What types of IoT data can this workflow handle?

The workflow can handle univariate or multivariate time series from industrial sensors, telemetry, or other IoT devices, as long as the data can be read into KNIME.

Does this workflow require labeled anomalies?

No. The focus is on unsupervised anomaly detection, where the workflow learns normal behavior from historical data and flags deviations as anomalies.

How are anomalies visualized in KNIME?

You can visualize anomalies on time series plots, control charts, or dashboards that highlight when and where abnormal behavior occurs.

Can I combine anomaly detection with root cause analysis?

Yes. You can extend the workflow to correlate anomalies with operational events, environmental conditions, or maintenance actions to help identify root causes.

How can I deploy IoT anomaly detection in production?

Deploy the workflow as a scheduled job or service on KNIME Business Hub, or embed it in a larger IoT analytics solution that continuously monitors data streams.

Anomaly Detection on IoT Data

How This Workflow Works

This workflow analyzes time-series sensor data to detect anomalies using control chart logic. It calculates dynamic thresholds for normal behavior, flags outliers at both individual and aggregate levels, visualizes alarm events in relation to equipment breakdowns, and triggers automated actions when critical anomalies are detected.

Key Features:

Detects abnormal sensor readings using statistical thresholds
Escalates alerts when multiple sensors show anomalies simultaneously
Visualizes alarm patterns over time for easier interpretation
Automates notifications when critical conditions are met

Step-by-step:

1. Establish Normal Operating Ranges:

The workflow calculates rolling averages and standard deviations for each sensor signal, defining upper and lower control limits that represent normal conditions. These limits adapt over time, reflecting the cumulative behavior of the equipment.

2. Detect and Flag Anomalies:

Each sensor reading is compared against its calculated normal range. If a value falls outside these bounds, the workflow flags it as a first-level anomaly. This process runs across all relevant frequency bands for each sensor.

3. Aggregate and Escalate Alarms:

The workflow checks if a significant portion of sensors—specifically, at least 25%—show anomalies at the same moment. When this threshold is crossed, it raises a second-level alarm, indicating a broader or more severe issue.

4. Visualize and Share Insights:

Second-level alarms are plotted over time, allowing users to see how these critical events align with equipment breakdowns or other operational milestones. The workflow can also generate reports and trigger automated notifications when a second-level alarm is active.