Time-series and sensor data analysis
Ezako labels events and detects anomalies in sensor and measurement series. Its work covers engine test signals, satellite telemetry, the inspection of radio sites and vibration monitoring on embedded boards; the same methods apply to machines, production lines and fleets of equipment.
On sensor series
Similar patterns
From a labeled event, search for the ranges that resemble it, on one or several columns and across series; each result carries a score and is reviewed by the analyst before it receives a label.
Label candidates
Rare patterns of a column proposed as candidates, scored, and submitted to the analyst for validation.
Computed columns
Combination of columns, derivative, low-pass denoising, normalisation, and the remaining time before a labeled event, the target of a remaining-useful-life model.
Fields of application
Rotating machines and turbines
Detecting a drift in vibration, pressure and temperature series before it becomes a failure.
Production lines
Spotting a quality drift in the sensors of a line before it reaches the parts.
Fleets of equipment
Ranking which sites, charging stations or vehicles to inspect first, as for ANFR’s radio sites.
Software and services
What Ezako provides
Upalgo Labeling Timeseries
Labeling of sensor time series, with the search for similar patterns across series.
AI Algorithms
Automatic detection and pre-labeling, validated by analysts.
UpalgoDB
The central server for recordings, labels and labeling teams.
References
Examples of projects carried out
DeepGreen
Vibration monitoring on an embedded board
Research and development within DeepGreen, the project led by CEA that builds the open-source Aidge platform for embedded AI: fault detection by machine learning on a microcontroller board of the STM32 class. Power generation machines were fitted with sensors to detect a departure from their expected behaviour; Ezako brings its expertise in time series.
Safran Aircraft Engines
Abnormal observations in engine test signals
A deep learning method to detect abnormal observations in the high-frequency sensor series of engine development tests, where they make the spectrograms unusable. Published with Safran Aircraft Engines.
CNES
Anomalies in satellite telemetry
Detection of anomalies in satellite telemetry by machine learning, in addition to the conventional surveillance methods.
ANFR
Which radio sites to inspect first
Prediction of the radio sites most likely to reveal an anomaly, to set the order of the inspections of a national park of more than 76,000 sites.
See what Ezako can do with your data
Describe your signals and your requirements to arrange a demonstration.