This paper investigates prognostics and health management (PHM) for data communication in industrial environments, focusing on serial communication devices. The article first provides a comprehensive review of health status prediction methods in circuit-related fields and summarizes the existing issues, such as outdated technology and the challenges in adapting those methods to real-world industrial environments. To address these gaps, we propose our targeted analysis and resolution strategies toward the specific domain. By constructing a dataset based on real-world communication voltage and bit error rate (BER) data, we establish a quantitative health status metric. Subsequently, an improved STMDLinear prediction method based on feature decomposition and fusion is introduced. In this model, we perform wavelet decomposition, reconstruction, and compression on the input data to enhance key information while preserving as much of the original data as possible and reducing storage requirements. Then the remainder vector and trend vector are processed through a TCN module and an attention module. These modules further extract features and fuse them at the end of our model, ultimately producing the predicted BER results. Finally, the network is applied to the real-world communication dataset acquired by us. The proposed model achieves significant performance improvements over existing advanced time series prediction networks, with over 20% improvements in MSE and MAE compared to the baseline. Furthermore, with only approximately 8,000 trainable parameters, it provides an advanced, efficient, and lightweight solution.