Anomalous behavior detection by an artificial intelligence-enabled system with multiple correlated sensors
Multi-metric artificial intelligence (AI)/machine learning (ML) models for detection of anomalous behavior of a machine/system are disclosed. The multi-metric AI/ML models are configured to detect anomalous behavior of systems having multiple sensors that measure correlated sensor metrics such as coolant distribution units (CDUs). The multi-metric AI/ML models perform the anomalous system behavior detection in a manner that enables both a reduction in the amount of sensor instrumentation needed to monitor the system's operational behavior as well as a corresponding reduction in the complexity of the firmware that controls the sensor instrumentation. As such, AI-enabled systems and corresponding methods for anomalous behavior detection disclosed herein offer a technical solution to the technical problem of increased failure rates of existing multi-sensor systems, which is caused by the presence of redundant sensor instrumentation that necessitates complex firmware for controlling the sensor instrumentation.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hewlett Packard Enterprise Development LP, Spring, TX (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC36-08GO28308
- Assignee:
- Hewlett Packard Enterprise Development LP (Spring, TX)
- Patent Number(s):
- 11,774,956
- Application Number:
- 17/207,540
- OSTI ID:
- 2293684
- Country of Publication:
- United States
- Language:
- English
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