Bi-LSTM Alarm Sequence Prediction for Long-Horizon Operator Guidance
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Solution Overview
Problem
Current alarm prediction systems are limited in predicting long duration alarm sequences and rely on LSTM architectures that fail to effectively learn dependencies between input and output sequences, resulting in inaccurate and short-term predictions, which are insufficient for proactive maintenance and operational efficiency.
Innovation Solution
The implementation of a Bidirectional Long short-term memory (BiLSTM) neural network with an encoder-decoder technique that uses true output sequences as input during training, allowing for the prediction of longer alarm sequences and the generation of a mapping table to recommend operator actions based on a Matching Sequence Score (MSS) for optimal preventive measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If LSTM architecture is used for alarm prediction, then the system can process sequential alarm data, but the prediction duration is limited to minutes and accuracy deteriorates for longer sequences
Solution Approach 1:
The alarm sequence prediction problem is segmented into multiple time steps, where the model predicts one alarm at a time in sequence. The BiLSTM processes the input alarm sequence in segments, generating predictions step-by-step for extended time horizons while maintaining accuracy at each prediction step rather than degrading over the entire sequence.
Solution Approach 2:
The patent transitions from unidirectional LSTM to bidirectional LSTM, adding a temporal dimension by processing sequences in both forward and backward directions. This dimensional change allows the model to capture dependencies from both past and future contexts, enabling longer prediction durations without sacrificing accuracy.
2Device complexity
If LSTM uses only past information for training, then the model architecture remains simple, but the ability to learn dependencies between input and output sequences is insufficient
Solution Approach 1:
The patent applies the inversion principle by using bidirectional LSTM that processes sequences in both forward and backward directions, rather than only forward as in traditional LSTM. This allows the model to learn dependencies by considering future context during training, significantly improving sequence dependency learning while maintaining manageable architectural complexity.
3Loss of time
If the forecasting window is small (minutes), then the LSTM model can provide predictions, but operators have insufficient time to plan and execute preventive measures
Solution Approach 1:
The BiLSTM model performs preliminary predictions of alarm sequences extending several hours into the future, allowing operators to take preventive actions before faults actually occur. By predicting the sequence of alarms in advance, the system enables operators to plan and execute mitigation strategies proactively rather than reactively.
Data Source
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AI summary
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.