# US 12,763,569 B2 · Thermal and vibration smart monitoring and outage prevention > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,763,569 B2 - Document ID: US12763569B2 - Status: Granted - Kind: B2 - Application number: 18/665,216 - Filed: 2024-05-15 - Published: 2025-11-20 - Granted: 2026-10-13 - Earliest claimed: 2024-05-15 - Assignees: Schneider Electric USA, Inc. - Applicants: Schneider Electric USA, Inc. - Inventors: Bruno Colin; Eduardo Barba Jimenez; Gautham Nagendra - Web page: https://patentgrep.com/patent/US12763569B2 ## CPC - H02H 6/00: Emergency protective circuit arrangements responsive to undesired changes from normal non-electric working conditions using simulators of the apparatus being protected, e.g. using thermal images - G08B 21/185: {Electrical failure alarms} - G08B 31/00: Predictive alarm systems characterised by extrapolation or other computation using updated historic data - H02H 5/04: responsive to abnormal temperature {(specially adapted for electric machines H02H7/0852)} - H02H: EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS (indicating or signalling undesired working conditions G01R, e.g. G01R31/00, G08B; locating faults along lines G01R31/08; emergency protective devices H01H) - G08B: SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS ## Abstract Thermal and vibration smart monitoring system for electrical distribution equipment. A plurality of sensors provide temperature and vibration data associated with busbars or busways of the electrical distribution equipment. A diagnostics processor processes the sensor data and load data, which is representative of an electrical load of the electrical distribution equipment, as inputs to a trained machine learning model to determine a prediction whether a predefined temperature alarm for the electrical distribution equipment will be exceeded at a subsequent moment in time and to generate an electronic or visual notification based on the prediction. ## Claims ### Claim 1 A method of thermal and vibration smart monitoring of electrical distribution equipment, the electrical distribution equipment including one or more rigid electric load carrying components, the method comprising: receiving sensor data from a plurality of sensors, wherein the sensor data comprises temperature data associated with the one or more rigid electric load carrying components and vibration data indicating a self-induced vibration of the one or more rigid electric load carrying components; receiving load data representative of an electrical load of the electrical distribution equipment; processing the sensor data and the load data as inputs to a trained machine learning model to determine a prediction whether a predefined temperature alarm for the electrical distribution equipment will be exceeded at a subsequent moment in time; and generating a notification based on the prediction. ### Claim 2 (depends on 1) The method of claim 1, wherein the electrical distribution equipment includes one or more circuit breakers and wherein the sensor data further comprises temperature data associated with the one or more circuit breakers. ### Claim 3 (depends on 2) The method of claim 2, wherein the sensor data further comprises vibration data associated with the one or more circuit breakers. ### Claim 4 (depends on 1) The method of claim 1, wherein the sensor data further comprises environmental data associated with ambient conditions of the electrical distribution equipment, and wherein processing the sensor data and the load data comprises modeling temperature and vibration of the electrical distribution equipment as a function of the ambient conditions and the electrical load. ### Claim 5 (depends on 4) The method of claim 4, wherein modeling the temperature and vibration of the electrical distribution equipment comprises defining, based on the received sensor data and load data, a predicted temperature and vibration response of the electrical distribution equipment over time in response to the ambient conditions and the electrical load. ### Claim 6 (depends on 5) The method of claim 5, further comprising defining a normality space associated with the electrical distribution equipment from the predicted temperature and vibration response, and wherein processing the sensor data and the load data comprises comparing the normality space to a temperature threshold corresponding to the predefined temperature alarm. ### Claim 7 A thermal and vibration smart monitoring system for electrical distribution equipment, the electrical distribution equipment including one or more rigid electric load carrying components, the system comprising: a plurality of sensors configured to provide sensor data, the sensor data comprising temperature data associated with the one or more rigid electric load carrying components and vibration data indicating a self-induced vibration of the one or more rigid electric load carrying components; a diagnostics processor receiving and responsive to the sensor data and to load data, the load data representative of an electrical load of the electrical distribution equipment; and a memory coupled to the diagnostics processor, the memory storing processor-executable instructions that, when executed, configure the diagnostics processor for: processing the sensor data and the load data as inputs to a trained machine learning model to determine a prediction whether a predefined temperature alarm for the electrical distribution equipment will be exceeded at a subsequent moment in time; and generating a notification based on the prediction. ### Claim 8 (depends on 7) The smart monitoring system of claim 7, further comprising one or more circuit breakers electrically connected to the electrical distribution equipment, and wherein the sensor data further comprises temperature data associated with the one or more circuit breakers. ### Claim 9 (depends on 8) The smart monitoring system of claim 8, wherein the sensor data further comprises vibration data associated with the one or more circuit breakers. ### Claim 10 (depends on 7) The smart monitoring system of claim 7, wherein the sensor data further comprises environmental data associated with ambient conditions of the electrical distribution equipment, and wherein the processor-executable instructions, when executed, further configure the diagnostics processor for generating a temperature and vibration model of the electrical distribution equipment as a function of the ambient conditions and the electrical load. ### Claim 11 (depends on 10) The smart monitoring system of claim 10, wherein the temperature and vibration model of the electrical distribution equipment defines, based on the sensor data and the load data, a predicted temperature and vibration response of the electrical distribution equipment over time in response to the ambient conditions and the electrical load. ### Claim 12 (depends on 11) The smart monitoring system of claim 11, wherein the predicted temperature and vibration response defines a normality space associated with the electrical distribution equipment, and wherein the processor-executable instructions, when executed, further configure the diagnostics processor for comparing the normality space to a temperature threshold corresponding to the predefined temperature alarm. ### Claim 13 An electrical distribution system comprising: one or more rigid electric load carrying components configured for supplying power to an electrical load; a plurality of sensors configured to provide sensor data, the sensor data comprising temperature data associated with the one or more rigid electric load carrying components and vibration data indicating a self-induced vibration of the one or more rigid electric load carrying components; a diagnostics processor receiving and responsive to the sensor data and to load data, the load data representative of the electrical load; and a memory coupled to the diagnostics processor, the memory storing a machine learned model that, when executed by the diagnostics processor: processes the sensor data and the load data as inputs to the machine learned model to determine a prediction whether a predefined temperature alarm for the electrical distribution system will be exceeded at a subsequent moment in time; and causes a notification to be generated based on the prediction. ### Claim 14 (depends on 13) The electrical distribution system of claim 13, wherein at least one of the plurality of sensors is located at a joint between the rigid electric load carrying components and the sensor data further comprises vibration data indicating a self-induced vibration of the joint. ### Claim 15 (depends on 13) The electrical distribution system of claim 13, further comprising a gateway of a wireless communications network, wherein the diagnostics processor receives the sensor data from the sensors wirelessly via the gateway. ### Claim 16 (depends on 15) The electrical distribution system of claim 15, wherein the diagnostic processor is accessible through a cloud connection, linked to a separated offer, or available as a single service. ### Claim 17 (depends on 13) The electrical distribution system of claim 13, wherein the sensor data further comprises environmental data associated with ambient conditions of the electrical distribution system, and wherein the machine learned model comprises a predicted temperature and vibration response over time in response to the ambient conditions and the electrical load. ### Claim 18 (depends on 17) The electrical distribution system of claim 17, wherein the predicted temperature and vibration response defines a normality space associated with the electrical distribution system, and wherein the predefined temperature alarm is based on comparing the normality space to a temperature threshold. ### Claim 19 (depends on 18) The electrical distribution system of claim 18, wherein a specific pattern of temperature or vibration evolution is used to complete the predefined alarm thresholds.