The Virtual Sensor API is used to detect faults in a variable of importance. This could be for instance to detect overheating in electrical assets or to detect thermal anomalies in HVAC process. It leverages supervised machine learning techniques to learn the relationship between the target variable, a set of heterogeneous available variables, and to identify drift between the predicted value of the target and its actual variable. This API should be used when the target variable is supposed to have an underlying pattern and when measurements of the target variable are always available. This document provides a general tutorial for users who want to consume the Virtual Sensor Fault Detection API.
- The Virtual Sensor Fault Detection API includes 4 features:
- Learn the model (the relationship between the input data and the target on which to detect faults). It requires data with measurement of the target. (createModel endpoint). - Detect fault by comparing the target variable value and its estimated value by the model. It requires the model ID of the corresponding model and the measured value of the target (applyModelAndDetectFaults endpoint). - Update an existing model by feeding it with new data (updateModel endpoint). - Get information on an existing model (getModelInformation endpoint).
Take the tour and experiment on your own data prior to any development through our hands-on application !\r\n\r\nhttps://urldefense.com/v3/__https://try-analytics-se.azurewebsites.net/vsfd__;!!GF_29dbcQIUBPA!gOmWZKxCOwy-V41NzIYvALqxexBuKHokVmaA7_otnXU9tY6UuNjA590JS1d-5gIsIbU$ [try-analytics-se[.]azurewebsites[.]net]