Assignee Address: DE Walldorf
- Main IPC: G10L25/30
- IPC: G10L25/30 ; G06N3/04 ; G06N3/08 ; G10L25/87 ; G10L19/02

Abstract:
In some example embodiments, there may be provided a method that includes receiving a machine learning model provided by a central server configured to provide federated learning; receiving first audio data obtained from at least one audio sensor monitoring at least one machine located at the first edge node; training, based on the first audio data, the machine learning model; providing parameter information to the central server in order to enable the federated learning among a plurality of edge nodes; receiving an aggregate machine learning model provided by the central server; detecting an anomalous state of the at least one machine. Related systems, methods, and articles of manufacture are also described.
Public/Granted literature
- US11848027B2 Federated learning with sound to detect anomalies in the industrial equipment Public/Granted day:2023-12-19
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