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Machine fault feature extractor
Give it vibration or sensor signals from a motor or pump. Get features ready for fault detection.
Machine fault feature extractor is a free AI skill for the job "Build and ship software". Give the instructions to an AI assistant that supports the required files and tools, then review its output.
Free · No account · BSD 2 Clause licence · Made by MathWorks · Level: advanced
Made by MathWorks. See the original on GitHub; we copied it as it is, with its licence.
What it does
A set of useful numbers pulled from the vibration or sensor signals of a motor, pump or other rotating machine, ready for spotting faults early. It runs in MATLAB. It follows five steps. First it cleans and prepares your signals. Then it extracts features, such as statistics and frequency measures, which also shrink huge vibration files into compact ones. Next it ranks the features and keeps the ones that best separate healthy from faulty. After that it builds a health indicator from them and finally helps you deploy the monitoring. It fits evenly sampled time data. For sorting several fault types or building anomaly detectors, it points you to other tools.
You give it
- Evenly sampled sensor data
- The machine type and sample rate
- Healthy and faulty recordings (optional)
- Where you will deploy the monitor (optional)
You get
- Extracted signal features
- A ranked, trimmed feature list
- A health indicator
- MATLAB code for the workflow
Use it when
- You have vibration data from a motor and want to detect wear.
- Your raw sensor files are too large to use directly.
- You need to pick which signal features actually predict failure.
- You want a single health score for a machine over time.
How to use it
- Add it to your AI once: download it and add the folder to Claude, ChatGPT or Codex as a skill.
- Ask for the job in your own words. Your AI opens the skill when it fits.
- It works best when your AI can run MATLAB with the Predictive Maintenance Toolbox.