A non-destructive fault diagnosis method for a diaphragm compressor in the hydrogen refueling station. (13th September 2019)

• Record Type: Journal Article
• Authors: Li, Xueying; Chen, Jiahao; Wang, Zhizhong; Jia, Xiaohan; Peng, Xueyuan
• Is Part Of: International journal of hydrogen energy. Volume 44:Number 44(2019)
• Page Start: 24301
• Page End: 24311
• Publication Date: 2019-09-13
• Subjects: Hydrogen refueling station -- Diaphragm compressor -- Acoustic emission -- Non-destructive testing -- Fault diagnosis; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2019.07.147
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: Costly and time-consuming maintenance of the hydrogen compressors due to their frequent breakdown severely hinders the deployment and promotion of hydrogen refueling station (HRS), and effective condition monitoring and fault diagnosis is the key to reduce the unscheduled downtime of the compressor. This paper proposes a non-destructive method for fault diagnosis of diaphragm compressors for HRSs based on the acoustic emission (AE) signal. The AE signals in the time domain are segmented into angle-domain signals correspond to a working cycle. The feature events of the moving components are determined through the measured AE signal in both angle-domain and angle-frequency domain based on short-term Fourier transform (STFT). Those feature events signals are innovatively applied to identify the typical abnormal conditions of excessively high oil pressure, slightly inadequate oil pressure and seriously inadequate oil pressure, replacing the traditional and destructive pressure measuring method. The results show that this method can be used to effectively diagnose abnormal working conditions and indicate that this method can be utilized as a powerful tool in the non-destructive condition monitoring and fault diagnosis of the diaphragm compressors. Highlights: A non-destructive fault diagnosis method for diaphragm compressor is proposed. AE signal together with STFT is applied to determine feature events of compressor. Relationship between oil and gas pressure is reflected by the feature events. The method is validated by successful identification of typical abnormal conditions.