Rare event detection system

A rare event detection system comprising:

• a memory for storing machine-readable instructions;
• and
• a processor core for accessing the machine-readable instructions and executing the machine-readable instructions as operations, the operations comprising:
• calculating a temporal segmentation pattern based on historical time-series data of historical telemetry streams from a first time period, wherein the historical telemetry streams include at least one or more of historical component statistics, historical operational metrics, or a historical status of a component of a vehicle, and wherein the temporal segmentation pattern includes historical temporal segments;
• identifying sets of predicted events associated with the historical temporal segments based on the historical telemetry streams, wherein the sets of predicted events correspond to shapes of the temporal segmentation pattern for respective historical temporal segments;
• calculating dynamic limits for the respective historical temporal segments based on the shapes of the temporal segmentation pattern defining bounds of nominal behavior of the component;
• identifying a set of active temporal segments of an incoming telemetry stream that captures at least one or more of current component statistics, current operational metrics, or a current status of the component of the vehicle, the set of active temporal segments correspond to a subset of the historical temporal segments of the temporal segmentation pattern, wherein the active temporal segments are from a second time period after the first time period;
• detecting a rare event for an active temporal segment of the set of active temporal segments in response to the incoming telemetry stream exceeding the dynamic limits for a historical temporal segment corresponding to the active temporal segment corresponding to anomalous behavior of the component;
• and
• displaying the active temporal segment having the rare event for the component.

A method for detecting rare events, the method comprising:

• calculating, by a segmentation module operating on a computing platform, a temporal segmentation pattern based on historical time-series data of historical telemetry streams from a first time period, wherein the historical telemetry streams include at least one or more of historical component statistics, historical operational metrics, or a historical status of a component of a vehicle, and wherein the temporal segmentation pattern includes historical temporal segments;
• calculating, by an event module operating on the computing platform, a temporal segmentation pattern of historical time-series data for historical telemetry streams from a first time period, wherein the temporal segmentation pattern includes historical temporal segments;
• identifying, by the event module, sets of predicted events associated with the historical temporal segments based on the historical telemetry streams, wherein the sets of predicted events correspond to shapes of the temporal segmentation pattern for respective historical temporal segments;
• calculating, by a dynamic limit module operating on the computing platform, dynamic limits for the respective historical temporal segments based on the shapes of the temporal segmentation pattern defining bounds of nominal behavior of the component;
• identifying, by the dynamic limit module, a set of active temporal segments of an incoming telemetry stream that captures at least one or more of current component statistics, current operational metrics, or a current status of the component of the vehicle, the set of active temporal segments correspond to a subset of the historical temporal segments of the temporal segmentation pattern, wherein the active temporal segments are from a second time period after the first time period;
• detecting, by a rare event module, a rare event for an active temporal segment of the set of active temporal segments in response to the incoming telemetry stream exceeding the dynamic limits for a historical temporal segment corresponding to the active temporal segment corresponding to anomalous behavior of the component;
• and
• displaying the active temporal segment having the rare event for the component.