Anomaly Detection in Manufacturing Data with RioDB

In modern manufacturing, downtime is costly—one unexpected machine failure can lead to thousands of dollars in lost production and delayed schedules. With IoT-enabled CNC machines generating high-frequency sensor data, detecting early warning signs of issues like overheating, tool wear, or excessive vibration is essential to maintaining smooth operations.

Traditional batch processing methods often miss critical patterns because they analyze data after the fact, introducing delays that make it impossible to address issues as they occur. This delay prevents real-time responses to anomalies like temperature spikes or equipment failures, which could be mitigated with continuous monitoring. What’s needed is a system that continuously monitors data in real time and identifies anomalies as they happen, enabling instant responses to prevent failures, reduce maintenance costs, and improve overall efficiency.

In this post, we will explore the case of Precision Aerospace Manufacturing Inc., which is utilizing 10 CNC machines, including DMG MORI NHX 5000 and Haas VF-4, in its production. These machines are critical for manufacturing high-precision aerospace components, where operational uptime and part quality are essential. We will analyze how real-time monitoring with RioDB can help identify and address potential anomalies in their operations.

Data Example: Monitoring CNC Machine Parameters

To understand the impact of real-time monitoring, let’s consider a dataset collected from Aerospace Manufacturing CNC machines. These machines generate high-frequency sensor data every second, monitoring key parameters:

• Temperature (°F): Tracks spindle and tool temperatures to detect overheating.
• Pressure (psi): Measures pneumatic or hydraulic systems to identify leaks or failures.
• Vibration (g): Monitors machine stability to detect misalignment or tool imbalance.
• Tool Wear (mm): Tracks gradual wear to predict tool failure.

For instance, a DMG MORI NHX 5000 machine sends 10 sensor records per second, each record containing the following fields:

Full Data Record Example:

{
  "Timestamp": "2025-01-01T09:00:00.000Z",
  "Machine_ID": "DMG-NHX5000-01",
  "Temperature (°F)": 122.4,
  "Pressure (psi)": 30.0,
  "Vibration (g)": 0.5,
  "Tool Wear (mm)": 0.0012,
  "Spindle_Speed (RPM)": 4500,
  "Feed_Rate (mm/min)": 1200,
  "Power_Consumption (kW)": 15.2,
  "Cycle_Status": "In Progress",
  "Operator_ID": "OP-42"
}

Here is a snapshot of the dataset:

Timestamp	Temperature(°F)	Pressure(psi)	Vibration(g)	Tool Wear(mm)
2025-01-01 09:00:00.0	122.4	30.0	0.5	0.0012
2025-01-01 09:00:01.0	125.6	30.1	0.6	0.0012
2025-01-01 09:00:02.0	123.1	30.0	0.4	0.0013

This continuous stream of data provides invaluable insights into machine performance, but without real-time analytics, critical anomalies could be missed, leading to unplanned downtime and increased costs.

The Challenge: Identifying Anomalies in Real Time

With thousands of records generated daily by multiple CNC machines, detecting anomalies like temperature spikes, excessive vibration, or tool wear becomes challenging. Traditional batch processing systems introduce delays, making it impossible to address issues as they occur. Missed anomalies can lead to:

1. Production Halts: Undetected overheating or tool failure can cause sudden machine stoppages.
2. Quality Issues: Subtle vibration or wear anomalies might compromise part accuracy.
3. Safety Hazards: Excessive vibration could destabilize the machine, risking operator safety.
4. High Maintenance Costs: Reactive maintenance often costs more than proactive interventions.

Real-time analytics, such as those provided by RioDB, are essential for overcoming these challenges. By continuously processing incoming data, RioDB can detect anomalies the moment they occur, ensuring immediate alerts and actionable insights.

RioDB Features: Sliding Window Processing for Real-Time Anomaly Detection

RioDB’s sliding window processing is a powerful feature that enables anomaly detection by aggregating and analyzing data streams over specified time intervals. 

Key Features of Sliding Window Processing:

1. Custom Time Intervals: Define windows (e.g., 10 seconds) to analyze sensor data in manageable chunks.
2. Aggregation Functions: Calculate metrics like max temperature, average tool wear, or sum of vibrations within each window.
3. Real-Time Alerts: Automatically flag anomalies as soon as thresholds are exceeded.

Example Use Case: For monitoring spindle temperature:

• Set a sliding window of 10 seconds.
• Calculate the maximum temperature in each window.
• Flag a window as anomalous if the max temperature exceeds 150°F.

Let’s configure RioDb to track temperature data first. We will configure the stream first with the command:

CREATE STREAM temperature_anomalies (
   time TIMESTAMP,
   machine_id STRING,
   temperature NUMBER
)
INPUT udp (
   port 5050
)
PARSER delimited (
   delimiter ','
);

Now, let’s declare a window for stream analysis. For example, to calculate statistics like minimum, maximum, and average temperature over a 5-minute range, we can declare a window as follows:

CREATE WINDOW last_5min_temperature
RUNNING min, max, avg
FROM temperature_anomalies.temperature
RANGE 5m;

This window processes incoming data in real-time by calculating the minimum, maximum, and average values for temperature over the last 5 minutes.

After defining a window, we define a query to extract insights from the data. For example, to compute additional metrics and enrich the output:

SELECT
 st.time,
 st.machine_id,
 st.temperature AS current_temperature,
 wnd.max AS max_temp,
 wnd.avg AS avg_temp
FROM temperature_anomalies st, last_5min_temperature wnd
WHEN st.temperature > 150
 OR wnd.max > 160
 OR wnd.avg > 140;

This query processes data to detect anomalies based on the current temperature, the maximum, and average values over the last 5-minute window. The temperature AS current_temperature field captures the immediate reading, while max_temp and avg_temp identify trends over time. The WHEN clause ensures only significant anomalies are flagged, such as when the current temperature exceeds 150°F, and both the maximum and average values indicate a sustained issue.

To complete the data pipeline, we define an output for the query. The final query now includes an output clause to send results directly to an external system:

SELECT
 st.time,
 st.machine_id,
 st.temperature AS current_temperature,
 wnd.max AS max_temp,
 wnd.avg AS avg_temp
FROM temperature_anomalies st, last_5min_temperature wnd
WHEN st.temperature > 150
 OR wnd.max > 160
 OR wnd.avg > 140
OUTPUT HTTP (
   url 'http://alerts.precision-aero.com:8080/anomalies',
   content_type   'application/json'
   method         'POST'
   timeout        60
);

The OUTPUT clause in this query ensures that detected anomalies are sent directly to an external HTTP endpoint. The url specifies the target location where results are sent, such as an alert management system or dashboard. Fields like maximum and average temperature values ensure the receiving system has all the necessary context for further action. This setup enables integration with real-time monitoring tools or predictive maintenance workflows.

RioDB now detects overheating as it happens in the input stream data. After detection, flagged data can be visualized in dashboards or forwarded to maintenance systems for immediate action. For example, operators might receive alerts via email or Slack, and the anomaly data can be integrated with predictive maintenance tools to schedule repairs or replacements. This real-time response ensures operational continuity and prevents potential failures.