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Remaining useful life prediction. Degradation path approximation (DPA) is a highly easy-to-understand and brand-new solution way for data-driven RUL prediction. Many research directions on DPA can …
Zero-shot fault diagnosis on the Tennessee–Eastman process by attribute fusion transfer. Paper: Attribute fusion transfer for zero-shot fault diagnosis
Paper: Multi-Scale Ensemble Booster for Improving Existing TSD Classifiers. We proposed a highly easy-to-use performance enhancement framework called multi-scale ensemble booster(MEB), helping exis…
The source code of paper: Trend attention fully convolutional network for remaining useful life estimation in the turbofan engine PHM of CMAPSS dataset. Signal selection, Attention mechanism, and I…
Remaining Useful Life (NASA CMAPS Dataset)
Using LSTM to predict Remaining Useful Life of CMAPSS Dataset
Fully Convlutional Neural Networks for state-of-the-art time series classification
用Tensorflow实现的深度神经网络。
Ready to use implementations of various Deep Learning algorithms using TensorFlow.
Source code for paper Classification with Costly Features using Deep Reinforcement Learning.
Keras implementation for Deep Embedding Clustering (DEC)
CNN for mechanical fault diagnosis