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.

3D point cloud has been widely applied in virtual reality and augmented reality. A complex 3D scene always needs a large number of the point cloud to represent and demands a lot of space to store. Thus, point cloud compression becomes a crucial issue to research. In this paper, we propose a novel lossy geometric compression method of autoencoder based on DCGAN optimization. This method can reconstruct a high-quality point cloud and solves a large area of missing points in the process of compression and decompression. To improve the point cloud codec performance, we propose a multi-scale 3D deconvolution hopping connection structure to obtain a better-quality reconstructed point cloud under low bit rates. Our approach is the first GAN-based point cloud compression algorithm to our knowledge. Compared with state-of-the-art methods on the MVUB dataset, our approach achieves a better rate-distortion performance and visual quality.