PdM LSTM - Preventative Maintenance

Simple prototype

1) Server Details

Paste your server credentials

2) Upload sensor CSV

CSV format: timestamp,sensor1,sensor2,... — header row optional

Sequence length (timesteps):
Train epochs:

3) Model & Predictions

4) Export / Server training

If you have large datasets, train on a server (Keras) and convert to TFJS using tensorflowjs_converter. Upload model files to Firebase Storage and load in the app.

Server-side flow:
1) Train with Keras (Python) and save HDF5 or SavedModel
2) Use tensorflowjs_converter to convert to TFJS format
3) Upload generated model.json and shard files to Kumul Cloud Storage
4) Call tf.loadLayersModel() in the client