公开(公告)号：US20220092432A1

公开(公告)日：2022-03-24

申请号：US17361974

申请日：2021-06-29

Applicant: Tata Consultancy Services Limited

Inventor： Tanushyam CHATTOPADHYAY ,  Suvra DUTTA ,  Abhisek DAS ,  Shubhrangshu GHOSH ,  Prateep KISRA

IPC: G06N3/08 ,  G06N3/04

Abstract: Conventionally, detecting time when a device is going to fail in real time has been a real challenge given the associated constraints and requirements. Due to absence in any supporting information or annotated data, traditional approaches have failed to detection abnormality in devices. Present disclosure provide systems and methods for detecting abnormal behaviour of a device from associated unlabeled sensor observations wherein KPIs are computed based on unlabeled sensor observations of at least two sensor parameters and windowing technique is applied on modified dataset to obtain windowed dataset based on which hyper-parameters of deep learning-based auto-encoder are optimized to obtain set of embeddings. Dimensionality reduction technique is applied on the embeddings to obtain embeddings with reduced dimension. Density based clustering technique with hyper-parameters is applied on embeddings with reduced dimension and cluster(s) for unlabeled sensor observations are obtained. Cardinality is assigned to cluster(s) to predict abnormal behaviour of the device.

公开(公告)号：US20220269689A1

公开(公告)日：2022-08-25

申请号：US17359818

申请日：2021-06-28

Applicant: Tata Consultancy Services Limited

Inventor： Tanushyam CHATTOPADHYAY ,  Arindam HALDER ,  Sangram Dasharath GAIKWAD ,  Tania GHOSH ,  Abhisek DAS ,  Shubhrangshu GHOSH ,  Suvra DUTTA ,  Prateep MISRA

IPC: G06F16/25 ,  G06F16/2455

Abstract: Sensor data (or IoT) analytics plays a critical role in taking business decisions for various entities (e.g., organizations, project owners, and the like). However, scaling of such analytical solutions beyond certain point requires adopting to various computing environments which seems to be challenging with the constrained resources available. Embodiments of the present disclosure provide system and method for analysing and executing sensor observational data in computing environments, wherein extract, transform, load (ETL) workflow pipeline created by users in the cloud, can be seamlessly deployed to job execution service available in cloud/edge without any changes in the code/config by end user. The configuration changes are internally handled by the system based on the selected computing environment and queries are executed either in distributed or non-distributed environments to output data frames. The data frames are further pre-processed in a desired computing environment and thereafter visualized accordingly.

公开(公告)号：US20220221847A1

公开(公告)日：2022-07-14

申请号：US17366777

申请日：2021-07-02

Applicant: Tata Consultancy Services Limited

Inventor： Tanushyam CHATTOPADHYAY ,  Abhisek DAS ,  Suvra DUTTA ,  Shubhrangshu GHOSH ,  Prateep MISRA

IPC: G05B23/02 ,  G06K9/62

Abstract: State-of-the-art approaches have concentrated on building solution(s) to match the amplitude of a time series with a user given one. However, these have failed to implement solution(s) which enables searching for pattern(s) that can depict human vision psychology. Embodiments of the present disclosure determine occurrence of pattern of interest in time series data for anomaly detection, wherein time series data is obtained, and first order derivative is computed. Further an angle of change in direction is derived based on a gradient of change in value of the time series data. This angle is further converted to a measurement unit. The time series data is quantized into bins and a weighted finite state transducers diagram (WFSTD) is obtained based on domain knowledge which is then converted to specific pattern. The specific pattern is searched in the bins to determine occurrence/count of the specific pattern for anomaly detection.