Department of Computational Mechanics, Faculty of Mechanical Engineering, Universidade Estadual de Campinas, Campinas 13083-860, Brazil.
Exploratory Hardware Desing Department, Instituto de Pesquisas Eldorado, Campinas 13083-898, Brazil.
Sensors (Basel). 2021 Dec 29;22(1):213. doi: 10.3390/s22010213.
This paper presents the development of a methodology to detect and evaluate faults in cable-stayed towers, which are part of the infrastructure of Brazil's interconnected electrical system. The proposed method increases system reliability and minimizes the risk of service failure and tower collapse through the introduction of predictive maintenance methods based on artificial intelligence, which will ultimately benefit the end consumer. The proposed signal processing and interpretation methods are based on a machine learning approach, where the tower vibration is acquired from accelerometers that measure the dynamic response caused by the effects of the environment on the towers through wind and weather conditions. Data-based models were developed to obtain a representation of health degradation, which is primarily based on the finite element model of the tower, subjected to wind excitation. This representation is also based on measurements using a mockup tower with different types of provoked degradation that was subjected to ambient changes in the laboratory. The sensor signals are preprocessed and submitted to an autoencoder neural network to minimize the dimensionality of the resources involved, being analyzed by a classifier, based on a Softmax configuration. The results of the proposed configuration indicate the possibility of early failure detection and evolution evaluation, providing an effective failure detection and monitoring system.
本文提出了一种用于检测和评估斜拉塔故障的方法，该斜拉塔是巴西互联电力系统基础设施的一部分。所提出的方法通过引入基于人工智能的预测性维护方法来提高系统可靠性，最大限度地降低服务故障和塔倒塌的风险，最终使终端消费者受益。所提出的信号处理和解释方法基于机器学习方法，其中从加速度计获取塔的振动，加速度计通过风况和天气条件测量环境对塔的动态响应。开发了基于数据的模型来获得健康退化的表示，该表示主要基于受到风激励的塔的有限元模型。该表示还基于使用具有不同类型诱发退化的模型塔进行的测量，该模型塔在实验室中受到环境变化的影响。传感器信号经过预处理，并提交给自编码器神经网络，以最小化所涉及资源的维度，然后由基于 Softmax 配置的分类器进行分析。所提出配置的结果表明了早期故障检测和演变评估的可能性，提供了有效的故障检测和监测系统。
