Vibration Data Analysis Using Python
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.