Falkonry Inc.

Vukasin Toroman

Nikunj R Mehta

Daniel Kearns

Lenin Kumar Subramanian

Joseph W Porter

Charu Singh

Vinit Acharya

Namrata Rao

An anomaly diagnosis system obtains a plurality of anomaly signals corresponding to a plurality of sensor signals of a physical system and segments one or more anomaly signals into a plurality of time segments. The system determines an anomaly score for each time segment based on anomaly values of the one or more anomaly signals during the time segment and identifies an anomaly time interval corresponding to at least one consecutive time segment within the plurality of time segments. The system clusters the plurality of anomaly signals within the anomaly time interval to identify an anomaly group of sensor signals associated with the anomaly time interval and determines an aggregate anomaly score for the anomaly group. The system generates a graphical user interface presenting a representation of the anomaly group of sensor signals and the aggregate anomaly scores and causes the graphical user interface to be displayed on a user device.

Anomaly diagnosis for time series data

Techniques for managing machine operations using encoded multi-scale time series data are provided. In one technique, operational data is received from a sensor coupled to an industrial device. For each portion in a first set of portions of the operational data (where each portion corresponds to a first time scale), first aggregated data is generated based on time series data from that portion and a first encoding is generated based on the first aggregated data. For each portion in a second set of portions of the operational data (where each portion of the second set corresponds to a second time scale that is different than the first time scale), second aggregated data is generated based on time series data from that portion and a second encoding is generated based on the second aggregated data. The operational data is classified to determine a condition of the industrial device during the time interval based on the first and second encodings.

Managing machine operations using encoded multi-scale time series data

A tile contains aggregated data at a certain resolution for the actual data present in the duration of time covered in that tile. Tiles are generated at every possible resolution suitable for a computer display and provide aggregate measures such as averages and variances. Tiles provide a summary of data such that from the highest level view down to the specific data time points collected, a user's attention may be drawn to the times when there is the most interesting pattern behavior to review and analyze.

Fluid and resolution-friendly view of large volumes of time series data

Growing community of inventors

Karlsruhe, Germany

Vukasin Toroman