## 1 Introduction  
The aging of power plant piping, both domestically and internationally, has increasingly resulted in severe leaks, causing substantial harm to human safety, the environment, and economic stability [1, 2, 3]. Consequently, the prompt identification of leaks in aging piping systems is important for ensuring safety and economic efficiency [4, 5, 6]. Historically, various sensors, including acoustic, vibration, pressure, and ultrasonic types, have been explored for early leak detection [7, 8, 9]. However, accurately detecting leaks remains challenging due to the varying physical characteristics of leaks, which are influenced by factors such as pressure, material properties, and environmental conditions. Ambient noise, in particular, significantly complicates the differentiation of leak signals, leading to reduced detection rates [10, 11]. With the advent of large-scale data, advancements in deep learning (DL) and machine learning (ML) technologies have progressed rapidly, enabling solutions to a range of complex problems [12, 13, 14]. These technologies have become integral in industrial applications,excelling in modeling complex patterns and delivering accurate predictions [15; 16; 17]. Artificial intelligence (AI), in particular, has emerged as a transformative tool for enhancing productivity, reducing costs, and improving safety across various sectors, owing to its advanced learning and inference capabilities [18; 19; 20]. This has raised significant expectations for the role of AI in detecting minor leaks in aging piping systems, fostering active research in this area [21; 22; 23]. For instance, [PERSON] et al. achieved 93% accuracy in detecting vibrations caused by leaks using low-power accelerometers and a support vector machine (SVM) model [24]. Similarly, [PERSON] et al. reported detection performances of 98.4% and 96.67% using convolutional autoencoders and convolutional neural network (CNN) models, respectively [25]. These studies demonstrate the potential of AI to advance fine leak detection in aging piping systems. Despite these advancements, existing methods face significant limitations. Traditional signal processing techniques often fail to accurately distinguish leak signals from noise, as noise can distort the defining characteristics of leak signals. Additionally, achieving high accuracy and performance in leak detection requires extensive data, yet collecting leak data in real-world environments is both challenging and hazardous. Even under controlled conditions that simulate real-world scenarios, the data collected are often insufficient due to the limitations of the experimental setup. Therefore, there is an urgent need for research focused on differentiating leak signals from noise with high accuracy and reliability, even when working with small datasets. To address these challenges, this paper proposes a feature fusion DL model based on stepwise transfer learning (TL) [26; 27; 28] that utilizes an attention mechanism. Time-domain root mean square (RMS) and frequency-domain frequency patterns are generated from one-dimensional (1D) time-series acoustic signals acquired using multiple acoustic sensors. These patterns are then used to train the feature fusion model based on TL. The proposed model comprises a two-stage process (Stages 1 and 2). In Stage 1, key features are extracted from RMS and frequency pattern signals using 1D CNN models for each domain after preprocessing the source domain data. In Stage 2 of the TL process, the classifiers of the 1D CNN models trained in Stage 1 for each domain are removed, and the output features of these models are combined. The preprocessed RMS and frequency pattern signals from the target domain data are then input into a model enhanced with bidirectional long short-term memory (LSTM) [29]. The bidirectional LSTM captures both forward and backward temporal dependencies, enriching the understanding of the model of sequence data.