Summary of Using Machine Learning For Fault Detection in Lighthouse Light Sensors, by Michael Kampouridis and Nikolaos Vastardis and George Rayment
Using machine learning for fault detection in lighthouse light sensors
by Michael Kampouridis, Nikolaos Vastardis, George Rayment
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed machine learning-based approach aims to automate the detection of malfunctions in photoresistor sensors used in lighthouses, which can cause misalignment of the light’s operational timing. The method evaluates four algorithms: decision trees, random forest, extreme gradient boosting, and multi-layer perceptron. The results show that the multi-layer perceptron is the most effective, capable of detecting timing discrepancies as small as 10-15 minutes. This high accuracy makes it a highly efficient tool for fault detection in lighthouse light sensors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Lighthouses are important for maritime safety by warning of dangers like coastlines and rocks. They work with photoresistor sensors that change their light based on time. But sometimes these sensors can fail, causing the light to be out of sync. This paper uses machine learning to find a way to automatically detect when this happens. The researchers tested four different methods: decision trees, random forest, extreme gradient boosting, and multi-layer perceptron. They found that the multi-layer perceptron worked best, able to spot small mistakes in the timing. |
Keywords
» Artificial intelligence » Extreme gradient boosting » Machine learning » Random forest
