With the advances in Internet-of-Things and data mining technologies, deep learning-based approaches have been widely used for intelligent fault diagnosis of manufacturing assets. However, uncertainty caused by the non-stationary process data such as vibration signal and noise interference in practical working environments will greatly affect the performance and reliability of predictions. The present paper develops a trustworthy and intelligent fault diagnosis framework based on a two-stage joint denoising method and evidential neural networks. The proposed denoising method integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the independent component analysis (ICA) method can effectively reduce data uncertainty caused by noise interference. The stacked gated recurrent unit (SGRU) model has been incorporated into the evidential neural networks as a deep classifier. The proposed evidential SGRU (ESGRU) method can quantify the prediction uncertainty, which estimates the prediction trustworthiness. Predictive entropy and reliability diagrams are used as calibration methods to validate the effectiveness of uncertainty estimation. The proposed framework is validated by two case studies of rolling bearing fault diagnosis in variable noise conditions. Experimental results demonstrate that the proposed method can achieve a high denoising effect and provide reliable uncertainty prediction results which are significant for practical applications.

随着物联网和数据挖掘技术的进步，基于深度学习的方法已广泛应用于制造资产的智能故障诊断。然而，实际工作环境中振动信号、噪声干扰等非平稳过程数据所带来的不确定性将极大地影响预测的性能和可靠性。本文开发了一种基于两阶段联合去噪方法和证据神经网络的可信且智能的故障诊断框架。所提出的将改进的完全集合经验模态分解与自适应噪声（ICEEMDAN）和独立分量分析（ICA）方法相结合的去噪方法可以有效降低噪声干扰引起的数据不确定性。堆叠门控循环单元（SGRU）模型已作为深度分类器纳入证据神经网络。所提出的证据SGRU（ESGRU）方法可以量化预测的不确定性，从而估计预测的可信度。使用预测熵和可靠性图作为校准方法来验证不确定性估计的有效性。所提出的框架通过两个可变噪声条件下滚动轴承故障诊断的案例研究得到了验证。实验结果表明，该方法能够取得较高的去噪效果，并提供可靠的不确定性预测结果，对实际应用具有重要意义。