Autonomous Anomaly Detection
SUBSTRATE ARTIFICIAL INTELIGENCE SA
Autonomous Anomaly Detection
SUBSTRATE ARTIFICIAL INTELIGENCE SA
Autonomous Anomaly Detection
SUBSTRATE ARTIFICIAL INTELIGENCE SA
Autonomous anomaly detection, predictive maintenance.
The autonomous anomaly detector service is a REST API-based solution that supports the training and prediction of events/severity from a time series.
Its value proposition is to mitigate the hard work required to build and maintain an anomaly detection system manually for assets streaming data signals for any industry.
In just a few clicks a swagger definition and test interface to simplify integration will be created once subscribed. No resource will be created in your Tenant so you will not have extra costs, just the subscription fee.
For each metric to be tracked, the developer creates a set of models. The time bucket intervals are flexible to what process is under evaluation. The basic workflow includes:
Creation of User Account
Creation of Model
Model training with historical data
Signal data
Event data
Severity data
Prediction
The training endpoint takes a signal and event/severity time series as input with basic configuration parameters. Internally the training process performs the following steps:
Train anomaly model
Identify anomalies in signal data
Break up the anomalies into clustered groups
Map events/severity points to anomaly clusters
Build event and severity prediction models
Save trained models into blob storage
The prediction process takes a time series sequence and returns a set of predictions that include event type and severity for the input data.
Inverter Data
Timestamp
Average Temperature
POA
Wind Direction
Wind Speed
Revenue Power Meter
AC Power
DC Power
DC Current
DC Voltage
Latitude
Longitude
Event Data
Timestamp
Type
Severity