SYSTEM AND METHODS FOR MACHINE ANOMALY DETECTION BASED ON SOUND SPECTROGRAM IMAGES AND NEURAL NETWORKS

    公开(公告)号:US20230078351A1

    公开(公告)日:2023-03-16

    申请号:US17948680

    申请日:2022-09-20

    Abstract: A system and methods for machine anomaly and behavior classification is provided. An audio capture device may attach to a mechanical apparatus comprising a first component and a second component. The first component and second component may separately generate audible noise. The audio capture device may generate a signal in response audio caused by the vibrating diagram. The system may receive the signal generated by the microphone of the audio capture device. The system may determine, based on a machine learning model and the signal, an anomalous event associated with the first component, a second component, or a combination thereof. Alternatively, or in addition, the system may classify operation of the machine based on second machine learning model.

    System and methods for machine anomaly detection based on sound spectrogram images and neural networks

    公开(公告)号:US11475910B2

    公开(公告)日:2022-10-18

    申请号:US17173946

    申请日:2021-02-11

    Abstract: A system and methods for machine anomaly and behavior classification is provided. An audio capture device may attach to a mechanical apparatus comprising a first component and a second component. The first component and second component may separately generate audible noise. The capture device may include a body, a diagraph disposed in the body, and a microphone. The diagraph may vibrate in response to sound generated by the mechanical device. The microphone may generate a signal in response audio caused by the vibrating diagram. The system may receive the signal generated by the microphone of the audio capture device. The system may determine, based on a machine learning model and the signal, an anomalous event associated with the first component, a second component, or a combination thereof. Alternatively, or in addition, the system may classify operation of the machine based on second machine learning model.

    ELECTRONIC TEXTILES AND METHODS FOR FABRICATION THEREOF

    公开(公告)号:US20230136666A1

    公开(公告)日:2023-05-04

    申请号:US17823021

    申请日:2022-08-29

    Abstract: Electronic textiles and methods of fabrication electronic textiles. Nanoparticles of a conductive material are sprayed along a conductive path into a fabric material so as to penetrate into the fabric. A layer of a second conductor material is coated over the nanoparticles along the conductive path. A layer of an insulator material is coated over the layer of the second conductor material so as to encapsulate the conductive path and form a trace. An electrode configured to contact a subject wearing the fabric material includes a layer of a third conductor material coated over the layer of the second conductor and electrically coupled with the conductive path. An electrical connector is secured to the fabric material and electrically coupled with the conductive path. The nanoparticles are sprayed onto the fabric material using a dual regime spray process implemented with a dual regime spray system.

    SYSTEM AND METHODS FOR MACHINE ANOMALY DETECTION BASED ON SOUND SPECTROGRAM IMAGES AND NEURAL NETWORKS

    公开(公告)号:US20210256991A1

    公开(公告)日:2021-08-19

    申请号:US17173946

    申请日:2021-02-11

    Abstract: A system and methods for machine anomaly and behavior classification is provided. An audio capture device may attach to a mechanical apparatus comprising a first component and a second component. The first component and second component may separately generate audible noise. The capture device may include a body, a diagraph disposed in the body, and a microphone. The diagraph may vibrate in response to sound generated by the mechanical device. The microphone may generate a signal in response audio caused by the vibrating diagram. The system may receive the signal generated by the microphone of the audio capture device. The system may determine, based on a machine learning model and the signal, an anomalous event associated with the first component, a second component, or a combination thereof. Alternatively, or in addition, the system may classify operation of the machine based on second machine learning model.

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