Detektion von Abnormalem Verhalten von Vorrichtungen von Zugeordneten Unmarkierten Sensorbeobachtungen

Details

Veröffentlichungs-Nr.

EP3961338

Aktenzeichen

EP21182379

Anmeldetag

29. Juni 2021

Veröffentlichung

3. Januar 2024

Erteilung

3. Januar 2024

Rechtsraum

EP

IPC

G05B23/02G06N20/00

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 clusters) for unlabeled sensor observations are obtained. Cardinality is assigned to clusters) to predict abnormal behaviour of the device.

Anmelder

Firma

Tata Consultancy Services Limited

Land

🇮🇳 Indien

Fachgebiete