Login Register Cart 0

A Practitioner's Approach to Problem-Solving using AI

Back Book Details

Time Sequence Data Monitoring Method Based on Auto-Aligning Bidirectional Long and Short-Term Memory Network

Author(s): Abha Kiran Rajpoot*, Shashank Awasthi, Mahaveer Singh Naruka, Dibyahash Bordoloi and Neha Garg

Pp: 158-170 (13)

DOI: 10.2174/9789815305364124010012

* (Excluding Mailing and Handling)

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.

Keywords: Auto-aligning, Attention scoring, Bidirectional LSTM, Data preprocessing, Monitoring method, Prediction probability, Real-time monitoring, Time sequence data.

Cite as

View More
View More