The MQTT protocol is widely used in IoT applications because of its simplicity and ability to connect different data sources to applications using a publish/subscribe model. While many MQTT brokers support persistent sessions and can store message history while an MQTT client is unavailable, there may be cases where data needs to be stored for a longer period of time. In such cases it is recommended to use a time series database. There are many options available, but if you need to store unstructured data such as images, sensor data or Protobuf messages, you should consider using ReductStore as a MQTT database. It is a time series database specifically designed to store large amounts of unstructured data, optimised for IoT and edge computing.

ReductStore provides client SDKs for many programming languages to integrate it into your infrastructure. For this example, we will use the JavaScript client SDK.

Let's build a simple application to understand how to keep a history of MQTT messages using ReductStore and Node.js.

A pre-trained model is a neural network that has already been trained on a large dataset to perform specific tasks, such as image classification or object detection. These models are highly valuable, allowing us to build on previous knowledge rather than starting from scratch.

However, computer vision models often require large datasets of labeled images or videos, which can quickly become challenging to manage, especially when sourced from continuous data streams. ReductStore addresses this need by providing an efficient and reliable time-series object store capable of handling large volumes of high-frequency, unstructured data such as video streams or labeled images. For practical guidance on implementing ReductStore and integrating it with Roboflow to develop high-performing computer vision models, refer to the guide: Computer Vision Made Simple with ReductStore and Roboflow.

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.