Intelligent fault diagnosis of machines has long been a research hotspot and has achieved fruitful results. However, intelligent fault diagnosis is a difficult issue in the case of small sample due to the lack of fault signals. To solve this problem, a small sample focused intelligent fault diagnosis method via multi-modules gradient penalized generative adversarial networks (MGPGAN) is proposed. The proposed method consists of three network modules: Generator, Discriminator, and Classifier. By adversarial training, Generator can generate mechanical signals in different health conditions. Because of the high similarity to the signals obtained in practice, the generated signals can also be used as training data so that the limited training dataset of the proposed method is expanded. Classifier has a strong ability to extract fault features from raw mechanical signals, and then classify different health conditions. The experimental results on two bearing vibration datasets indicate that the proposed method can not only generate bearing vibration signals but also obtain fairly high fault classification accuracy under the small sample condition.

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.