This dataset contains expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity.

Real spacecraft and curiosity rover anomalies for anomaly detection

Indications of telemetry anomalies can be found within previously mentioned ISA reports. All telemetry channels discussed in an individual ISA were reviewed to ensure that the anomaly was evident in the associated telemetry data, and specific anomalous time ranges were manually labeled for each channel. If multiple anomalous sequences and channels closely resembled each other, only one was kept for the experiment in order to create a diverse and balanced set. Anomalies were classified into two categories, point and contextual, to distinguish between anomalies that would likely be identified by properly set alarms or distance-based methods that ignore temporal information (point anomalies) and those that require more complex methodologies such as LSTMs or Hierarchical Temporal Memory (HTM) approaches to detect (contextual anomalies)

SMAP Anomalies

• TM Channels (55)
• Total TM values (429,735)
• Total anomalies (69)

MSL:

TM Channels (27) Total TM values (66,709) Total anomalies (36)

Data in .npy files

All credits go to the original authors of the dataset, many thanks to them for making such data publicly available:

• Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell, Tom Soderstrom. Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding, 2018, NASA Jet Propulsion Laboratory
• Read more of NASA anomaly detection work: https://github.com/khundman/telemanom