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A Deep Learning Model with Adaptive Learning Rate for Fault Diagnosis

机译：具有故障诊断的自适应学习率的深度学习模型

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摘要

With the increasing amount of data in the field of equipment fault diagnosis, deep learning is playing an increasingly important role in the process of fault diagnosis, during which the timeliness requirement is high and the fault diagnosis results need to be obtained accurately and timely. However, with the increase of network layers, the training time of deep learning model becomes longer. Learning rate in the deep learning model plays an important role in the process of model training, and a well-designed learning rate adjustment strategy can effectively reduce the training time and satisfy the requirements of fault diagnosis. At present, some deep learning models usually adopt a globally uniform learning rate strategy, which is unreasonable for different parameters. This paper has designed an adaptive learning rate strategy for the parameters of weight and bias respectively in deep learning model. Specifically, the strategy contains a learning rate strategy based on stochastic gradient descent method for weight, and a power exponential learning rate strategy for bias. Experiments are carried out to validate the effectiveness of proposed learning rate strategy. Results suggest that the strategy can reduce the training time and reconstruction error rate of deep learning model, and improve the classification accuracy of fault diagnosis.
机译：随着设备故障诊断领域的数据越来越多，深度学习在故障诊断过程中发挥着越来越重要的作用，在此期间，性能要求高，需要准确地和及时获得故障诊断结果。然而，随着网络层的增加，深度学习模型的训练时间变得更长。深度学习模型中的学习率在模型培训过程中起着重要作用，并且精心设计的学习率调整策略可以有效地减少训练时间并满足故障诊断的要求。目前，一些深入学习模型通常采用全球统一的学习率策略，这对于不同的参数是不合理的。本文为深度学习模型分别设计了分别为重量和偏差参数的自适应学习速率策略。具体地，该策略包含基于用于重量的随机梯度下降方法的学习速率策略，以及用于偏置的功率指数学习率策略。进行实验以验证建议的学习率策略的有效性。结果表明，该策略可以降低深度学习模型的培训时间和重建误差，提高故障诊断的分类准确性。

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