Summary of Fault Diagnosis on Induction Motor Using Machine Learning and Signal Processing, by Muhammad Samiullah et al.
Fault Diagnosis on Induction Motor using Machine Learning and Signal Processing
by Muhammad Samiullah, Hasan Ali, Shehryar Zahoor, Anas Ali
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a study on detecting and identifying induction motor faults using machine learning and signal processing. The researchers developed a model of a three-phase induction motor in MATLAB Simulink to generate healthy and faulty data, including stator currents, rotor currents, input power, slip, rotor speed, and efficiency. They created four types of faults: open circuit, short circuit, overload, and broken rotor bars, and collected 150,000 data points with a 60-40% ratio of healthy to faulty data. The team applied Fast Fourier Transform (FFT) to detect and identify healthy and unhealthy conditions and added a distinctive feature in the data. They trained different machine learning models and found that the Decision Tree algorithm performed best with an accuracy of about 92%. This study contributes to the literature by providing a valuable approach to fault detection and classification for industrial applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is all about finding problems in machines called induction motors, which are important in factories. The researchers used special software to make a fake motor that could do healthy things and unhealthy things, like getting broken or overheated. They collected lots of data on what the motor was doing when it was healthy or not healthy. Then they used computers to look at the data and figure out what was wrong with the motor. They tried different ways of doing this and found that one way, called a Decision Tree, worked really well – about 92% of the time! This is helpful because it can help prevent machines from breaking down or getting damaged. |
Keywords
* Artificial intelligence * Classification * Decision tree * Machine learning * Signal processing
