Considering the models that apply the contextual information of time-series data could improve the fault diagnosis performance, some neural network structures such as RNN, LSTM, and GRU were proposed to model the fault diagnosis effectively. However, these models are restricted by their serial computation and hence cannot achieve high diagnostic efficiency. Also the parallel CNN is difficult to implement fault diagnosis in an efficient way because it requires larger convolution kernels or deep structure to achieve long-term feature extraction capabilities. Besides, BERT model applies absolute position embedding to introduce contextual information to the model, which would bring noise to the raw data and therefore cannot be applied to fault diagnosis directly. In order to address the above problems, a fault diagnosis model named deep parallel time-series relation network(DPTRN) has been proposed in this paper. There are mainly three advantages for DPTRN: (1) Our proposed time relationship unit is based on full multilayer perceptron(MLP) structure, therefore, DPTRN performs fault diagnosis in a parallel way and improves computing efficiency significantly. (2) By improving the absolute position embedding, our novel decoupling position embedding unit could be applied on the fault diagnosis directly and learn contextual information. (3) Our proposed DPTRN has obvious advantage in feature interpretability. We confirm the effect of the proposed method on four datasets, and the results show the effectiveness, efficiency and interpretability of the proposed DPTRN model.
翻译:考虑到应用时间序列数据背景信息的模型,可以改善错误诊断性能,一些神经网络结构,如RNN、LSTM和GRU等神经网络结构被提议有效模拟错误诊断,然而,这些模型受到序列计算的限制,因此无法达到高诊断效率。此外,平行的CNN很难以有效的方式进行错误诊断,因为它需要更大的混凝土内核或深层结构来实现长期特征提取能力。此外,BERT模型采用绝对位置嵌入模型以引入背景信息,这将给原始数据带来噪音,因此无法直接应用于错误诊断。为了解决上述问题,本文提出了一个名为深度平行时间序列关系网络(DPTRN)的错误诊断模型。 DPTRN主要有三个优点:(1) 我们拟议的时间关系单元基于全多层透视镜(MLP)结构,因此,DPTRN同时进行错误诊断并大幅提高计算效率。 (2) 通过改进绝对位置嵌入原始数据的绝对位置,我们新的分解型单位嵌入错误后无法直接应用。