## 1 Introduction
The WPT offers the advantage of allowing the wavelet transform to be applied multiple times by selecting the decomposition level, resulting in enhanced filtering. This is why it has been utilized in several studies [35, 36, 37]. One of the primary challenges with these techniques is determining the optimal parameters, such as the mother wavelet and decomposition level, for effective analysis. Early studies on wavelets and AE revealed [26] that better results were achieved when the mother wavelet closely resembled the original AE waveform. The Symlet wavelet of order 8 was found to be suitable when a single pulse generated the wave, whereas the Daubechies db40 wavelet was more appropriate for waves generated by bursts [38]. Consequently, several studies on AE signals have examined the selection of the optimal mother wavelet [39], but, as there is no universally optimal mother wavelet for AE signals yet, it is essential to develop a methodology that selects the best one for each specific case. This will be the focus of this work, as ease and speed of detection are crucial for enhancing the monitoring process. When it comes to decomposition levels, higher levels require more computation time, which may not be suitable for continuous monitoring tasks. Therefore, selecting the optimal parameters for the wavelet technique is vital to ensure a faster and more efficient diagnosis during service. The present study addresses AE applied to monitor fretting fatigue damage in solid railway axles, a topic for which no related works have been identified in the literature, apart, as said, from a previous study proposed by the current authors [22]. Such a study centered on the application of an unsupervised classification algorithm to AE acquisitions gathered during a full-scale fatigue test performed using a three-point rotating bending bench (Vitry test rig). Artificial notches were introduced into the tested axle to initiate and propagate fretting fatigue damage until significant fatigue cracks developed. The algorithm applied initial clustering, based on a pattern recognition approach, to isolate signals related to potential damage, which proved to be challenging. While the approach was ultimately successful, it required post-processing of experimental data, making it less suitable for early in-service damage detection and resulting in a slower overall procedure. In contrast, the current work proposes a new, structured signal analysis workflow aimed at achieving fast and reliable crack initiation detection for in-service monitoring. This methodology includes: (1) selecting optimal WPT parameters using a supervised classification model based on ensemble bagged trees; (2) assigning class labels according to temporal segmentation of AE data, supported by change point analysis (CPA) on signal energy to differentiate early and advanced fatigue stages; (3) applying the selected WPT parameters for feature extraction from raw AE signals; and (4) employing the trained classifier to detect crack initiation in real time without the need for prior noise filtering or clustering. This approach enhances automation and efficiency in condition-based monitoring of railway axles.