Wood utility poles are widely applied in power transmission and telecommunication systems in Australia. Because of a variety of external influence factors, such as fungi, termite and environmental conditions, failure of poles due to the wood degradation with time is of common occurrence with high degree uncertainty. The pole failure may result in serious consequences including both economic and public safety. Therefore, accurately and timely identifying the health condition of the utility poles is of great significance for economic and safe operation of electricity and communication networks. In this paper, a novel non-destructive evaluation (NDE) framework with advanced signal processing and artificial intelligence (AI) techniques is developed to diagnose the condition of utility pole in field. To begin with, the guided waves (GWs) generated within the pole is measured using multi-sensing technique, avoiding difficult interpretation of various wave modes which cannot be detected by only one sensor. Then, empirical mode decomposition (EMD) and principal component analysis (PCA) are employed to extract and select damage-sensitive features from the captured GW signals. Additionally, the up-to-date machine learning (ML) techniques are adopted to diagnose the health condition of the pole based on selected signal patterns. Eventually, the performance of the developed NDE framework is evaluated using the field testing data from 15 new and 24 decommissioned utility poles at the pole yard in Sydney.

中文翻译：

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使用智能无损评估 (NDE) 框架对原位木质电线杆进行自动健康状况诊断

木质电线杆在澳大利亚广泛应用于电力传输和电信系统。由于受真菌、白蚁、环境条件等多种外部影响因素的影响，由于木材随时间退化而导致的电杆失效是常有的事，具有高度的不确定性。电线杆故障可能导致包括经济和公共安全在内的严重后果。因此，准确、及时地识别电线杆的健康状况对于电力和通信网络的经济和安全运行具有重要意义。在本文中，开发了一种具有先进信号处理和人工智能 (AI) 技术的新型无损评估 (NDE) 框架来诊断现场电线杆的状况。首先，极点内产生的导波 (GW) 使用多传感技术进行测量，避免了难以解释仅由一个传感器无法检测到的各种波模式。然后，采用经验模态分解 (EMD) 和主成分分析 (PCA) 从捕获的 GW 信号中提取和选择损伤敏感特征。此外，采用最新的机器学习 (ML) 技术根据选定的信号模式诊断杆的健康状况。最终，使用来自悉尼电线杆场的 15 根新电线杆和 24 根退役电线杆的现场测试数据评估开发的 NDE 框架的性能。经验模态分解 (EMD) 和主成分分析 (PCA) 用于从捕获的 GW 信号中提取和选择损伤敏感特征。此外，采用最新的机器学习 (ML) 技术根据选定的信号模式诊断杆的健康状况。最终，使用来自悉尼电线杆场的 15 根新电线杆和 24 根退役电线杆的现场测试数据评估开发的 NDE 框架的性能。经验模态分解 (EMD) 和主成分分析 (PCA) 用于从捕获的 GW 信号中提取和选择损伤敏感特征。此外，采用最新的机器学习 (ML) 技术根据选定的信号模式诊断杆的健康状况。最终，使用来自悉尼电线杆场的 15 根新电线杆和 24 根退役电线杆的现场测试数据评估开发的 NDE 框架的性能。