Centrifugal compressors are key equipment in the natural gas pipeline network of the petrochemical industry, and their high failure rate can cause significant economic losses to the affiliated enterprises. This article proposes an ontology based fault diagnosis method for centrifugal compressors. Firstly, this method takes the centrifugal compressor fault analysis reports accumulated by petrochemical enterprises as the knowledge source, extracts fault diagnosis knowledge from the knowledge source through ontology modeling, and promotes the integration, sharing, and reuse of fault diagnosis knowledge. Then, a fault diagnosis knowledge base was constructed using the ontology software Protégé, and rule?based reasoning (RBR) was implemented using the Semantic Web Rule Language (SWRL). The knowledge was stored and queried using the software Neo4j. The effectiveness of this fault diagnosis method has been confirmed through testing the oil system composed of centrifugal compressors. The results show that this method improves the application efficiency of fault knowledge and provides a high?quality knowledge foundation for the diagnosis and decision?making of centrifugal compressors.

As the global energy structure transitions toward cleaner and more sustainable sources, natural gas, as a low?carbon and environmentally friendly fossil fuel, continues to see increasing consumption. However, with the ongoing expansion of natural gas pipeline networks, the growing coverage areas, and the significant increases in both transportation distances and volumes, the safe operation of pipelines is facing unprecedented challenges, with pipeline leakage emerging as a particularly critical issue. In response to the long?term monitoring needs of oil and gas pipeline leaks, based on the principle of negative pressure wave detection, a highly efficient and reliable pipeline leakage monitoring simulation system was developed using LabVIEW, a powerful graphical programming platform. This system incorporates advanced sensor networks, data communication technologies, and signal processing algorithms. It was used for the leakage monitoring of oil and gas pipelines, and its performance was verified through experiments. The results show that the system is capable of detecting pipeline leakage and rapidly locating leakage points through efficient algorithms, providing crucial information for timely repair and ensuring the safety and stability of oil and gas pipelines.