## 1 Introduction
implemented a leakage detection algorithm that utilized a scheme with principal component analysis [15]. Some other techniques focused on flowrate balance signals with Bayesian networks [16], phase transformation and maximum likelihood [6], fuzzy logic [17; 18], and artificial neural networks [19; 20]. Furthermore, these classifying methods did not give a satisfactory result based on the input of raw AE signals. Hence, a preprocessing setup is necessary to extract better information from the recorded AE signals. However, even this application has certain limitations, such as wave attenuation, inference of background noise, and inaccessible sensor locations. One of the processes that has the largest effect on measuring leakage signals is the interaction between the coal fuel stream from the fuel injection inlet and the tube's wall. The AE signal from the fluid leakage associated with the tube's wall has a small amplitude and is non-stationary, but it spreads over a long duration; it is usually covered by another signal with a high signal-to-noise ratio (S/N), which can be caused by the excitations generated by the coal powder impacting with the metal tubes. This impact phenomenon randomly generates sharp and transient impulses with high amplitude. To filter the fluid leakage signal, this kind of high S/N impulse waveform needs to be detected and removed from the recorded signal. Previously, the impulse detection problem has usually been solved by setting a threshold (e.g., a soft threshold [21; 22], wavelet threshold [23], constant false alarm rate for object detection [24]) and a cut-off range where the amplitude of the signal exceeds the threshold. However, this approach is easily affected by random peaks from noise, and the cut-off range does not cover the whole length of the impulse. Based on the concept of object detection [25; 26], we defined the fuel-attacked impulses as salient objects over the remaining background signal, and the object detection and object localization algorithms are employed to identify this kind of impulse. Despite the achievements of AI-based methods, such as the Hidden Markov [27], fuzzy algorithm [28], and convolution neural network [29; 30] methods, the design of an impulse detection algorithm still relies on the patterns of signals, which have been preliminarily studied in [31]. However, the leakage signal in the boiler tube contains complex intrinsic information with a different pattern, and the patterns of the impulse signal and leak signal are hardly differentiated. Therefore, this paper proposes a deep learning flexible boundary regression (DLFBR) method to capture the different intrinsic information of the object, which is the attacked impulses, and the background signal, which is mainly caused by the leakage signal. DLFBR frames impulse detection as a regression issue to spatially separate the boundary box surrounding the impulse. It first uses a one-dimensional convolution neural network (1D-CNN) to divide the time signal into grids with different grid sizes. With respect to each grid size, a confidence mask and a feature map are created to mark the position and extract the object-related information. Then, the boundary regression algorithm yields the boundary around the object that needs to be detected based on a different relative scale of the feature map and confidence mask. To purify the leakage signal, this paper proposes an impulse detection methodology using the DLFBR model to detect impulses caused by the impact of the fuel stream on the metal tube. A summary of the main contributions of this paper are presented as follows:  
1. This research proposes a preprocessing method for shape extraction (SE) to extract the global shape signal, which contains intrinsic information on the impulse and a low level of random noise in the macro-level that corresponds to the length of the sampled signal. 2. Then, the main proposed method uses DLFBR, which is structured based on a 1D-CNN with the boundary regression layer.