AbstractWith 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.