Detection of abnormal behaviour of devices from associated unlabeled sensor observations

A system, comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• obtain a dataset comprising a first unlabeled sensor observation and a second unlabeled sensor observation from at least one sensor respectively, the at least one sensor is attached to a device;
• compute one or more key performance indicators (KPIs) based on a function of the first unlabeled sensor observation and the second unlabeled sensor observation comprised in the dataset to obtain a modified dataset, wherein the one or more key performance indicators are computed for a plurality of time instances of the first unlabeled sensor observation and the second unlabeled sensor observation;
• apply a windowing technique on the modified dataset based on a transpose function implemented on the plurality of time instances of the first unlabeled sensor observation and the second unlabeled sensor observation comprised in the modified dataset to obtain a windowed dataset;
• optimize, using the windowed dataset, one or more hyper-parameters of a deep learning-based auto-encoder to obtain one or more optimum encoder hyper-parameters, wherein the one or more optimum encoder hyper-parameters serve as one or more corresponding embeddings;
• apply a dimensionality reduction technique on the one or more corresponding embeddings to obtain a set of embeddings with reduced dimension;
• and
• apply a clustering technique on the set of embeddings to obtain a plurality of clusters, wherein one or more clusters from the plurality of clusters is assigned at least one cardinality, the at least one cardinality is indicative of an operating status of the device over a time under consideration.

A computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device causes the computing device to detect abnormal behaviour of devices from associated unlabeled sensor observations by:

• obtaining a dataset comprising a first unlabeled sensor observation and a second unlabeled sensor observation from at least one sensor respectively, the at least one sensor is attached to a device;
• computing one or more key performance indicators (KPIs) based on a function of the first unlabeled sensor observation and the second unlabeled sensor observation comprised in the dataset to obtain a modified dataset, wherein the one or more key performance indicators are computed for a plurality of time instances of the first unlabeled sensor observation and the second unlabeled sensor observation;
• applying a windowing technique on the modified dataset based on a transpose function implemented on the plurality of time instances of the first unlabeled sensor observation and the second unlabeled sensor observation comprised in the modified dataset to obtain a windowed dataset;
• optimizing, using the windowed dataset, one or more hyper-parameters of a deep learning-based auto-encoder to obtain one or more optimum encoder hyper-parameters, wherein the one or more optimum encoder hyper-parameters serve as one or more corresponding embeddings;
• applying a dimensionality reduction technique on the one or more corresponding embeddings to obtain a set of embeddings with reduced dimension;
• and
• applying a clustering technique on the set of embeddings to obtain a plurality of clusters, wherein one or more clusters from the plurality of clusters is assigned at least one cardinality, the at least one cardinality is indicative of an operating status of the device over a time under consideration.