Isn't an autoencoder supposed to compress the input into a lower dimension, then reconstruct (i.e. https://blog.keras.io/building-autoencoders-in-keras.html)? This model seems to project the input dimension from 52 to 100 (i.e. a higher dimension), then reconstructs which seems highly likely to overfit the training data.

In this case it appears to work, because I guess provided you have enough examples of faults and non-faults then over-fitting/memoizing the non-fault dataset is probably ok.

I do get similar results (but nowhere nears as good reconstruction) using a 16 dim hidden layer with l1 regularization. It doesn't reconstruct all the noise in the inputs, but it still produces reconstructions of the "faulty" datasets that contain higher reconstruction errors (generally offsets like your charts).