Long duration alarm sequence predictions using bi-lstm and operator sequence recommendations thereof

Abstract

Techniques used by the state of the art alarm prediction systems rely mostly on LSTM, which have technical limitation in predicting a sequence with long durations. Embodiments of the present disclosure provide a method and system for long duration alarm sequence predictions from past alarm sequence using a Bi-LSTM and operator sequence recommendations thereof. The BiLSTM with an encoder decoder technique uses true output sequence as input to decoder at each time step during training. This allows the BiLSTM to learn dependencies among input and output sequence effectively. The operator sequence recommended is identified based on closeness of the predicted future output alarm sequence with one among the unique alarm sequences in a mapping table. The alarm sequence closeness is computed using a Matching Sequence Score (MSS) disclosed by the method, since known sequence evaluation metrics such as Blue Score has limitations to be directly applied in alarm sequence evaluation.