Design of online detection algorithm for bolt fastening state in substation based on deep learning

As the core component of the power system, the normal operation of the substation is very important to the reliability of the power supply. As one of the key elements to connect substation equipment, the maintenance of the bolt fastening state has an important impact on the safety and stability of equipment. The purpose of this study is to propose an algorithm based on deep learning, which is used to detect the fastening state of substation bolts in real time to ensure the normal operation and maintenance of equipment. In this study, we collected a large number of substation bolt state data and designed a deep learning model. Firstly, we preprocessed the data with Gramian Angular Field (GAF) and Piecewise Aggregate Approximation (PAA). Then, we put forward a diagnosis model of the acoustic emission signal of substation bolts based on two-dimensional time series and Attention Mechanism (AM), and then use this model to capture the time series information of bolt state. The experimental results show that after 100 iterations, the diagnostic accuracy of the model has reached the highest, with an average accuracy of about 98%. Our deep learning algorithm can highly accurately identify the fastening state of bolts, which provides an effective guarantee for the safety and reliability of substations, and also has the ability to monitor in real-time.