In the realm of manufacturing, data analysts and other stakeholders measuring factory efficiency strive to extract actionable insights and support data-driven decision-making processes, with the goal of increasing yields.
However, a crucial question arises: What data truly matters to move the needle?
The challenge of meaningful alerts
One way to think about essential data is when you are seeing data that you wouldn’t expect to see - data consistent with machines not functioning the way you would expect, or that reveals an inefficiency that should be working in a more productive way. These anomalies can then be monitored and, once detected, acted upon.
Many solutions that try to identify these anomalies are not nearly smart enough to precisely detect true anomalies, so they will detect a lot of activities that seem atypical.
However, when faced with an overwhelming number of alerts, there's a risk of fatigue, where essential notifications are ignored in a sea of blinking lights. After all, if everything is an alert, nothing is. Therefore, establishing a robust system for distinguishing between significant and trivial anomalies is paramount.
In a factory setting, too many alerts can lead to a situation where the most critical notifications are overlooked. This "alert fatigue" can undermine the effectiveness of even the most advanced monitoring systems, since they struggle to properly distinguish between alerts for behavior that doesn’t affect operations and behavior that has identified significant risk. To combat this, it's vital to have a system that not only detects anomalies but also ensures these detections are more meaningful and actionable.
This is where Viam's machine learning (ML) pipelines shine, offering a sophisticated solution for improving anomaly detection that just plugs into your existing equipment without complicated integrations.
Viam's approach to machine learning and data capture
Viam’s machine learning pipelines are designed to train models on any data you want to capture from machines, and anomaly detection is an example of how this training can be applied to turn that data into actionable insights. In factory environments, this can help enterprises to prioritize the alerts that truly signify that an event that merits attention is taking place, which helps reduce noise and improve response times.
One of the standout features of Viam is its flexibility in data capture. Instead of collecting and storing tons of image data that doesn’t have anything going on in it, Viam allows you to selectively capture the most important data that is crucial for your operations. This targeted approach ensures that the models you develop are both relevant and efficient.
From data analysis to model deployment
The process of anomaly detection using Viam involves several key steps:
- Data Analysis: Begin by analyzing the data to identify patterns and anomalies. Viam can integrate with any third-party tool for easy querying or visualization to help sift through large datasets to pinpoint what matters most.
- Model Training: Once the data is analyzed, the next step is training a machine learning model on this data. Viam provides guidance and tools for training models, ensuring they are tailored to your specific needs.
- Model Application: After training, apply the model to your machines to start detecting anomalies. Viam's platform supports easy deployment, allowing you to monitor machine events in real-time.
Viam also offers a Modular Registry where you can access pre-built ML model for deployment to a machine. This feature allows you to quickly deploy off-the-shelf models as needed, streamlining the process of implementing machine learning in your factory.
Use cases for Viam's ML pipelines
Viam's machine learning solutions can be applied across various use cases in manufacturing:
- Process Improvements: Optimize production processes by identifying inefficiencies and bottlenecks.
- Quality Control: Enhance product quality by detecting defects and variations early in the production cycle.
- Automating Robotics: Improve the performance and reliability of robotic systems through predictive maintenance and anomaly detection.
- Security Enhancement: Monitor and secure factory environments by detecting unauthorized activities and potential threats.
- Product Development: Use insights from anomaly detection to refine and innovate product designs and manufacturing techniques.
By utilizing Viam's machine learning pipelines, factories can significantly improve the detection of meaningful anomalies. This leads to better decision-making, reduced alert fatigue, and enhanced operational efficiency. With flexible data capture, robust model training, easy deployment, and the ability to use any ML model through the Modular Registry, Viam provides a comprehensive solution for modern manufacturing challenges. For more detailed guides and tutorials on implementing these solutions, visit Viam's documentation and blog.
