Chen Dongdong, Chen Minghui, Lang Binxin, Wang Xiaoqing, Xu Qiang, Shen Jiong, Liang Lihua, Luo Qin
Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang Province, Zhejiang Academy of Special Equipment Science, Hangzhou 310020, China.
College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Sensors (Basel). 2025 Aug 22;25(17):5219. doi: 10.3390/s25175219.
To address the challenges of weak early-stage loosening fault signals and strong environmental noise interference in escalator drive mainframe anchor bolts, which hinder effective fault feature extraction, this paper proposes an improved Residual Convolutional Denoising Autoencoder (RCDAE) for signal denoising in high-intensity noise environments. The model combines DMS (Dynamically Multimodal Synergistic) loss function, the gated residual mechanism, and CNN-Transformer. The experimental results demonstrate that the proposed model achieves an average accuracy of 93.88% under noise intensities ranging from 10 dB to -10 dB, representing a 2.65% improvement over the baseline model without the improved RCDAE (91.23%). At the same time, in order to verify the generalization performance of the model, the CWRU bearing data set is used to conduct experiments under the same conditions. The experimental results show that the accuracy of the proposed model is 1.30% higher than that of the baseline model without improved RCDAE, validating the method's significant advantages in noise suppression and feature representation. This study provides an effective solution for loosening fault diagnosis of escalator drive mainframe anchor bolts.
为解决自动扶梯驱动主机地脚螺栓早期松动故障信号微弱、环境噪声干扰强烈，从而阻碍有效故障特征提取的问题，本文提出一种改进的残差卷积去噪自编码器（RCDAE），用于在高强度噪声环境下进行信号去噪。该模型结合了动态多模态协同（DMS）损失函数、门控残差机制和卷积神经网络-Transformer。实验结果表明，所提出的模型在10 dB至-10 dB的噪声强度下平均准确率达到93.88%，比未改进RCDAE的基线模型（91.23%）提高了2.65%。同时，为验证模型的泛化性能，使用西储大学（CWRU）轴承数据集在相同条件下进行实验。实验结果表明，所提出模型的准确率比未改进RCDAE的基线模型高1.30%，验证了该方法在噪声抑制和特征表示方面的显著优势。本研究为自动扶梯驱动主机地脚螺栓松动故障诊断提供了一种有效解决方案。
