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首页> 外文期刊>Mechanical systems and signal processing >Multi-layer gated temporal convolution network for residual useful life prediction of rotating machinery
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Multi-layer gated temporal convolution network for residual useful life prediction of rotating machinery

机译：用于旋转机械剩余使用寿命预测的多层门控时间卷积网络

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摘要

The classical recurrent neural networks (RNNs) face the defect oflong-term dependence in the prediction of time series and thus have a poor generalization ability. In the meantime, their loss functions are generally obtained by means of traversing the whole training data set for supervised learning, which increases the time complexity and space complexity. As a result, classical RNNs usually show poor prediction accuracy and undesirable computation efficiency in the prediction of residual useful life (RUL) of rotating machinery (RM). In view of this, a multi-layer gated temporal convolution network (MLGTCN) is proposed to predict RUL of RM in this paper. In our proposed MLGTCN, a multi-layer temporal convolution network structure is innovatively constructed to perform convolution operations on input data for expanding the receptive field, and the long-term historical information can be traced, which solves the problem of long-term dependence and enhances the generalization ability of MLGTCN. Moreover, the gated linear units (GLUs) are creatively constructed to filter out the important information hierarchically, which endows MLGTCN with a high nonlinear approximation capability. Additionally, in order to improve the global optimization ability and convergence speed, a reinforcement learning algorithm based on semi-gradient temporal difference (semi-gradient TD) is adopted and a novel action controller is designed for updating the convolution kernels and bias values of MLGTCN, which can use the increment information between the successive predicted values, thus rapidly approaching the optimal strategy. Owing to the above MLGTCN's advantages, high prediction accuracy and desirable computation efficiency can be achieved in the RUL prediction of RM. The effectiveness of our proposed method is experimentally validated with the RUL prediction of double-row roller bearings.
机译：经典复发性神经网络（RNNS）面临延伸期依赖性的缺陷，在时间序列的预测中，因此具有差的概括能力。同时，它们的损失函数通常通过遍历用于监督学习的整个训练数据集来获得，这增加了时间复杂性和空间复杂性。结果，经典RNN通常在旋转机械（RM）的剩余使用寿命（RUL）预测中显示出较差的预测精度和不期望的计算效率。鉴于此，提出了一种多层门控时间卷积网络（MLGTCN）以预测本文的RM RM。在我们提出的MLGTCN中，创新地构造了多层时间卷积网络结构，以对扩展接收领域的输入数据执行卷积操作，并且可以跟踪长期历史信息，这解决了长期依赖性的问题和提高MLGTCN的泛化能力。此外，创造性地构造出门控线性单元（GLU）以分层地滤除重要信息，其具有高非线性近似能力的MLGTCN。另外，为了提高全局优化能力和收敛速度，采用基于半梯度时间差（半梯度Td）的加强学习算法，并且设计了一种新颖的动作控制器，用于更新MLGTCN的卷积核和偏置值，这可以使用连续预测值之间的增量信息，从而快速接近最佳策略。由于上述MLGTCN的优点，在RM的RUL预测中可以实现高预测精度和所需的计算效率。我们所提出的方法的有效性是通过双排滚子轴承的鲁尔预测进行实验验证的。

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