Historical Alarm

Historical alarm analysis focuses on improving the efficiency and accuracy of alarm systems across various domains, from industrial processes to healthcare, by leveraging past alarm data. Current research emphasizes developing advanced machine learning models, including deep learning architectures like encoder-only designs, BiLSTM-CNNs, and embedding techniques, to identify patterns, predict future events, and reduce false alarms. These advancements aim to enhance operational safety, improve decision-making, and optimize resource allocation by providing more actionable insights from alarm data, ultimately leading to more efficient and reliable systems.