Estimating Remaining Useful Life (RUL) from multi-sensor time series data is difficult through manual inspection. Current machine learning and data analytics 5 methods, for RUL estimation require large number of failed instances for training, which are rarely available in practice, and these methods cannot use information from currently operational censored instances since their failure time is unknown. Embodiments of the present disclosure provide systems and methods for estimating RUL using time series data by implementing an LSTM-RNN based 10 ordinal regression technique, wherein during training RUL value of failed instance(s) is encoded into a vector which is given as a target to the model. Unlike a failed instance, the exact RUL for a censored instance is unknown. For using the censored instances, target vectors are generated and the objective function is modified for training wherein the trained LSTM-RNN based ordinal regression is 15 applied on an input test time series for RUL estimation. [To be published with FIG. 2] Obtaining a first time series data and a second time series data for one or more failed instances and one or more 202 censored instances respectively specific to the one or more parameters associated with one or more entities Determining (i) a Remaining Useful Life (RUL) for the one or more failed instances and (ii) at least a minimum RUL for the 204 one or more censored instances Generating (i) a first set of binary labels using the RUL for the one or more failed instances and (ii) a second set of binary labels using the at least a minimum RUL for the one 206 or more censored instances respectively Training, a Recurrent Neural Network (RNN) based Ordinal Regression Model (ORM) comprising one or more binary classifiers, using (i) the first set of binary labels and (ii) the 208 second set of binary labels and associated label information thereof Obtaining a time series data pertaining to one or more parameters of the entities, wherein the time series data 210 comprises one or more test instances Applying the trained RNN based ORM comprising the one or more trained binary classifiers on the time series data 212 comprising the one or more test instances to obtain an estimate of target label for each trained binary classifier, wherein an estimate of target vector is obtained using the estimate of target label obtained for each of the one or more trained binary classifiers Generating, by using the estimate of target vector, a RUL estimate specific to the one or more test instances of the 214 one or more entities
FAILED AND CENSORED INSTANCES BASED REMAINING USEFUL LIFE (RUL) ESTIMATION OF ENTITIES