A processor implemented method for detecting abnormal behaviour of devices from unlabeled sensor observations pertaining to the devices, comprising: obtaining, via one or more hardware processors, 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 connected to a device and the at least one sensor captures sensor observation pertaining to the device; computing, via the one or more hardware processors, 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, via the one or more hardware processors, 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 via the one or more hardware processors, 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, via the one or more hardware processors, a dimensionality reduction technique on the one or more corresponding embeddings to obtain a set of embeddings with reduced dimension; applying via the one or more hardware processors, 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 cardinality is indicative of an operating status of the device over a time under consideration; and generating, via the one or more hardware processors, at least one alert based on the at least one cardinality assigned to the one or more clusters of the plurality of clusters, wherein the alert is communicated via a communication channel to an operator to acquire data from at least the one sensor for further processing and analysis. obtaining, via one or more hardware processors, 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 connected to a device and the at least one sensor captures sensor observation pertaining to the device; computing, via the one or more hardware processors, 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, via the one or more hardware processors, 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 via the one or more hardware processors, 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, via the one or more hardware processors, a dimensionality reduction technique on the one or more corresponding embeddings to obtain a set of embeddings with reduced dimension; applying via the one or more hardware processors, 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 cardinality is indicative of an operating status of the device over a time under consideration; and generating, via the one or more hardware processors, at least one alert based on the at least one cardinality assigned to the one or more clusters of the plurality of clusters, wherein the alert is communicated via a communication channel to an operator to acquire data from at least the one sensor for further processing and analysis.