An industrial equipment fault diagnosis method based on deep belief rule base fusing indicator uncertainty

Abstract Precise fault diagnosis under complex working conditions is critical for ensuring the safety of industrial equipment. The belief rule base (BRB), characterized by its interpretability and capability to handle nonlinear, small-sample data, is inherently suitable for such diagnostic tasks. However, traditional BRB methods are impeded by the combinatorial rule explosion derived from high-dimensional features and the uncertainty introduced by environmental noise. To address structural complexity and environmental uncertainty simultaneously, this paper proposes a deep BRB fault diagnosis method integrated with indicator uncertainty deep BRB uncertainty. First, the extreme gradient boosting algorithm is employed to evaluate feature importance and select key features, constructing a hierarchical deep inference topology. Second, a quantification method based on the average distance of monitoring data is proposed to extract the uncertainty of key indicators, establishing an inference mechanism that fuses this uncertainty. Third, the projected covariance matrix adaptation evolution strategy is utilized to jointly optimize the full parameter set of the model, thereby enhancing generalization performance. Finally, experimental validation is conducted using bearing and gearbox datasets. Results indicate that the proposed method outperforms traditional approaches in terms of comprehensive diagnostic performance, achieving higher fault classification accuracy and stronger robustness, thus validating its superiority in handling interference and uncertain environments.