## 3 Experiments and Analysis of Results  
To evaluate the effectiveness of the proposed method, an experiment was conducted in which a signal containing several different failures was analyzed based on synthetic data. The input data were fed to the input of the algorithm, sequentially passing through the stages of localization and classification. In the first stage, localization of the faults was performed based on partitioning the signal into intervals potentially containing anomalies. For this purpose, a wavelet transform was used to identify areas with characteristic changes associated with a violation of the normal behavior of the signal. The second stage involved classification of the identified anomalies, implemented using numerical optimization methods. Classification takes place in a multithreaded mode, where each thread minimizes the MSE for a specific interval based on a library of synthetic failures. The mean square deviation (MSE) calculated between signal fragments and reference patterns of known failure types was used as a similarity criterion. The following criteria were used to evaluate the detection success:  
1. For each failure actually present in the signal, the method must produce a low MSE value with the corresponding reference failure class. 2. The method must not exhibit low MSE values for classes not present in the analyzed signal. If both conditions were met, the detection was considered successful. Thus, the experiment allowed for not only testing the ability of the method to correctly identify failures but also evaluating its robustness to possible false positives. A signal containing two types of anomalies, haar and algb, was chosen as the test signal for this experiment (Figure 2). These types of failures are characterized by different features: haar represents sharp jumps or dips in the data while algb reflects a failure similar to the appearance of some polynomial. After preprocessing, including noise suppression and removal of the sinusoidal component, anomaly detection techniques were applied to the signal. The purpose of this step was to partition the signal into intervals with a high probability of containing failures (Figure 2). Figure 3 shows the intervals with possible failures based on the grouping of intervals after performing the wavelet transform. Each interval is expected to contain an anomaly because the interval boundaries are based on the peaks that were obtained after WT. The results of the algorithms are presented in Table 1. The analysis of the table shows that, for all intervals containing failures of haar and algb types, the algorithms demonstrated successful recognition. The quantitative quality assessment expressed through the mean square error (MSE) confirms this conclusion: for all correctly identified haar and algb failures, the MSE value does not exceed 0.5. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Interval** & **Fault Type** & **Error (MSE)** \\ \hline
1 & Bad signal & Nan \\
2 & Invexp & 0.739 \\
3 & Invexp & 0.927 \\
4 & haar & 0.448 \\
5 & haar & 1.644 \\
6 & haar & 1.662 \\
7 & haar & 1.209 \\
8 & Bad signal & Nan \\
9 & Bad signal & Nan \\
10 & Bad signal & Nan \\
11 & haar & 0.383 \\
12 & haar & 1.505 \\
13 & Invexp & 3.785 \\
14 & Bad signal & Nan \\
15 & Bad signal & Nan \\
16 & Invexp & 0.696 \\
17 & algb & 2.142 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results. However, in addition to the true positives, there was one false positive case associated with the Invexp algorithm. The interval erroneously classified as containing an anomaly actually has no pronounced signs of failure. This problem can be solved in several ways:  
1. Increasing the number of iterations: When optimizing the parameters of the Invexp algorithm (or any other algorithm prone to false positives), a larger number of iterations can lead to a more accurate minimization of the target function and, consequently, a lower probability of false positives. 2.