In general, the bolted joints that connect and secure components together can be easily spotted in our surroundings. This joining method has been commonly used in various areas of engineering (e.g. aerospace, civil, and mechanical engineering) as it has been proven one of the most effective means to join parts together. Although it has its advantages, the vibration that bolted structures endure during service ultimately causes the bolts to loosen. This can, in turn, have a negative effect on the structure’s safety and may, at worst, cause it to fail. Routine inspections of structures are conducted on a regular basis, with some inspection categories requiring heavy equipment in order to acquire certain data. In addition, monitoring systems can be expensive to install and maintain, especially in large infrastructures. In an effort to rectify this, in this study, a piezoelectric transducer–based nondestructive technique is used in conjunction with the application of a reattachable device to investigate the possibility of creating a low-cost inspection system. The acquired data were processed by means of an artificial neural network technique that showed promising results in terms of mitigating bolt loosening.

通常，将组件连接和固定在一起的螺栓连接可以很容易地发现在我们的周围环境中。这种连接方法已广泛用于工程的各个领域（例如，航空航天，土木和机械工程），因为它已被证明是将零件连接在一起的最有效方法之一。尽管它有其优点，但螺栓结构在使用过程中所承受的振动最终会导致螺栓松动。反过来，这可能会对结构的安全性产生负面影响，并可能在最坏的情况下导致其失效。定期对结构进行例行检查，某些检查类别需要重型设备才能获取某些数据。此外，监视系统的安装和维护成本可能很高，尤其是在大型基础架构中。为了纠正这一点，在这项研究中，基于压电换能器的无损技术与可重新连接的设备一起使用，以研究创建低成本检查系统的可能性。通过人工神经网络技术处理获取的数据，该技术在减轻螺栓松动方面显示出令人鼓舞的结果。