Recorded vibrations carry extensive information about the dynamic state of a mechanical system, describing the processes within it in both direct and detailed ways. Vibration signal naturally reflects the mechanical process, often including its very subtle aspects, so its proper analysis may help to monitor not only the system’s dynamic state and operating conditions but also the physical properties and changes in its components, such as wear or faults in bearings or transmission gears, physical defects or breakage of parts, deformations and imbalance, lack of lubrication, etc.

When conducted comprehensively and involves modern ML techniques, vibration analysis approaches the skill of an experienced mechanic’s trained ear, capable of sensing a mechanism's condition through subtle, often indescribable changes in its sound.

However, let’s start with basic vibration analysis methods, specifically by calculating statistics or features that reflect certain well-interpreted properties of the process, each with clear physical meaning. This type of analysis addresses the most common questions about the process, while more subtle features - or those harder to formalize, or just specific for a particular case - may remain unqueried, despite their value for diagnostic and prognostic purposes.

High-speed performance is a must for today's computer vision applications, but it comes with many challenges. These include processing a high volume of frames per second (FPS), which requires not only fast algorithms, but also efficient data storage to handle the large quantities of images being processed in real time.

Traditional annotation methods are often time-consuming and labor-intensive for training machine learning models. In other words, they create bottlenecks that slow down projects from getting done.

At the same time, Roboflow was designed to address the challenges associated with annotating data, but manually labeling all images is often tedious and unrealistic. In this case, ReductStore can provide the tools to query, filter, and replicate specific images for further annotation and training.

In this article, we'll explain how Roboflow can help reduce the time and effort required to annotate images, and how ReductStore can be used to store and filter important images.

Deploying a vision model like YOLOv10 at the edge has become a game-changer for real-time object detection. Developed by researchers at Tsinghua University, YOLOv10 introduces architectural innovations that optimizes speed and accuracy, making it ideal for vision tasks that require low inference latency.

This article provides resources for training a YOLOv10 model and managing data storage for real-time performance on edge devices. We will look at a combination of tools, including Roboflow for dataset preparation, Ultralytics for model training, and ReductStore for efficient data storage.