## 1 Introduction
The goal of this paper is to compare different signal-based analysis methods, applying them in the study of the first bearing dataset released in 2014 by NASA IMS Center, which represents a proper paradigmatic test bench to establish their potential. Unlike the other two datasets, each involving the presence of a single fault and so often regarded as an ideal case study to assess the feasibility of new techniques (see for example [10; 11; 12]), dataset 1 is well-known in the scientific community as a demanding challenge to analyze due to the occurrence of three different faults, two of them on the same bearing. However, its analysis is not blind because the Center itself provided researchers and industrialists with the diagnostic report of the faults detected at the end of the experiment, and since its publication the dataset has been intensively studied. In [13], all the three datasets are successfully analyzed using Short Time Fourier Transform (STFT), the Squared Envelope Spectrum (SES) based on the Kurtogram and Cyclic Spectral Coherence (CSC), but especially for bearing 4, fault signatures remained barely detectable [14], draws a comparison between the performance of wavelet decomposition-based denoising and Morlet wavelet filter-based denoising methods applied on dataset 1, attesting the latter as more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals. The study carried out in [15] proposes a hybrid scheme that combines EWT (Empirical Wavelet Transform) threshold filtering and the Teager energy spectrum method to approach dataset 1, showing a better modal aliasing suppression effect and more explicit frequency division segments than the traditional EMD (Empirical Mode Decomposition) threshold filtering. In [16], Authors test the potential of [PERSON]'s parameters as diagnostic tools. In [17], a data-driven method based on Gath-Geva clustering and operating on the energy spectrum and RMS value is used to identify the degraded state of IMS bearings, proving good classification performance and resulting in fault diagnostic accuracy, while in [18] a LAD (Logical Analysis of Data)-based decision model, an innovative supervised learning data mining methodology, is designed and tested to detect faults on the very same bearings. Ref. [19] deals with the phase of fault prognosis presenting a Bayesian approach for the RUL (Remaining Useful Life) prediction of rolling bearings: a modified Paris crack growth model is developed, and RUL distribution is estimated applying the Metropolis-Hasting algorithm to the Markov chain Monte Carlo method. In [20], the so-called feature engineering, aiming at Prognostics and Health Management(PHM), is conducted applying three metrics (Correlation, Monotonicity and Robustness) to assess the suitability of statistical parameters as health indexes, but only considering the third bearing of the test rig. The state-of-the-art on the analysis of IMS dataset 1 previously summed up could be further extended, but it is a fact that, probably due to the complexity of the case, these signals have been so far much more frequently studied with data-driven methods (primarily Neural Networks, as in [21; 22], and the Hidden Markov Model, as in [23; 24; 25; 26]) or hybrid approaches ([27; 28; 29; 30; 31; 32]) than with signal-based techniques.