Sound-based prognostics for a combustion air inducer

    公开(公告)号:US12105057B2

    公开(公告)日:2024-10-01

    申请号:US18448780

    申请日:2023-08-11

    CPC classification number: G01N29/4454 F24F11/49 G01N29/14

    Abstract: A device is configured to operate a Heating, Ventilation, and Air Conditioning (HVAC) system. The device is further configured to determine that the speed of a combustion air inducer has exceeded a speed threshold value. The device is further configured to receive an audio signal from a microphone while operating the HVAC system, to identify an audio signature for the combustion air inducer from an audio signature library, and to determine the audio signature for the combustion air inducer is present within the audio signal. The device is further configured to determine a fault type based on the determination that the audio signature for the combustion air inducer is present within the audio signal.

    METHOD AND SYSTEM FOR AN ACOUSTIC BASED ANOMALY DETECTION IN INDUSTRIAL MACHINES

    公开(公告)号:US20240151690A1

    公开(公告)日:2024-05-09

    申请号:US18372300

    申请日:2023-09-25

    Abstract: In industrial inspection scenarios, early detection of machine malfunction is extremely essential as it helps in preventing any significant damage and the associated economic losses. Embodiments herein provide a method and system for an acoustic based anomaly detection in industrial machines using a beamforming and a sequential transform learning. Herein, the system employs two-stage multi-channel source separation technique that uses the well-known delay and sum beamforming followed by a recent data-driven sequential transform learning (STL) approach to obtain clean sources. The STL is a solution to linear state-space model where operators/matrices are learnt from data and is used here to model the dynamics of time-varying source signals for source separation. Subsequently, a reference template matching is employed on each separated source to detect an anomaly. The numerical results obtained with the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset demonstrate superior performance for source separation and anomaly detection.

    Acoustic signal based analysis of batteries

    公开(公告)号:US11855265B2

    公开(公告)日:2023-12-26

    申请号:US17112756

    申请日:2020-12-04

    CPC classification number: H01M10/484 G01N29/024 G01N29/14 G01R31/392 G08C17/02

    Abstract: Systems and methods for acoustic signal based analysis, include obtaining acoustic response signal data of at least a portion of a battery cell, the acoustic response signal data comprising waveforms generated by transmitting one or more acoustic excitation signals into at least the portion of the battery cell and recording response vibration signals to the one or more acoustic excitation signals. One or more metrics are determined from at least the acoustic response signal data, the one or more metrics being determined based on correlation of the one or more metrics to one or more characteristics of battery cells and a reference model is generated from the one or more metrics. A test battery can be evaluated using the reference model. Actionable insights or recommendations can be generated based on the evaluation. The reference model can also be updated based on the evaluation.

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