Coded Excitation of Ultrasound for Improved Signal Acquisition in Pipeline Inspection Robots

用于改进管道检查机器人信号采集的超声波编码激励

• 批准号: 2444872
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2444872
• 关键词: Coded; Excitation; Ultrasound; Improved; Signal

This project is investigating the use of advanced excitation techniques to improve the performance of inline inspection robots. Inline robots are used to monitor the structural health and integrity of large pipeline structures. This project aims to understand how advanced excitation techniques may help to improve the sensitivity, energy consumption, size, and design of ultrasound acquisition systems on inline robots.Currently, traditional excitation methods with high voltage pulses are used to yield sufficient Signal-to-Noise Ratios (SNRs) in the acquired signals. However, high voltage pulsers are inefficient [2] which increases the required energy that must be stored and carried by the robot. Additionally, typical robots incorporate large numbers of ultrasound transducers to ensure the full pipe structure is inspected which introduces problems with crosstalk. Crosstalk from both electronic and ultrasonic pickup between adjacent measurements complicates signal analysis and imposes spacing constraints between transducers. Signal variation introduced by differences across transducers also complicates the signal analysis. These challenges result in strict design requirements and an increased robot build and operating cost. Coded Excitation has the potential to overcome these challenges. Literature has shown that Coded Excitation allows measurements with low excitation voltages [3]; can be used to optimise and standardise waveforms from transducers [4]; and can reduce crosstalk and increase imaging rate [5]. Therefore, Coded Excitation has the potential to improve inline robot performance and design. This project initially focussed on the fundamentals of Coded Excitation. This involved exploring how Coded Excitation impacts SNR and how the code can be optimised for different measurements. The project now looks to investigate: the robustness of Coded Excitation on different hardware; alternative sequence types and their performance limits; sequence orthogonality and crosstalk; optimising transducer standardisation; and hardware requirements for low-power modular implementations.

该项目正在研究使用先进的激励技术来提高在线检测机器人的性能。直列式机器人用于监测大型管道结构的结构健康和完整性。该项目旨在了解先进的激励技术如何有助于提高在线机器人上超声采集系统的灵敏度、能耗、尺寸和设计。目前，传统的高压脉冲激励方法被用来在采集的信号中产生足够的信噪比(SNR)。然而，高压脉冲器效率低下，这增加了机器人必须存储和携带的所需能量。此外，典型的机器人集成了大量的超声波换能器，以确保检查完整的管道结构，这会带来串扰问题。相邻测量之间来自电子和超声波拾取的串扰使信号分析复杂化，并对换能器之间的间距施加限制。传感器之间的差异引起的信号差异也使信号分析变得复杂。这些挑战导致了严格的设计要求以及增加的机器人制造和运营成本。编码激励有可能克服这些挑战。文献表明，编码激励允许以低激励电压进行测量[3]；可用于优化和标准化来自传感器的波形[4]；并可减少串扰并提高成像率[5]。因此，编码激励有可能改善直列式机器人的性能和设计。该项目最初侧重于编码激励的基本原理。这涉及到探索编码激励如何影响信噪比，以及如何针对不同的测量优化编码。该项目现在希望调查：编码激励在不同硬件上的健壮性；可选的序列类型及其性能限制；序列正交性和串扰；优化换能器标准化；以及低功耗模块实现的硬件要求。