Abstract
This research proposes a time sequence data monitoring method that utilizes
a auto-aligning bidirectional long and short-term memory network (LSTM) for
efficient and accurate monitoring of equipment. The method involves several steps,
including data preprocessing, bidirectional LSTM modeling, attention scoring,
prediction probability calculation, and real-time monitoring. By leveraging the
capabilities of auto-aligning and bidirectional LSTM, the proposed method aims to
enhance the accuracy and effectiveness of equipment monitoring based on time
sequence data.
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