Xihuan Su
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xihuan Su has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Anton Louise De Ocampo
Batangas State University, Philippines
1 shared publication - Antonette Villapando Chua
Dept. of Electronics Engineering, Batangas State University, The National Engineering University, Batangas City, Philippines
1 shared publication - Dianyou Kang
1 College of Engineering, Batangas State University the National Engineering University, Alangilan Campus, Batangas City 4200, Philippines
1 shared publication - Oliver Lexter July Alvarez Jose
Dept. of Electronics Engineering, Batangas State University, The National Engineering University, Batangas City, Philippines
1 shared publication - Ralph Gerard B. Sangalang
Dept. of Electronics Engineering, Batangas State University, The National Engineering University, Batangas City, Philippines
1 shared publication
Latest Publications
- Open Access
- 15 May 2026
- Published by JVE International Ltd. in Mathematical Models in Engineering
- Vol. 12 (2) , 125-146
Non-intrusive appliance health monitoring and remaining useful life prediction using a DRN-transformer model
This paper presents a high-performance embedded micro-sensing and processing integrated non-intrusive load monitoring system for real-time appliance health assessment and remaining useful life (RUL) prediction, with the core innovation focused on the design and implementation of an STM32H7-based embedded micro-system that realizes seamless integration of high-frequency signal acquisition, edge-side real-time preprocessing, and intelligent multi-task analysis on a single chip. Utilizing an STM32H7 microcontroller, it captures high-frequency voltage and current signals to detect appliance degradation features. Time-frequency preprocessing with FFT and STFT improves signal representation, enabling better feature extraction. The proposed DRN-Transformer model integrates deep residual networks and Transformer attention mechanisms to extract both local and long-range temporal features. Tested on four common household appliances (refrigerator, washing machine, air conditioner, microwave oven), the system achieved over 92 % accuracy in health classification (up to 97.8 % for washing machines) and a low error in RUL estimation with an average RMSE of 3.2 months and an average MAPE of 8.5 %, outperforming traditional machine learning baselines (SVM/RF) by more than 40 % in health classification accuracy and surpassing mainstream deep learning models (LSTM/Transformer/LSTM-VMD) by 15-20 % in RUL prediction precision. Furthermore, the proposed embedded micro-sensing platform explicitly considers hardware-level constraints such as on-chip memory capacity, computational complexity, and real-time inference latency. By optimizing model architecture and adopting lightweight deployment strategies, the system achieves efficient edge-side intelligence while maintaining compatibility with resource-constrained microcontroller platforms. The system outperformed traditional baseline models and showed good generalization across different brands. With low latency and power consumption, it is well-suited for smart home applications.
- 15 May 2026
- Published by JVE International Ltd. in Mathematical Models in Engineering
- Vol. 12 (2) , 125-146
Open AccessNon-intrusive appliance health monitoring and remaining useful life prediction using a DRN-transformer model
This paper presents a high-performance embedded micro-sensing and processing integrated non-intrusive load monitoring system for real-time appliance health assessment and remaining useful life (RUL) prediction, with the core innovation focused on the design and implementation of an STM32H7-based embedded micro-system that realizes seamless integration of high-frequency signal acquisition, edge-side real-time preprocessing, and intelligent multi-task analysis on a single chip. Utilizing an STM32H7 microcontroller, it captures high-frequency voltage and current signals to detect appliance degradation features. Time-frequency preprocessing with FFT and STFT improves signal representation, enabling better feature extraction. The proposed DRN-Transformer model integrates deep residual networks and Transformer attention mechanisms to extract both local and long-range temporal features. Tested on four common household appliances (refrigerator, washing machine, air conditioner, microwave oven), the system achieved over 92 % accuracy in health classification (up to 97.8 % for washing machines) and a low error in RUL estimation with an average RMSE of 3.2 months and an average MAPE of 8.5 %, outperforming traditional machine learning baselines (SVM/RF) by more than 40 % in health classification accuracy and surpassing mainstream deep learning models (LSTM/Transformer/LSTM-VMD) by 15-20 % in RUL prediction precision. Furthermore, the proposed embedded micro-sensing platform explicitly considers hardware-level constraints such as on-chip memory capacity, computational complexity, and real-time inference latency. By optimizing model architecture and adopting lightweight deployment strategies, the system achieves efficient edge-side intelligence while maintaining compatibility with resource-constrained microcontroller platforms. The system outperformed traditional baseline models and showed good generalization across different brands. With low latency and power consumption, it is well-suited for smart home applications.