1. IO-Link Sensor / Smart Meter
Acquires process data (vibration, temperature, pressure, etc.)
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Scheduled maintenance cycles and reactive repairs only get you so far in today’s industrial environments. Especially in high-throughput operations, where equipment reliability and uptime have never mattered more, these traditional ways of maintaining production assets can’t keep pace with modern performance demands.
That’s why predictive maintenance is gaining traction across industries ranging from automotive to food and beverage. Because it continuously analyzes equipment and process data, the benefits of predictive maintenance extend across performance, reliability and cost. It helps organizations minimize downtime, lower maintenance expenses and extend equipment life. How? By giving teams the capability to anticipate issues before they become failures.
It also generates measurable improvements like:
- Less downtime
- Increased capacity
- Better use of resources
- Improved maintenance planning
- Optimized spare part logistics
Breaking down barriers to predictive maintenance
If you want to reap the benefits of predictive maintenance, organizations require contextualized operational data. To achieve this, AWS users have traditionally built anomaly detection solutions by combining two components:
- AWS IoT SiteWise as the industrial data backbone for structured and contextualized data
- An AWS AI/ML service, such as Amazon Lookout for Equipment or Amazon Sagemaker, to build, train and deploy anomaly detection models
This approach, while common, requires months of development effort and specialized machine learning expertise—limitations that make it difficult to demonstrate early ROI and scale quickly across global sites.
Bring the benefits of predictive maintenance to life
For organizations that want to realize the business value of predictive maintenance faster, there’s a simpler option: the Native Anomaly Detection feature in AWS IoT SiteWise. It offers a fully managed, plug-and-play solution for data pre-processing, model training and inferencing workflows. It dramatically reduces the time and expertise needed to deploy machine learning for industrial assets.
Making predictive maintenance easier to deploy
Announced by AWS in July 2025, the Native Anomaly Detection feature brings built-in machine learning directly into AWS IoT SiteWise for automated detection of equipment issues and performance anomalies.
Companies can now easily collect data from a wide range of equipment, build digital asset models and process information in near-real-time, all while integrating seamlessly with other AWS tools. Within minutes, engineering teams can configure asset properties and data sources to detect anomalies using a simple, no-code configuration workflow.
This approach addresses two of the biggest barriers that organizations must overcome before they can experience the benefits of predictive maintenance:
- It eliminates the need for specialized machine learning knowledge by embedding no-code workflows directly into SiteWise.
- It offers flexibility to control monitoring frequency so teams can better optimize costs and performance.
How native anomaly detection works in practice
The Native Anomaly Detection feature in AWS IoT SiteWise enables organizations to act on equipment data faster and more effectively than ever before.
This empowers them to:
- Move from raw machine data to usable insights in days instead of months by creating and training anomaly detection models from SiteWise data using SiteWise Monitor or the console
- Monitor asset health and performance by evaluating live data streams from industrial assets against automatically learned, “normal” behavior patterns
- Pinpoint unusual equipment conditions faster and process deviations in real-time by generating anomaly scores that spot anomalies right away
- Reduce overhead by simplifying data ingestion, model lifecycle management and inference monitoring, all within the unified SiteWise environment
AWS + CloudRail streamline industrial data integration
The capabilities of AWS IoT SiteWise Native Anomaly Detection become even more powerful when combined with Belden’s CloudRail. It seamlessly integrates with AWS IoT SiteWise so you can efficiently connect sensors, smart meters and machines into a standardized SiteWise data model, even in brownfield environments.
This makes it simple to feed the AWS Native Anomaly Detection feature with structured, high-quality industrial data so you can unlock actionable insights faster, helping you connect to what’s possible.
The integration between CloudRail and AWS IoT SiteWise service uses a standard asset model and automatic hierarchy creation that makes it fast and easy to get your first machine online within hours and scale rapidly throughout the factory.
Belden connects the factory floor to AWS IoT SiteWise
When combined with OT data applications like CloudRail, Belden’s complete connection solutions offer an out-of-the-box way to connect industrial sensors within minutes to AWS IoT SiteWise.
Automatic data modeling enables cloud architects and IT teams to feed data into the AWS IoT SiteWise Native Anomaly Detection service to immediately start monitoring critical assets. By automating model selection, training, evaluation and results integration, customers can rapidly scale AI/ML applications.
This enables you to enjoy the benefits of predictive maintenance with reduced effort and cost and a clearer ROI.
How Belden + AWS enable the new Native Anomaly Detection service
Get to know the basic building blocks involved in this predictive maintenance journey, from physical sensors on machines to cloud-based analytics and anomaly detection.
5. AWS IoT SiteWise Platform
Collects, models, stores and analyzes data, relying on built-in ML to detect abnormal conditions
2. Belden Hardware
Moves data from the shop floor to the cloud securely and reliably through cables, IO-Link masters, gateways, firewalls, etc.
3. Belden Services
Installation, assessment and support so infrastructure can handle data capture and transmission to AWS
4. CloudRail Software
Organizes sensor data so AWS can use it effectively
Belden can help you connect new machines using standard protocols like OPC UA, Profinet and Ethernet/IP, as well as legacy equipment from various industrial manufacturers like Pepperl+Fuchs, SICK, Balluff, Siemens or Schneider Electric. This is achieved through a secondary sensor retrofitting approach utilizing IO-Link protocol and the sensor-to-AWS solution from CloudRail.
Ready to reduce downtime and increase profits? Get in touch with Belden to connect your industrial assets for quick wins and to scale the benefits of predictive maintenance throughout your global fleet of equipment.