A novel isoparametric normalization mapping for p-V diagrams in intelligent diagnosis of reciprocating compressors via CNN
摘要
The accurate and real-time fault diagnosis of reciprocating compressors is quite challenging, particularly in dynamic environments such as marine settings, where operating conditions change a lot over time. These variations, such as intake pressure, temperature, and gas component, can significantly alter the pressure-volume (p-V) diagram, leading to potential misinterpretations by traditional fault detection methods. This study proposes an intelligent framework that unites a novel isoparametric normalization of the indicator diagram with a lightweight convolutional neural network (CNN), making two contributions. First, the logarithmic p-V diagram is innovatively warped into a dimensionless square domain by normalization of isoparametric element mapping; this procedure magnifies fault-induced distortions while suppressing theoretical p-V diagram changes caused by load, speed, or gas component variations. Second, a compact CNN, systematically tuned through hyper-parameter optimization, is trained on the normalized diagrams. With the feature enhancement of isoparametric normalization, the network converges within three epochs (about 6 min) and attains 98.43% test accuracy on 960 labelled samples covering fifteen compressor states. This performance was validated through extensive experiments on a 160 kW two-stage test rig, where the model demonstrated robustness and reliability under varying operating conditions. The model, optimized for real-time deployment in dynamic industrial environments and designed to support continuous sample library enrichment and online retraining, offers a practical solution for predictive health management of reciprocating compressors.