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.
翻译:人工神经网络在田纳西东部进程发现故障方面达到了最先进的性能,但通常需要巨大的记忆才能为其巨大的参数提供资金。 为了实施在线实时发现故障的方法,应用了三种深压缩技术(修剪、集聚和量化)来减少计算负担。我们已经广泛研究了7种不同的压缩技术组合,所有方法都实现了64%以上的高模型压缩率,同时保持了很高的检测错误的准确性。最好的结果是应用了所有三种技术,这三种技术将模型的大小缩小了91.5%,并且仍然高度精度超过94%。这导致生产环境中的存储要求减少,并使实际世界的部署更加顺利。