Data Symmetry Enhancement-Based Abnormal State Detection of High-End Hydrogen Compressors Under No-Fault Samples

Diaphragm-type hydrogen compressors serve as core equipment in hydrogen refueling stations, yet their early-stage deployment faces critical challenges, including insufficient fault mode data accumulation and unclear health evaluation criteria. Conventional manual operation and maintenance monitoring methods suffer from low efficiency and poor reliability, failing to meet the demand for safe and stable operation. To address these limitations, this study proposes a novel abnormal state detection methodology applicable under no-fault sample conditions. The approach leverages data symmetry enhancement techniques to expand the training dataset using only normal operation records, and integrates multi-source sensor data for comprehensive equipment health analysis. Experimental results show that the proposed method achieves 96% detection accuracy, an approximately 8% missed detection rate, and millisecond computational latency, significantly outperforming traditional detection algorithms. This work provides a practical solution for early-stage equipment monitoring without fault samples, enhancing both technical robustness and operational efficiency for diaphragm hydrogen compressor maintenance.