Quantitative Diagnosis of Loose Piston Rod Threads in Reciprocating Compressors for Hydrogen Storage and Transport
International journal of hydrogen energy(2023)
摘要
Fracture of the piston rod yields catastrophic accidents in reciprocating hydrogen compressors, resulting in costly and time-consuming maintenance and developing the latest challenge to hydrogen safety and economy. The fracture-prone section is mostly located at the connecting thread of the crosshead or of the piston, mainly due to thread loosening and hereby insufficient preload followed by fatigue and fracture. Here, a novel quantitative diagnosis method for thread loosening of hydrogen reciprocating compressor piston rods was proposed. First, a finite element model of a single-throw reciprocating compressor was established to analyse the dynamic behaviour of the operating posture and load pattern at the crosshead and piston rod connecting thread. Further, the impact events triggered by crosshead displacement and rod load reversal events were identified to enhance the interpretability of the signal, thereby obtaining a loose-sensitive frequency band of 2-15 kHz and specific phase angle features. Next, the multi-band feature spectrum was formed using variational modal decomposition and data dimensionality reduction based on the Hilbert transform, which demonstrates satisfactory performance in the feature reservation as well as spectrum compression of acceleration signals in both the time and frequency domain. The proposed methodology was validated by experiments with various levels of thread loosening. The result indicated the multi-band feature spectrum feature was effective in quantitative loosening diagnosis with accurate and stable classification. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
更多查看译文
关键词
Reciprocating compressor,Piston rod fracture,Thread loosening,Quantitative diagnosis,Hydrogen energy
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要