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Hybrid clustering-partitioning techniques that optimizes accuracy and compute cost for prognostic surveillance of sensor data
Hybrid clustering-partitioning techniques that optimizes accuracy and compute cost for prognostic surveillance of sensor data
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机译:混合聚类-分区技术可优化准确性并计算成本,以进行传感器数据的预后监视
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摘要
The disclosed embodiments relate to a system for performing prognostic surveillance operations on sensor data. During operation, the system obtains a group of signals from sensors in a monitored system during operation of the monitored system. Next, if possible, the system performs a clustering operation, which divides the group of signals into groups of correlated signals. Then, for one or more groups of signals that exceed a specified size, the system randomly partitions the groups of signals into smaller groups of signals. Next, for each group of signals, the system trains an inferential model for a prognostic pattern-recognition system based on signals in the group of signals. Then, for each group of signals, the system uses a prognostic pattern-recognition system in a surveillance mode and the inferential model to detect incipient anomalies that arise during execution of the monitored system.
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