predictive-maintenance-using-machine-learning | end demo architecture for predictive maintenance issues | Machine Learning library

predictive-maintenance-using-machine-learning is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Keras applications. predictive-maintenance-using-machine-learning has a Permissive License and it has low support. However predictive-maintenance-using-machine-learning has 957 bugs, it has 8 vulnerabilities and it build file is not available. You can download it from GitHub.

The project uses Amazon SageMaker to train a deep learning model with the MXNet deep learning framework. The model used is a stacked Bidirectional LSTM neural network that can learn from sequential or time series data. The model is robust to the input dataset and does not expect the sensor readings to be smoothed, as the model has 1D convolutional layers with trainable parameter that can to smooth and peform feature transformation of the time series. The deep learning model is trained so that it learns to predict the remaining useful life (RUL) for each sensor. The model training is orchestrated by running a jupyter notebook on a SageMaker Notebook instance. When you go through the project demonstration, the nasa turbofan engine dataset is automatically downloaded to an S3 bucket created in your account, by the quick launch template above. In to demonstrate how the project can be used to perform batch inference on new time series data from sensor readings, an AWS Lambda function (is included. The Lambda function can be invoked by an AWS CloudWatch Event so that it runs on a schedule or AWS S3 put event so that it runs as soon as new sensor readings are stored in S3. When invoked, the Lambda function creates a SageMaker Batch Transform job, which uses the SageMaker Model that was saved during training, to obtain model predictions for the new sensor data. The results of the batch transform job are stored back in S3, and can be fed into a dashboard or visualization module for monitoring.