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Abstract This paper presents a novel bolt looseness detection method for power transmission towers based on vibration signal analysis. The proposed method utilizes pulse excitation to extract the vibration signal of the tower, which is then adaptively decomposed using the Variational Mode Decomposition of Spider Wasp optimizer (SWVMD). This overcomes limitations of traditional Variational Mode Decomposition methods by leveraging bio‐inspired optimization to improve signal decomposition. Simulated signals processed with different optimization methods verify the superiority of the SWO approach. Field tests on a 110‐kV transmission tower further demonstrate the effectiveness of the proposed SWVMD technique for analyzing on‐site vibration data. A new improved intrinsic multiscale sample entropy feature is also introduced for bolt state characterization. A Spider Wasp Support Vector Machine classifier is developed to realize accurate bolt loosening monitoring using the extracted features. Dynamic response tests under varying bolt conditions show that the method can identify early loosening and reduce tower damage risks compared to conventional techniques. This novel vibration‐based detection framework presents an innovative application of nature‐inspired computing for power infrastructure health monitoring.

The stability of transmission tower bases is key to ensuring the safe operation of power lines. This paper proposes a joint displacement-control technique for foundation-inclined piles and prestressed foundation tie beams to address the problem of tower base displacement and durability degradation caused by environmental factors. A finite element model of an exposed-pile transmission tower conforming to the structural characteristics of the actual line tower was established based on the current situation of Tower 292 of the ±800 kV Tianzhong line in Xinjiang, China. Three different displacement-control schemes were analyzed under the combined effects of tower line load, ice-cover load, and wind load, including changing exposed pile height, changing inclined pile tilt angle, and increasing the prestressed foundation tie beam. The analysis shows that the combined displacement-control technology of foundation-inclined piles and prestressed foundation tie beams can reduce the horizontal displacement of EHV tower foundations by more than 50%, which greatly reduces safety problems caused by tower displacement and effectively improves tower durability.

The displacement of transmission tower feet can seriously affect the safe operation of the tower, and the accuracy of structural health monitoring methods is limited at the present stage. The application of deep learning method provides new ideas for structural health monitoring of towers, but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning (DL). In this paper, we propose a DT-DL based tower foot displacement monitoring method, which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element method. Then the vibration signal visualization and Data Transfer (DT) are used to add tower fault data samples to solve the problem of insufficient actual data quantity. Subsequently, the dynamic response test is carried out under different tower fault states, and the tower fault monitoring is carried out by the DL method. Finally, the proposed method is compared with the traditional online monitoring method, and it is found that this method can significantly improve the rate of convergence and recognition accuracy in the recognition process. The results show that the method can effectively identify the tower foot displacement state, which can greatly reduce the accidents that occurred due to the tower foot displacement. [ABSTRACT FROM AUTHOR]