Run a self-training anomaly detection algorithm against a given dataset. The selected algorithm is Moving Median Decomposition as seen in this whitepaper for Anomaly Detection for Predictive Maintenance. This Library is for identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.

This is an ipm package, which contains one or more reusable assets within the ipm Community. The 'package.json' in this repo is a ipm spec's package.json, here, which is a superset of npm's package.json spec, here.

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Ready out-of-the-box

Requires API keys from Email and SMS Alerts via SendGrid and Twilio.

See AnomalyDetectionConstants Library.

AnomalyDetection - Portal for viewing and configuring live datafeeds, rules, and alerts

ExampleDetectAnomaly - Runs anomaly detection against an example dataset, returns an array of Anomalies with metadata.

AnomalyDetection - Core library for detecting anomalies in datasets AnomalyDetectionConstants - Optional credentials for Alerting (SMS via Twilio, Email via Sendgrid) jStat - Dependency on jstat-statistics-toolkit TwilioSMS - Dependency on twilio-sms-library SendGridEmail - Dependency on(https://github.com/rreinold/clearblade-sendgrid-email-integration)

Anomalies - Tracks all detected anomalies AnomalyConfiguration - Stores user-defined rules and alerts for Anomaly Detection

AnomalyDetection()

Detects Anomalies with Moving Median Decomposition See attached whitepaper for Anomaly Detection for Predictive Maintenance https://files.knime.com/sites/default/files/inline-images/Anomaly_Detection_Time_Series_final.pdf

Run a self-training anomaly detection algorithm against a given dataset

Twilio(user, pass, sourceNumber)

Sends a text message using Twilio's REST API.

DetectAnomaly() ⇒ AnomalyVisual

Detects an anomaly in the last X MQTT Messages Uses 'AnomalyConfiguration' config to check for a key in the JSON of each message. ex. 'sensor_key' will be set to 'temperature', so we know to pull the temperature key/value pair from a mqtt message like this: {"temperature":40,"humidity":31}

ExampleDetectAnomaly() ⇒ Array.<Anomaly>

Example logic for detecting anomalies in a dataset of sensor values

SaveAnomalyConfiguration()

Ingests updated anomaly configuration, and saves to AnomalyConfiguration Collection

SendAlert()

This is triggered by an Anomaly being recorded, and inserted into the Anomalies Collection

• Fetches the anomaly row
• Checks for alerting configuration
• Sends email, text alert

SimulateSensorFeed()

Simulates a sensor feed from a sensor network of temperature sensors Publishes NUM_MESSAGES messages to TOPIC

Calibration

Anomaly

Creator: Robert Reinold Updated: 2017-10-02T00:00:00Z Version: v2.0 Tags: email, sendgrid, marketing, mail, api, rest, http

Usage:

1. Create a free SendGrid Account.
2. Log into your SendGrid account, and view the Settings > API Keys tab. Create an API Key with full access to "Mail Send" rights.
3. Replace <SEND_GRID_API_KEY> with SendGrid API key
4. Replace <ORIGIN_EMAIL_ADDRESS> with your desired email address. This will be the 'sender' of the email.
5. Add 'SendGridEmail' as a dependency to your code services (Settings > Requires > Add)

Kind: global variable

Detects Anomalies with Moving Median Decomposition See attached whitepaper for Anomaly Detection for Predictive Maintenance https://files.knime.com/sites/default/files/inline-images/Anomaly_Detection_Time_Series_final.pdf

Run a self-training anomaly detection algorithm against a given dataset

Kind: global function
Parameter: number[] dataset array of numbers

• AnomalyDetection()
  • ~calibrate()
  • ~detect() ⇒ Array.<Anomaly>
  • ~getCalibration() ⇒ Calibration

Use precomputed anomaly detection calibration profile This can be used to speed up performance for real-time anomaly detection

Kind: inner method of AnomalyDetection

Run the algorithm against the provided dataset.

Note: This is the most computation-heavy method in this library

Kind: inner method of AnomalyDetection
Returns: Array.<Anomaly> - anomalies - the list of anomalies found in dataset
Parameter: number strictnessOverride (optional) Set the strictness of the anomaly detection

Fetch the computed anomaly detection calibration parameters

Kind: inner method of AnomalyDetection
Returns: Calibration - calibration

Detects an anomaly in the last X MQTT Messages Uses 'AnomalyConfiguration' config to check for a key in the JSON of each message. ex. 'sensor_key' will be set to 'temperature', so we know to pull the temperature key/value pair from a mqtt message like this: {"temperature":40,"humidity":31}

Kind: global function
Returns: AnomalyVisual - data to visualize this anomaly detection

Example logic for detecting anomalies in a dataset of sensor values

Kind: global function
Returns: Array.<Anomaly> - anomalies

Ingests updated anomaly configuration, and saves to AnomalyConfiguration Collection

Kind: global function
Parameter: AnomalyConfiguration anomalyConfiguration - map of settings to update

This is triggered by an Anomaly being recorded, and inserted into the Anomalies Collection

• Fetches the anomaly row
• Checks for alerting configuration
• Sends email, text alert

Kind: global function

Simulates a sensor feed from a sensor network of temperature sensors Publishes NUM_MESSAGES messages to TOPIC

Kind: global function

Kind: global typedef
Properties

Kind: global typedef
Properties