<p>The advent of deep learning technologies has opened new pathways for the fault diagnosis problem in the industrial production process. However, existing fault diagnosis methods primarily depend on large volumes of high-quality labeled data, which are often difficult and costly to obtain. Moreover, these methods may yield lead to inaccurate and unstable diagnostic results. Inspired by unsupervised learning and clustering ensemble techniques, this paper explores an effective and label-independent fault diagnosis method, termed reliability evaluation-based deep fuzzy clustering ensemble (RE-DFCE). Specifically, the multi-head attention mechanism and random mask technology are introduced to extract advanced features. Subsequently, fuzzy clustering based on the extracted advanced features is employed to describe the ambiguity and uncertainty in diagnostic data. Furthermore, a reliability evaluation method based on entropy measures is proposed to quantitatively evaluate the reliability of each cluster result, reducing the impact of uncertainty and noise on the diagnostic results. Finally, to further improve the accuracy and robustness of the diagnostic model, an unsupervised cluster ensemble based on the fuzzy consistency function of the graph is introduced to integrate these reliability-evaluated results. Extensive experiments on the CWRU benchmark dataset demonstrate that RE-DFCE achieves superior performance compared to existing methods.</p>