Unplanned downtime is one of the biggest challenges in modern production. Every minute a machine stands still costs money, resources, and sometimes even customer trust. Industrial anomaly detection offers a powerful way to identify deviations in machine behavior early – long before failures occur.

But how can manufacturers leverage AI-based anomaly detection without deep data science expertise? That’s exactly what MLnext Creation makes possible. This software solution empowers domain experts to create powerful machine learning models for anomaly detection, without writing a single line of code.

Why Industrial Anomaly Detection matters

In industrial environments, anomalies often indicate early signs of wear or process deviations. Traditionally, detecting these patterns requires specialized knowledge and complex tools. Something many teams simply don’t have the time or resources for.

This is where industrial anomaly detection changes the game. Instead of relying on reactive measures, modern anomaly detection for predictive maintenance focuses on learning normal behavior and identifying deviations automatically. This approach forms the foundation of AI predictive maintenance manufacturing.

From Domain Knowledge to AI Models without Coding – How MLnext Creation works

Instead of forcing users to dive into complex coding or data science, MLnext Creation offers a web-based interface designed for simplicity. You don’t need to learn programming languages or set up complicated environments.

The workflow is clear and guided. Users select relevant time-series data such as vibration, temperature, or power consumption, configure the experiment via a web-based interface, and train models automatically. This makes predictive maintenance anomaly detection accessible to OT teams, maintenance engineers, and automation experts alike.

Understanding Anomalies for Predictive Maintenance

Once trained, the models created with MLnext Creation continuously compare live process data with learned normal behavior. Deviations are detected early and visualized clearly, enabling anomaly detection predictive maintenance use cases such as identifying worn components before failure, detecting sensor drift, or recognizing unstable process conditions.

Instead of simple alarms, users gain actionable insights that support data-driven maintenance decisions.

Industrial Anomaly Detection with VAEs

Under the hood, MLnext Creation relies on advanced AI methods such as VAE anomaly detection based on Variational Autoencoders combined with time-series analysis.

Anomaly detection using variational autoencoders is particularly well suited for industrial processes with complex dependencies. These models learn compact representations of normal behavior and highlight deviations automatically. In more advanced scenarios, approaches such as LSTM VAE anomaly detection further improve detection quality for dynamic industrial systems.

Seamless Integration from Model to Shopfloor

Once a model is trained, it can be exported in the open ONNX format and deployed using MLnext Execution directly on PLCnext Control or industrial edge devices.

This ensures that industrial anomaly detection runs in real time, close to the machine, and fully integrated into existing automation environments.

From Insights to Action: Predictive Maintenance with MLnext Creation

By enabling your team to build machine learning models without programming skills, you take control of predictive maintenance. Instead of waiting for alarms or failures, you gain actionable insights that help you act before issues escalate. This means:

• Reduced downtime and costs: Detect anomalies early and prevent expensive breakdowns.
• Optimized maintenance strategies: Move from reactive to predictive maintenance, planning interventions when they’re truly needed.
• Improved equipment effectiveness: Keep your machines running at peak performance and extend their lifecycle.
• Empowered teams: Your domain experts can leverage AI without relying on external specialists, saving time and resources.

In short, MLnext bridges the gap between your operational expertise and cutting-edge AI technology. It’s not just about avoiding problems. It’s about creating a smarter, more resilient production environment that gives you a competitive edge.

Industrial Anomaly Detection in Practice

With MLnext Creation, industrial anomaly detection becomes a practical tool for everyday manufacturing challenges. Domain experts can turn their process knowledge into AI models – without writing code.

Whether you are starting your first AI predictive maintenance manufacturing project or scaling anomaly detection across multiple assets, MLnext Creation provides a clear and accessible path.

Ready to get started?

Download the demo version from the https://www.plcnextstore.com and explore how MLnext can transform your maintenance strategy.
If you want to learn more about MLnext and PLCnext Technology, feel free to read one of our latest blog posts: ‘Virtual PLC meets AI: Why software-defined control is the foundation for intelligent automation.‘

Discover more about MLnext and how it bridges the gap between domain knowledge and AI innovation.