In recent years, several time-frequency representation (TFR) and convolutional neural network- (CNN-) based approaches have been proposed to provide reliable remaining useful life (RUL) estimation for bearings. However, existing methods cannot tackle the spatiotemporal continuity between adjacent TFRs since temporal proposals are considered individually and their temporal dependencies are neglected. In allusion to this problem, a novel prognostic approach based on discrete cosine transformation (DCT) and temporal adjacent convolutional neural network (TACNN) is proposed. Wavelet transform (WT) is applied to effectively map the raw signals to the time frequency domain. Considering the high load and complexity of model computation, bilinear interpolation and DCT algorithm are introduced to convert TFRs into low-dimensional DCT spectrum coding matrix with strong sparsity. Furthermore, the TACNN model is proposed which is capable of learning discriminative features for temporal adjacent DCT spectrum coding matrix. Effectiveness of the proposed method is verified on the PRONOSTIA dataset, and experiment results show that the proposed model is able to realize automatic high-precision estimation of bearings RUL with high efficiency.

中文翻译：

---

基于离散余弦变换和基于时间相邻卷积神经网络的轴承剩余使用寿命估计

近年来，已经提出了几种基于时频表示（TFR）和卷积神经网络（CNN）的方法，以提供可靠的轴承剩余使用寿命（RUL）估计。但是，现有方法无法解决相邻TFR之间的时空连续性，因为时间建议被单独考虑并且其时间依赖性被忽略。针对这一问题，提出了一种基于离散余弦变换（DCT）和时间相邻卷积神经网络（TACNN）的预后方法。应用小波变换（WT）可以有效地将原始信号映射到时频域。考虑到模型计算的高负荷和复杂性，引入双线性插值和DCT算法将TFR转换为具有稀疏性的低维DCT频谱编码矩阵。此外，提出了TACNN模型，该模型能够学习时间相邻DCT频谱编码矩阵的判别特征。在PRONOSTIA数据集上验证了该方法的有效性，实验结果表明，该模型能够高效，高效地实现轴承RUL的自动高精度估计。