Traditional neural networks generally neglect the primary and secondary relationships of input information and process the information indiscriminately, which leads to their bad nonlinear approximation capacity and low generalization ability. As a result, traditional neural networks always show poor prediction accuracy in the performance degradation trend prediction of rotating machinery (RM). In view of this, a novel neural network called quantum recurrent encoder–decoder neural network (QREDNN) is proposed in this paper. In QREDNN, the attention mechanism is used to simultaneously reconstruct encoder and decoder of QREDNN, so that QREDNN can fully excavate and pay attention to important information but suppress the interference of redundant information to obtain better nonlinear approximation capacity. On the other hand, the quantum neuron is used to construct a new quantum gated recurrent unit (QGRU) in which activation values and weights are represented by quantum rotation matrices. The QGRU can traverse the solution space more finely and has a lot of multiple attractors, so it can replace the traditional recurrent unit of the encoder and decoder and enhance the generalization ability and response speed of QREDNN. Moreover, the Levenberg–Marquardt (LM) algorithm is introduced to improve the update speeds of the rotation angles of quantum rotation matrices and the attention parameters of QREDNN. Based on the superiorities of QREDNN, a new performance trend prediction method for RM is proposed, in which the denoised fuzzy entropy (DFE) of vibration acceleration signal of RM is input into QREDNN as the performance degradation feature for predicting the performance degradation trend of RM. The examples of predicting the performance trend of rolling bearings demonstrate the effectiveness of our proposed method.

传统的神经网络通常会忽略输入信息的主要和次要关系，并且会不加区别地处理信息，这导致它们的非线性逼近能力很差，泛化能力很低。结果，传统的神经网络在旋转机械（RM）的性能下降趋势预测中总是显示出较差的预测精度。有鉴于此，本文提出了一种新颖的神经网络，称为量子递归编码器-解码器神经网络（QREDNN）。在QREDNN中，注意力机制用于同时重建QREDNN的编码器和解码器，以便QREDNN可以充分挖掘和关注重要信息，而抑制冗余信息的干扰以获得​​更好的非线性逼近能力。另一方面，量子神经元用于构建新的量子门控循环单元（QGRU），其中激活值和权重由量子旋转矩阵表示。QGRU可以更精细地遍历解空间，并且具有多个吸引子，因此它可以代替传统的编码器和解码器递归单元，并增强QREDNN的泛化能力和响应速度。此外，引入了Levenberg-Marquardt（LM）算法来提高量子旋转矩阵的旋转角和QREDNN的注意力参数的更新速度。基于QREDNN的优势，提出了一种新的RM性能趋势预测方法，其中，将RM的振动加速度信号的去噪模糊熵（DFE）输入到QREDNN中，作为预测RM的性能下降趋势的性能下降特征。预测滚动轴承性能趋势的实例证明了我们提出的方法的有效性。