Sensor Frozen-Period Detection Using Run-Length Clustering

The patent applies dynamics by transitioning from a static predetermined threshold to a dynamic threshold that adapts to changing sensor data conditions. The threshold is no longer fixed but evolves based on the statistical properties of the incoming data stream, allowing the detection system to maintain accuracy across varying sampling periods and data characteristics.

The patent implements parameter changes by modifying the threshold parameter from a constant predetermined value to a dynamically calculated value based on data statistics. This involves changing the threshold parameter according to the observed data distribution, mean, variance, or other statistical measures, enabling the system to adapt to different operating conditions and sampling rates.

The patent applies preliminary action by performing initial statistical analysis on the sensor data to establish baseline characteristics before formal detection begins. This preliminary characterization of the data distribution, mean, and variance enables the system to quickly adapt to new data conditions without requiring complex real-time calculations during the actual detection process.

The detection system implements self-service by automatically adjusting its own threshold parameters based on the statistical properties of the incoming sensor data. The system monitors its own performance and adapts the threshold dynamically without external intervention, using the data itself to determine appropriate detection parameters for varying conditions.