## 1 Introduction  
Bolted connections are a widely used form of structural connection in steel truss structures. When bolted connections are subjected to mechanical actions such as vibration, impact, and creep, their connection state often changes, resulting in loosening, slippage, or even detachment. This can compromise the integrity of the structure and endanger its reliability and safety. Therefore, monitoring the status of bolt connections is of great significance for ensuring structural safety, preventing major safety accidents, and reducing the severity of accidents. Traditional bolt loosening damage detection methods can be divided into two categories: based on modality and based on impedance [1; 2; 3; 4; 5]. Both of these methods adopt a contact measurement approach, where the sensor typically needs to be located near the surface of the point being measured and properly attached. This is not easily achievable for a steel truss structure with a large number of connection nodes and bolts [6]. Sound is essentially the propagation of vibrations through the air, and similar to vibration signals, sound can serve as an important source of information reflecting the state of damage. Just as an auto mechanic can diagnose the type of car trouble based on the sound of the engine, railway maintenance workers often use hammers to strike the rails, judging whether there are any loose bolts by the sound. Cableway maintenance workers also frequently determine whether the bolts on the passenger cableway support rods are loose by listening to the sounds. Sound propagation has advantages such as omnidirectional spread, high temporal resolution, and no visibility limitations. At the same time, sound measurement is a non-contact measurement method. The microphone array measurement device used is relatively low-cost and easy to install, and it can effectively avoid the saturation and failure phenomena that are common with accelerometer measurements [7; 8; 9]. [PERSON] et al. [10] proposed a percussion-based cup-lock scaffold loosening detection method using a convolutional bidirectional long short-term memory (CBLSTM) model to classify the percussion acoustic signal features, which provides a new idea for the stability monitoring of scaffolding systems. Next, to effectively prevent the bolted joint from loosening underwater, [PERSON] et al. [11] proposed a detection method utilizing the knocking sound and KNN algorithm. [PERSON] et al. [12] further investigated the knocking method of bolting loosening monitoring in the presence of noise interference and data insufficiency, and significantly improved the monitoring accuracy by proposing noise interference-resistant speech features and prototype network classifiers. Focusing on bolt loosening monitoring in the earliest stage, [PERSON] et al. [13] combined variational modal decomposition and residual network (ResNet) to achieve effective monitoring based on intrinsic feature extraction of knocking sound. [PERSON] et al. [14] innovatively used a feature-reduced multiple stochastic convolutional kernel transform (FM-ROCKET) model in underwater bolt flange loosening detection, combining deep learning and shallow learning methods, which significantly improves the effect of detection. Regarding the issue of uneven knocking force, [PERSON] et al. [15; 16] proposed a force-adaptive percussion method for assessing bolt loosening, which utilizes sound phase features to mitigate the impact of irregular percussion forces, and also developed a rapid detection method for multiple-bolt loosening in steel beam-column joints based on percussion techniques, enhancing the application potential of percussion-based methods in engineering. In the field of bolt loosening detection, although scholars have made significant progress through diversified methods and techniques, it still faces a series of urgent problems. Firstly, in terms of motivation, the current mainstream research generally adopts the knocking method.