A method for predicting failures and remaining useful life (RUL) for equipment, the method comprising: for event-based sequential data received from the equipment comprising fault events, conducting feature extraction on the event-based sequential data to generate data samples with sequences of event features based on the fault events; executing data augmentation on the data to increase the amount of data samples available for model training, the data augmentation configured to generate additional semantically similar synthetic data samples based on the available data samples by using, within the generated sequences: dropout of subsequences, random injection of subsequences, random shuffling or permutations of subsequences, random variation in continuous features such that mean and variance of data distribution is maintained, and value swap from nearby subsequences; applying deep learning modeling (131-133) to the increased amount of data samples of sequences of event features to generate a model configured to predict the failures and the RUL for the equipment based on event features extracted from data of the equipment; executing optimization (141-142) on the model, wherein the optimization (141-142) includes data-adaptive optimization configured to weigh ones derived from the event-based sequential data received from the equipment higher than ones derived from the semantically similar data samples for the prediction of the failures and the RUL for the equipment; and executing the model on data received from the equipment and controlling operation of the equipment based on the thereby predicted failures and RUL. for event-based sequential data received from the equipment comprising fault events, conducting feature extraction on the event-based sequential data to generate data samples with sequences of event features based on the fault events; executing data augmentation on the data to increase the amount of data samples available for model training, the data augmentation configured to generate additional semantically similar synthetic data samples based on the available data samples by using, within the generated sequences: dropout of subsequences, random injection of subsequences, random shuffling or permutations of subsequences, random variation in continuous features such that mean and variance of data distribution is maintained, and value swap from nearby subsequences; dropout of subsequences, random injection of subsequences, random shuffling or permutations of subsequences, random variation in continuous features such that mean and variance of data distribution is maintained, and value swap from nearby subsequences; applying deep learning modeling (131-133) to the increased amount of data samples of sequences of event features to generate a model configured to predict the failures and the RUL for the equipment based on event features extracted from data of the equipment; executing optimization (141-142) on the model, wherein the optimization (141-142) includes data-adaptive optimization configured to weigh ones derived from the event-based sequential data received from the equipment higher than ones derived from the semantically similar data samples for the prediction of the failures and the RUL for the equipment; and executing the model on data received from the equipment and controlling operation of the equipment based on the thereby predicted failures and RUL. <EOS>