jiaxiang-cheng/PyTorch-CNN-for-RUL-Prediction
PyTorch implementation of CNN for remaining useful life prediction. Inspired by Babu, G. S., Zhao, P., & Li, X. L. (2016, April). Deep convolutional neural network-based regression approach for estimation of remaining useful life. In International conference on database systems for advanced applications (pp. 214-228). Springer, Cham.
This project helps operations engineers and maintenance managers predict when industrial equipment is likely to fail. By analyzing sensor data collected over time, it forecasts the remaining useful life of machinery, allowing for proactive maintenance scheduling. The output is a clear estimation of how much longer a piece of equipment can operate reliably.
101 stars. No commits in the last 6 months.
Use this if you manage expensive industrial machinery and need to predict equipment failure to optimize maintenance schedules and prevent unexpected downtime.
Not ideal if you are looking for a plug-and-play solution without any custom sensor data or an understanding of machine learning concepts.
Stars
101
Forks
18
Language
Python
License
Apache-2.0
Category
Last pushed
Jul 05, 2021
Commits (30d)
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