An Unsupervised Noisy Sample Detection Method for Deep Learning-Based Health Status Prediction
Original price was: Rs6,500.00.US$63.49Current price is: Rs5,500.00.
PROJ20039
Description
Due to rough environment or data-acquisition equipment failure, the machine monitoring data may be contaminated with noise, which may lead to generation of noisy samples. For the widely-used deep learning-based prediction model, some noisy samples (with low intensity noise) may improve model generalization while some noisy samples (with high intensity noise) may hamper the prediction models. In this process Cmaps dataset is used to find the health prediction of the turbo engine. The dataset which is contains information about the sensors and Remaining Useful Life of the engine cycle. In this process we proposes the Deep learning method such as Long short Term Memory Neural Network (LSTM) and Artificial Neural Network (ANN) are used to test RUL of the turbo engines. The effectiveness of the proposed method is verified using a simulated turbofan engine degradation dataset and a real milling machine monitoring dataset. Finally it will generates the result in the form of metrics like accuracy, precision, recall and f1-score
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