ALARM: Automated Latent Anomaly Recognition Method

ALARM (Automated Latent Anomaly Recognition Method) is a robust software application that leverages data-driven algorithms to automatically analyze time-series process data, identifying and assessing anomalies within system operations. This encompasses sensor irregularities, such as bias and drift, and process anomalies, like equipment failures. Through extensive historical sensor data, ALARM generates indicators of detected anomalous behavior and associated sensor sets with minimal human intervention. Its key attributes include its ability to automatically model from large datasets, unlike current solutions requiring the manual selection of sensor sets. It also incorporates a unique algorithm to evaluate the root cause of detected anomalies, a typically challenging task and refines its algorithm based on previously confirmed anomalies to boost detection accuracy. ALARM's value proposition lies in situations where anomaly detection offers financial benefits. Still, developing detailed physics-based models is either prohibitively costly or impractical, making it highly beneficial for large-scale systems operators such as power plants, monitoring and diagnostic centers, cybersecurity firms, and data centers.

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