Hi Dhiya,
In order to create a robust model, you will need a large set of sensor data healthy and faulty operations operating in different conditions.
If you do not enough data, as suggested in the video you can build mathematical model and estimate its parameters from the sensor data and simulate a model with different fault states in different operating conditions to generate synthetic failure data
However, the number of machine data required so that the model is robust, depends on multiple factors like model complexity, the number of features that you are using to train the model. The more important thing is to have a better quality data covering different operating conditions since a predictive model is as good as the data that is feeded while training the model.
You can refer to the following resources to understand the amout of data required to train a model:
- https://keras.io/examples/keras_recipes/sample_size_estimate/
- https://www.akkio.com/post/how-much-data-is-required-to-train-ml
I hope this helps.
