A method of detecting at least one abnormal datapoint in operation data (U) associated with an industrial environment , wherein the operation data (U) comprises historical data and streaming data corresponding to operation of industrial assets (612-618) in the industrial environment , the method comprising: applying one or more anomaly detection models (fi) to at least one subset (S) of the operation data (U), wherein the anomaly detection models (fi) are trained based on a training dataset (L) consisting of datapoints labeled as normal; classifying subset-datapoints in the subset (S) as one of normal datapoints (N) and abnormal datapoints (A) using the anomaly detection models (fi); determining confidence index in the classification of the subset-datapoints based on at least one of an engineering software input and operation of a comparable industrial environment; re-classifying the subset-datapoints when a confidence threshold is not satisfied; updating the training dataset with the confidence index and the re-classified datapoints; updating the training dataset with the normal datapoints; retraining the anomaly detection models (fi) with the updated training dataset after expiration of a threshold time, wherein the threshold time is based on the number of updates to the training dataset; and detecting the at least one abnormal datapoint in the operation data (U) using the anomaly detection models (f'i). removing the subset from the operation data (U); detecting the at least one abnormal datapoint in remainder operation data (U) without the subset using the retrained anomaly detection models (fi); applying the retrained anomaly detection models (fi) to a new subset of the operation data (U) and classifying new-datapoints in the new subset as one of the normal datapoints (N) and the abnormal datapoints (A); and updating the training dataset and retraining the retrained anomaly detection models (f'i). applying one or more anomaly detection models (fi) to at least one subset (S) of the operation data (U), wherein the anomaly detection models (fi) are trained based on a training dataset (L) consisting of datapoints labeled as normal; classifying subset-datapoints in the subset (S) as one of normal datapoints (N) and abnormal datapoints (A) using the anomaly detection models (fi); determining confidence index in the classification of the subset-datapoints based on at least one of an engineering software input and operation of a comparable industrial environment; re-classifying the subset-datapoints when a confidence threshold is not satisfied; updating the training dataset with the confidence index and the re-classified datapoints; updating the training dataset with the normal datapoints; retraining the anomaly detection models (fi) with the updated training dataset after expiration of a threshold time, wherein the threshold time is based on the number of updates to the training dataset; and detecting the at least one abnormal datapoint in the operation data (U) using the anomaly detection models (f'i). removing the subset from the operation data (U); detecting the at least one abnormal datapoint in remainder operation data (U) without the subset using the retrained anomaly detection models (fi); applying the retrained anomaly detection models (fi) to a new subset of the operation data (U) and classifying new-datapoints in the new subset as one of the normal datapoints (N) and the abnormal datapoints (A); and updating the training dataset and retraining the retrained anomaly detection models (f'i). <EOS>