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Development of a low‐power wireless acoustic emission sensor node for aerospace applications
Author(s) -
Grigg Stephen,
Pullin Rhys,
Pearson Matthew,
Jenman David,
Cooper Robert,
Parkins Andrew,
Featherston Carol Ann
Publication year - 2021
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2701
Subject(s) - sleep mode , wireless sensor network , node (physics) , energy harvesting , power (physics) , acoustic emission , energy (signal processing) , piezoelectricity , wireless , event (particle physics) , structural health monitoring , modal , sensor node , piezoelectric sensor , acoustics , computer science , energy consumption , simple (philosophy) , electronic engineering , process (computing) , engineering , electrical engineering , power consumption , wireless network , telecommunications , key distribution in wireless sensor networks , materials science , structural engineering , physics , computer network , philosophy , operating system , epistemology , quantum mechanics , polymer chemistry
Summary Acoustic emission (AE) is the spontaneous release of energy caused by the growth of damage, the monitoring of which gives an indication of the presence of damage within a structure. The current standard for AE localisation is difficult to apply in a low‐power system as sensors must either be wired together or Node's time synchronised, which is power intensive. This paper proposes the use of a method of bonding three piezoelectric sensors in a small triangular array, which has previously been shown by Aljets et al. to be capable of locating sources in simple structures. In this prior work the wave's A 0 mode was used to predict the angle of arrival and the distance the wave has travelled through single sensor modal analysis. This paper presents the development of hardware to apply this technique and testing that showed artificial sources could be located in simple plates to a good level of accuracy. The addition of complexity to structures significantly reduced accuracy. This prompted hardware modifications to use the S 0 mode for angle prediction. Testing showed that this significantly improved performance in a complex composite structure. The power consumption of the device is very low, consuming 0.33 mW in sleep mode, 17.44 mW whilst waiting for an event and 38 mW to record, process and transmit an event. This level of consumption has the potential to be self‐powered via energy harvesting.