Wireless sensor networks (WSNs) operate in complex and harsh environments; thus, node faults are inevitable. Therefore, fault diagnosis of the WSNs node is essential. Affected by the harsh working environment of WSNs and wireless data transmission, the data collected by WSNs contain noisy data, leading to unreliable data among the data features extracted during fault diagnosis. To reduce the influence of unreliable data features on fault diagnosis accuracy, this paper proposes a belief rule base (BRB) with a self-adaptive quality factor (BRB-SAQF) fault diagnosis model. First, the data features required for WSN node fault diagnosis are extracted. Second, the quality factors of input attributes are introduced and calculated. Third, the model inference process with an attribute quality factor is designed. Fourth, the projection covariance matrix adaptation evolution strategy (P-CMA-ES) algorithm is used to optimize the model’s initial parameters. Finally, the effectiveness of the proposed model is verified by comparing the commonly used fault diagnosis methods for WSN nodes with the BRB method considering static attribute reliability (BRB-Sr). The experimental results show that BRB-SAQF can reduce the influence of unreliable data features. The self-adaptive quality factor calculation method is more reasonable and accurate than the static attribute reliability method.

The prediction of processor performance has important reference significance for future processors. Both the accuracy and rationality of the prediction results are required. The hierarchical belief rule base (HBRB) can initially provide a solution to low prediction accuracy. However, the interpretability of the model and the traceability of the results still warrant further investigation. Therefore, a processor performance prediction method based on interpretable hierarchical belief rule base (HBRB-I) and global sensitivity analysis (GSA) is proposed. The method can yield more reliable prediction results. Evidence reasoning (ER) is firstly used to evaluate the historical data of the processor, followed by a performance prediction model with interpretability constraints that is constructed based on HBRB-I. Then, the whale optimization algorithm (WOA) is used to optimize the parameters. Furthermore, to test the interpretability of the performance prediction process, GSA is used to analyze the relationship between the input and the predicted output indicators. Finally, based on the UCI database processor dataset, the effectiveness and superiority of the method are verified. According to our experiments, our prediction method generates more reliable and accurate estimations than traditional models.