Predicting equipment failures plays a pivotal role in minimizing maintenance costs and boosting production efficiency within the industrial sector. This paper introduces a novel approach that integrates Causal Inference with predictive modeling to enhance prediction accuracy, tackling key challenges such as noise interference, insufficient causal validation, and missing data. We first validate the causal connections identified by the Greedy Equivalence Search algorithm using conditional mutual information to strengthen the reliability of the causal graph. An information bottleneck strategy is then employed to isolate essential causal features, effectively filtering out irrelevant noise and refining the causal structure. Crucially, in the actual prediction phase, we propose a recursive causal inference-based imputation method to handle missing data, leveraging the causal graph to iteratively infer and fill gaps, thereby improving data completeness and prediction accuracy. Experimental results demonstrate that the proposed method significantly outperforms existing approaches, exhibiting superior accuracy and robustness in managing complex industrial datasets.

With its unique information-filtering function, text summarization technology has become a significant aspect of search engines and question-and-answer systems. However, existing models that include the copy mechanism often lack the ability to extract important fragments, resulting in generated content that suffers from thematic deviation and insufficient generalization. Specifically, Chinese automatic summarization using traditional generation methods often loses semantics because of its reliance on word lists. To address these issues, we proposed the novel BioCopy mechanism for the summarization task. By training the tags of predictive words and reducing the probability distribution range on the glossary, we enhanced the ability to generate continuous segments, which effectively solves the above problems. Additionally, we applied reinforced canonicality to the inputs to obtain better model results, making the model share the sub-network weight parameters and sparsing the model output to reduce the search space for model prediction. To further improve the model’s performance, we calculated the bilingual evaluation understudy (BLEU) score on the English dataset CNN/DailyMail to filter the thresholds and reduce the difficulty of word separation and the dependence of the output on the word list. We fully fine-tuned the model using the LCSTS dataset for the Chinese summarization task and conducted small-sample experiments using the CSL dataset. We also conducted ablation experiments on the Chinese dataset. The experimental results demonstrate that the optimized model can learn the semantic representation of the original text better than other models and performs well with small sample sizes.