The model is a Fully Convolutional Neural Network originally proposed in this paper. The implementation is based on the TF 2 version provided here. The hyperparameters (kernel_size, filters, the usage of BatchNorm) were found via random search using KerasTuner.
Given a time series of 500 samples, the goal is to automatically detect the presence of a specific issue with the engine.
The data used to train the model was already z-normalized: each timeseries sample has a mean equal to zero and a standard deviation equal to one.
- 3601 training instances
- 1320 testing instances
Each timeseries corresponds to a measurement of engine noise captured by a motor sensor.
The following hyperparameters were used during training:
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