Affiliation:
1. College of Engineering, Ocean University of China, Qingdao 266100, China
2. Navy Submarine College, Qingdao 266199, China and the Institute of Acoustics, Chinese Academy of Sciences, Beijing 100089, China
Abstract
Pressure vessel leakages cannot initially be visited directly and will gradually cause deterioration, which can result in catastrophic damage. Acoustic emission (AE) signals generated by leakage have the potential of being used for online monitoring. Unfortunately, AE signals have the
characteristics of being non-stationary, wide-band and with strong noise interference, which causes the monitoring results to have low reliability. To address the poor robustness of traditional time-domain and time-frequency domain-based monitoring methods, an online monitoring method based
on adaptive singular value decomposition (ASVD) is proposed in this paper. Firstly, singular value decomposition (SVD) is used to divide the signal space into a signal subspace and a noise subspace. Experiments indicate that SVD can distinguish leakages under conditions of different pressures
and variable temperature, which means that SVD is sensitive to changes in signal. Subsequently, update iteration-based ASVD algorithms are proposed for long-term online health monitoring and ASVD is shown to be successful in distinguishing the different statuses of intact, leakage and repaired.
To improve the robustness of ASVD, a novel energy indicator is proposed, which can identify the status change more effectively. With the proposed methodology, an online monitoring application for pressure vessel leakage detection is expected to be achievable.
Publisher
British Institute of Non-Destructive Testing (BINDT)
Subject
Materials Chemistry,Metals and Alloys,Mechanical Engineering,Mechanics of Materials
Cited by
2 articles.
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