thehapyone/Predictive-Maintenance-Project
In this project, high dimensional noisy data collected from thousands of trucks during the course of 4 years was compressed using Artificial Neural Networks (ANN). This compressed meaningful information was used for performing predictive maintenance on turbochargers. Our novel deep learning ANN architecture, compressed vehicle data by over 87% while still improving fault forecasting prediction by 23% and even with extreme data size reduction of 99.7% we still see a significant performance improvement of 6.31%.
This project helps operations managers and maintenance engineers predict potential failures in vehicle components like turbochargers. It takes large amounts of sensor data from vehicles and processes it to identify patterns, then outputs precise predictions about when a part is likely to fail. The primary user is anyone responsible for fleet maintenance and vehicle reliability.
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Use this if you need to perform predictive maintenance on large fleets of vehicles, especially when dealing with high-volume sensor data that needs efficient processing and storage.
Not ideal if you are looking for a plug-and-play solution without the need for custom model training or if your primary concern isn't vehicle fault prediction.
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Sep 29, 2020
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