公开(公告)号：US20210019876A1

公开(公告)日：2021-01-21

申请号：US16900106

申请日：2020-06-12

Applicant: Tata Consultancy Services Limited

Inventor： Achanna Anil KUMAR ,  Rishab KHAWAD ,  Riddhi PANSE ,  Andrew GIGIE ,  Tapas CHAKRAVARTY ,  Kriti KUMAR ,  Saurabh SAHU ,  Mariswamy Girish CHANDRA

IPC: G06T7/00 ,  G01N21/88

Abstract: The disclosure herein generally relates to image processing, and, more particularly, to a method and system for impurity detection using multi-modal image processing. This system uses a combination of polarization data, and at least one of a depth data and an RGB image data to perform the impurity material detection. The system uses a graph fusion based approach while processing the captured images to detect presence of the impurity material, and accordingly alert the user.

公开(公告)号：US20240151690A1

公开(公告)日：2024-05-09

申请号：US18372300

申请日：2023-09-25

Applicant: Tata Consultancy Services Limited

Inventor： Saurabh SAHU ,  Mariswamy Girish CHANDRA ,  Kriti KUMAR ,  Achanna Anil KUMAR ,  Angshul MAJUMDAR

IPC: G01N29/04 ,  G01N29/06 ,  G01N29/14 ,  G01N29/44

CPC classification number: G01N29/043 ,  G01N29/069 ,  G01N29/14 ,  G01N29/4418 ,  G01N2291/0258

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.

公开(公告)号：US20240288340A1

公开(公告)日：2024-08-29

申请号：US18400174

申请日：2023-12-29

Applicant: Tata Consultancy Services Limited

Inventor： Saurabh SAHU ,  Achanna Anil KUMAR ,  Mariswamy Girish CHANDRA ,  Kriti KUMAR ,  Angshul MAJUMDAR

IPC: G01M99/00 ,  H04R3/00 ,  H04R5/027 ,  H04S3/00

CPC classification number: G01M99/005 ,  H04R3/005 ,  H04R5/027 ,  H04S3/008 ,  H04S2400/01 ,  H04S2400/15

Abstract: This disclosure relates generally to a field of industrial machine inspection, and, more particularly, to method and system for acoustic based industrial machine inspection using Delay-and-Sum beamforming (DAS-BF) and dictionary learning (DL). The disclosed method presents a two-stage approach for anomaly detection using a multi-channel acoustic mixed signal. In the first stage, separation of a plurality of acoustic signals corresponding to the spatially distributed acoustic sources is performed at a coarser level by using the DAS-BF. Subsequently, dictionaries pre-trained using the plurality of acoustic signals of the individual source machines are utilized for generating a plurality of separated acoustic source signals. The generated plurality of separated acoustic source signals are analyzed for the anomaly detection by comparing them with a corresponding normal machine sound template.