3 posts tagged with "vibration sensor"

This is a complete guide to storing vibration sensor data efficiently and effectively. We'll cover everything from the basics of vibration data to best practices for managing it as well as setting up a robust and scalable environment to store, query, and replicate vibration sensor data.

Vibration data is typically collected from sensors attached to machinery or equipment to monitor its health and performance. This data can be used to detect anomalies, predict failures, and optimize maintenance schedules.

However, effectively managing vibration data can be challenging due to its high frequency, large volume, and complex nature. To address these challenges, we must implement efficient storage strategies that balance data retention with storage constraints.

After covering the basics of vibration data, we'll explore the best practices for managing this data, including storing both raw and pre-processed metrics to take advantage of their benefits. We'll also look at the differences between traditional time series databases and a time series object store such as ReductStore, which is designed to efficiently handle time series unstructured data, making it an excellent choice for storing high-frequency vibration sensor measurements.

We'll then cover a real-world example of storing vibration sensor data using Python and ReductStore. This example will show you step-by-step how to store raw sensor data, calculate key metrics, and query and retrieve this data for analysis.

Finally, we'll discuss strategies for preventing data loss through volume-based retention policies and automated replication to ensure that valuable information is always available for diagnosis and analysis.

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.

In How to Store Vibration Sensor Data, we discuss the importance of efficiently storing both raw vibration data and pre-processed metrics, and the benefits of using time-series databases such as ReductStore. We explore best practices for setting up a time-series database and implementing data retention policies to effectively manage high-frequency sensor data.

We also see how to store vibration sensor values in 1-second chunks, each packaged as binary data, to optimize the storage process when dealing with high-frequency data such as vibration or acoustic measurements.

In this post, we compare ReductStore and InfluxDB in a real-world benchmark scenario, focusing on their write and read performance for high-frequency sensor data. We show how ReductStore's binary storage provides superior efficiency and scalability over InfluxDB when handling large volumes of unstructured time-series data.

The benchmark was run on an SSD drive, but results may vary depending on hardware configuration and database settings; to explore how it performs on your setup, you can run the benchmark yourself using the Reduct Vibration Example repository on GitHub.