Federal University of Pernambuco, Recife 50740-550, Brazil.
Universidad Politécnica Salesiana, Cuenca 010105, Ecuador.
Comput Intell Neurosci. 2019 Feb 3;2019:1383752. doi: 10.1155/2019/1383752. eCollection 2019.
Gearboxes are mechanical devices that play an essential role in several applications, e.g., the transmission of automotive vehicles. Their malfunctioning may result in economic losses and accidents, among others. The rise of powerful graphical processing units spreads the use of deep learning-based solutions to many problems, which includes the fault diagnosis on gearboxes. Those solutions usually require a significant amount of data, high computational power, and a long training process. The training of deep learning-based systems may not be feasible when GPUs are not available. This paper proposes a solution to reduce the training time of deep learning-based fault diagnosis systems without compromising their accuracy. The solution is based on the use of a decision stage to interpret all the probability outputs of a classifier whose output layer has the softmax activation function. Two classification algorithms were applied to perform the decision. We have reduced the training time by almost 80% without compromising the average accuracy of the fault diagnosis system.
齿轮箱是在许多应用中起着重要作用的机械装置，例如汽车的变速器。它们的故障可能会导致经济损失和事故等后果。功能强大的图形处理单元的出现将基于深度学习的解决方案应用于许多问题，包括齿轮箱的故障诊断。这些解决方案通常需要大量数据、高计算能力和长时间的训练过程。当没有 GPU 时，基于深度学习的系统的训练可能不可行。本文提出了一种解决方案，可以在不影响准确性的情况下减少基于深度学习的故障诊断系统的训练时间。该解决方案基于使用决策阶段来解释具有 softmax 激活函数的输出层的分类器的所有概率输出。应用了两种分类算法来执行决策。我们将训练时间缩短了近 80%，而故障诊断系统的平均准确性没有受到影响。
