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Alerts via Anomaly Detection

Alexandrite

Alerts via Anomaly Detection

Alerts via Anomaly Detection

This documents objective is to provide information and links about alerts used for anomaly detection. This document covers following topics:

  • What Is Anomaly Detection
  • Implementing Anomaly Detection
  • Creating an Anomaly Alert and Prerequisites
  • Anomaly Stats
  • Certainty Parameter
  • Video Example On How To Create An Alert for Anomaly Detection
  • Tips and troubleshooting

What Is Anomaly Detection

Anomaly Detection in ThingWorx is implemented via built-in ThingWatcher functionality. ThingWatcher detects anomalies by monitoring a data stream from a device, calculating an expected distribution of data, and validating that the current data point is a member of the expected distribution.

 

Implementing Anomaly Detection

Anomaly Detection is enabled by default in ThingWorx. However, several steps are required to configure the functionality for your specific environment, including the prerequisite activities below.

 

Creating an Anomaly Alert and Prerequisites  

Configuring Anomaly Detection to monitor a stream of data. For information about setting up Anomaly Detection, view Preparing ThingWorx for Anomaly Detection.

Anomaly Stats

Anomaly Alert Statuses moves through several statuses as it works its way through the corresponding phases.

  • Initialized
  • Calibrating
  • Training
  • Buffering
  • Monitoring
  • Failed

Certainty Parameter

The Certainty Parameter when implementing anomaly detection requires a number of factors to consider. At its most basic, ThingWatcher functionality compares two sets of data, a validation set (collected during the Calibrating phase) and a test dataset (data streaming from a remote device). ThingWatcher tries to determine the likelihood that the distribution of values in the test dataset is from the same distribution of values contained in the validation dataset. The accuracy of the model plays a large role in this determination, but so does the Certainty parameter used for the statistical analysis of the two data sets.

 

Video Example On How To Create An Alert for Anomaly Detection

Anomaly Detection Part 1. Create connectivity between KEPServer and ThingWorx Platform.

Anomaly Detection Part 2. Configure Anomaly Alert to bind simulated data coming through KEPServer for Anomaly Detection.

Anomaly Detection Part 3. Viewing data via Anomaly Mashup.

Tips and troubleshooting

Diagnose and fix the most common issues that may be encountered when working with ThingWatcher. It cannot be stressed strongly enough that you should be familiar with your data including the average time interval between data points, and the collection duration and certainty threshold you specified.