Ultrasonic waveguide sensor is suitable for monitoring pipeline wall thickness at high temperature (>500 °C) due to the heat dissipation and heat insulation effect of guide plate. The shear wave is produced by shear-wave piezoelectric ceramic. The plate wave used to measure pipeline wall thickness is formed by the shear wave. However, conventional high-temperature sensors using shear-wave piezoelectric ceramics were limited in cost, frequency and size. Especially, it is difficult to obtain high-frequency shear wave with small-sized sensors. In this paper, a waveguide sensor structure based on wave mode conversion is developed to overcome problems above. Shear horizontal (SH0) mode plate waves can be converted by longitudinal waves generated from standard, low-frequency and small-sized piezoelectric ceramics. A suitable frequency-thickness product is chosen to avoid dispersion and signal distortion. The feasibility of the designed sensor is verified by experiments. The mature ultrasonic longitudinal wave probe is used as the transducer to improve the frequency and reduce the cost and size of the sensor, which provides a basis for achieving large-area matrix measurement.

The depth of corrosion pits is critical to the safe operation of pipelines. Conventionally, monitoring of metal pipeline corrosion has generally relied on a single technique. Among these, the ultrasonic thickness measurement method and the field signature method are widely used in pipeline monitoring systems. The ultrasonic method has high reliability and accuracy for monitoring the thickness of pipelines, but it is limited by coupling and measurement range. Additionally, the field signature method often suffers from inadequate identification of corrosion pits and lower measurement accuracy. To address these limitations, a multi-sensor fusion model is proposed to monitor corrosion in metal pipelines. The multi-sensor fusion model is constructed by alternately arranging ultrasonic sensors and field signature probes, and a dedicated fusion algorithm is designed. The integrated model leverages the complementary strengths of both techniques while mitigating their individual shortcomings. Furthermore, an artificial neural network is employed to accurately identify pitting depth, thereby resolving the challenge in discriminating corrosion pit depths. Experimental results demonstrate that the multi-sensor fusion model can overcome the inherent drawbacks associated with a single technique. Consequently, it enhances the overall reliability, measurement accuracy, and operational range of the pipeline corrosion measurement system.

The liquid level sensor based on ultrasonic guided waves has been applied in the aircraft fuel level measurement system due to its advantages such as high accuracy, strong anti-electromagnetic interference ability, and immunity to water accumulation. It compensates for the shortcomings of capacitive liquid level sensors. However, high order mode waves, interference noise and aircraft vibrations have affected the accuracy and reliability of the ultrasonic liquid level sensor. A conical orthogonal matrix ring structure is designed to eliminate interference waves through mechanisms of acoustic scattering and absorption, leading to a notable improvement in signal-to-noise ratio and operational reliability. Furthermore, an envelope superposition algorithm grounded in multi-wave superposition theory is proposed for processing ultrasonic signals under dynamic liquid level conditions. This algorithm not only counteracts vibration-induced signal degradation but also further enhances signal-to-noise ratio. Experiments show that the proposed structure and algorithm have effectively reduced the interference from clutter and overcome the influence caused by aircraft vibration. The signal-to-noise ratio at low liquid level has increased from 3.3 dB to 13.9 dB. The signal-to-noise ratio during low-frequency vibration has increased from 6.4 dB to 11.5 dB. At the same time, it has enhanced the reliability of the sensors and their ability to adapt to the onboard environment, which holds significant engineering application value.