In this section, we will cover some frequently asked questions (FAQs), which will help you to extend this application:
- Can we use other deep architectures to make predictions in similar IoT settings?
Answer: Yes, using other deep architectures could be a viable option. For example, creating a convolutional-LSTM network by combining the predictive power of both CNN and LSTM layers has proven to be effective in many use cases, such as audio classification, natural language processing (NLP), and time-series forecasting.
- Sometimes we do not have enough IoT data to train the model flexibly. How can we increase the amount of training data?
Answer: There are many ways to do this. For example, we can try to generate the training set by combining all the engines data. For this, the generated CSV files for both training, testing, and RUL would be helpful. Another example might...
