Ultrasonic defect detection of high-density polyethylene pipe materials using FIR filtering and block-wise singular value decomposition.

Condition monitoring of high-density polyethylene (HDPE) pipes used for fluid and gas transfer is important for the safety of energy conservation and the environment. Ultrasonic phased array imaging methods provide a solution to detect and assess defects in HDPE pipes. However, ultrasonic bulk waves propagating in these viscoelastic media are strongly attenuated, resulting in reduced signal amplitude. In this study, a linear-phase Finite Impulse Response (FIR) filter is used to remove unwanted frequency components from the measured ultrasonic signals to improve the signal-to-noise ratio before applying the imaging algorithm of the total focusing method (TFM). Building upon this, a block-wise singular value decomposition (SVD) technique, which can adaptively determine the singular value cutoff threshold based on each block in the whole TFM image, is used to enhance the obtained TFM image quality. The performance of the combination of FIR filtering and block-wise SVD technique is validated by experimental data of HDPE pipe materials. Results demonstrate that the proposed approach generates good images to provide the detection and characterization of side-drilled holes in HDPE pipe materials.

Ultrasonic wireless power links for battery-free condition monitoring in metallic enclosures.

This paper presented a novel ultrasonic wireless power link (UWPL) to provide power supply for embedded condition monitoring of enclosed metallic structures, where recharging or replacing batteries can be problematic. Two piezoelectric transducers are adopted to establish the wireless power links, within which one transducer is used to generate ultrasonic waves and the other is to receive the transferred ultrasonic energy and to energize the associated embedded condition monitoring units. A power management solution is established to regulate the receiver output into a constant voltage suitable for sensing application. A theoretical model was established to understand the UWPL dynamics and to analyze the energy budget balance between the UWPL and the sensing power demands. A finite element model was built to validate the proposed idea. The UWPL was then experimentally implemented using two piezoelectric transducers and tested in aluminium plates with different thickness. A power management sub-system was developed and tested for sensing applications. An output power of 1.73 mW was obtained on a 1.5 kΩ resister with the input voltage of 15 V at 42.6 kHz through a 6 mm-thick aluminium plate. Sufficient power can be transferred over a large distance via metallic structures, showing the capability in implementing battery-free condition monitoring of enclosed metallic structures, such as petroleum pipelines, engines, and aluminium airframe.

A Convolutional Neural Network for Impact Detection and Characterization of Complex Composite Structures.

This paper reports on a novel metamodel for impact detection, localization and characterization of complex composite structures based on Convolutional Neural Networks (CNN) and passive sensing. Methods to generate appropriate input datasets and network architectures for impact localization and characterization were proposed, investigated and optimized. The ultrasonic waves generated by external impact events and recorded by piezoelectric sensors are transferred to 2D images which are used for impact detection and characterization. The accuracy of the detection was tested on a composite fuselage panel which was shown to be over 94%. In addition, the scalability of this metamodelling technique has been investigated by training the CNN metamodels with the data from part of the stiffened panel and testing the performance on other sections with similar geometry. Impacts were detected with an accuracy of over 95%. Impact energy levels were also successfully categorized while trained at coupon level and applied to sub-components with greater complexity. These results validated the applicability of the proposed CNN-based metamodel to real-life application such as composite aircraft parts.

Electrode Coverage Optimization for Piezoelectric Energy Harvesting from Tip Excitation.

Piezoelectric energy harvesting using cantilever-type structures has been extensively investigated due to its potential application in providing power supplies for wireless sensor networks, but the low output power has been a bottleneck for its further commercialization. To improve the power conversion capability, a piezoelectric beam with different electrode coverage ratios is studied theoretically and experimentally in this paper. A distributed-parameter theoretical model is established for a bimorph piezoelectric beam with the consideration of the electrode coverage area. The impact of the electrode coverage on the capacitance, the output power and the optimal load resistance are analyzed, showing that the piezoelectric beam has the best performance with an electrode coverage of 66.1%. An experimental study was then carried out to validate the theoretical results using a piezoelectric beam fabricated with segmented electrodes. The experimental results fit well with the theoretical model. A 12% improvement on the Root-Mean-Square (RMS) output power was achieved with the optimized electrode converge ratio (66.1%). This work provides a simple approach to utilizing piezoelectric beams in a more efficient way.