  Artificial neural network has achieved the state-of-art performance in fault
detection on the Tennessee Eastman process, but it often requires enormous
memory to fund its massive parameters. In order to implement online real-time
fault detection, three deep compression techniques (pruning, clustering, and
quantization) are applied to reduce the computational burden. We have
extensively studied 7 different combinations of compression techniques, all
methods achieve high model compression rates over 64% while maintain high fault
detection accuracy. The best result is applying all three techniques, which
reduces the model sizes by 91.5% and remains a high accuracy over 94%. This
result leads to a smaller storage requirement in production environments, and
makes the deployment smoother in real world.
