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Title: Wireless vibration sensing with local signal processing for condition monitoring within a gas turbine engine
Author: Villanueva Marcocchio, Aldo
ISNI:       0000 0004 7431 8572
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2018
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Vibration-based condition monitoring is a technique that contributes to the reliability of rotating machinery. Wireless sensor nodes can be used for continuous machine health monitoring in automotive, industrial or aerospace sectors. Consider that random packet loss and signal recovery are common issues in wireless sensing, especially under these types of scenarios. Conventionally, to compensate for these issues, lost data packets are retransmitted, the signal is boosted or the communication channel is changed. However, these techniques may not be enough or convenient to alleviate the packet loss problem as energy conservation in sensor nodes is a critical aspect to face as they are generally powered by batteries or energy harvesters. More importantly, the signal recovery problem needs to be addressed as wireless retransmissions may be limited in aerospace applications. Hence, a different approach is desirable. This thesis presents a framework that mitigates the random packet loss problem, performs data compression, recovers the signal and increases energy savings at the sensor nodes. The focus is on energy conservation and increased signal recovery performance in wireless vibration sensing systems directed to support equipment health management in aero-engines. The presented framework is divided into vibration data encoding and vibration data decoding. In the former, local signal processing in the frequency domain and compressive sensing occurs at the wireless sensor nodes. For the latter, a novel signal recovery algorithm is used to decode the signal at the base station. The proposed algorithm enhances the performance of the standard orthogonal matching pursuit algorithm for recovery of sparse vibration signals. The wireless vibration sensing system was successfully demonstrated on an active Trent 1000 gas turbine engine running on a testbed to collect real vibration data. This data was used as prior frequency support for the proposed signal recovery algorithm. The inclusion of prior information improves system performance by reducing the number of samples required for signal recovery. Energy is conserved at the wireless sensors by reducing the amount of data to be sent for vibration signal recovery.
Supervisor: Jones, Bryn ; Kadirkamanathan, Visakan Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available