###### Abstract  
Vibration signals captured with an accelerometer carry essential information about Rolling Element Bearings (REBs) faults in rotating machinery, and the envelope spectrum has proven to be a robust tool for their diagnosis at an early stage of development. In this paper, Cepstrum Pre-Whitening (CPW) has been applied to REBs' signals to enhance and extract health-state condition indicators from the preprocessed signals' envelope spectra. These features are used to train some of the state-of-the-art Machine Learning (ML) algorithms, combined with time-domain features such as basic statistics, high-order statistics and impulsive metrics. Before training, these features were ranked according to statistical techniques such as one-way ANOVA and the Kruskal-Wallis test. A Convolutional Neural Network (CNN) has been designed to implement the classification of REBs' signals from a Deep Learning (DL) point of view, receiving raw time signals' greyscale images as inputs. The different ML models have yielded validation accuracies of up to 87.6%, while the CNN yielded accuracy of up to 77.61%, for the entire dataset. In addition, the same models have yielded validation accuracies of up to 97.8%, while the CNN, 90.67%, where signals from REBs with faulty balls have been removed from the dataset, highlighting the difficulty of classifying such faults. Furthermore, from the results of the different ML algorithms compared to those of the CNN, frequency-domain features have proven to be highly relevant condition indicators combined with some time-domain features. These models can be potentially helpful in applications that require early diagnosis of REBs faults, such as wind turbines, vehicle transmissions and industrial machinery. c 2022 2022 2022 2022 2022 2022 2022 20222 2022 2022 20222 2022 2022 20222  
and frequency domain features [2]. [PERSON] & [PERSON] conducted an in-depth study on the nature of vibration signals at different stages of rolling elements' impacts with faults with applications in defect size estimation [3]. [PERSON] & [PERSON] thoroughly analysed the Case Western Reserve University (CWRU) signals with their benchmark method, based on pre-processing them with Discrete/Random Separation and using the envelope spectrum to identify faults [4]. In the scope of artificial intelligence [5; 6], numerous papers have been published proposing different methods to train models for REBs diagnosis. In the field of ML, [PERSON] et al. proposed a method based on SVM and the Markov model to predict the degradation process of REBs [7]. [PERSON] et al. extracted features based on Acoustic Emission (AE) analysis with the Hilbert-Huang Transform (HHT) and used them to train a k-NN model [8]. [PERSON] et al. proposed a non-linear observer-based technique called Advanced Fuzzy Sliding Mode Observer (AFSMO) to improve the average performance of fault identification with a DT model [9]. In the field of DL, [PERSON] et al. used the second-generation wavelet transform to improve the robustness of DL-based fault diagnosis [10]. [PERSON] et al. converted vibration signals from REBs into greyscale images and used them to extract features to serve as inputs to their proposed CNN [11]. [PERSON] et al. conducted a DL-based benchmark study evaluating four models: a multilayer perceptron, an auto-encoder, a CNN and an recurrent neural network [12]. [PERSON] et al. suggested obtaining Defect Signature Wavelet Images (DSWIs) from REBs signals, which show the visualization of the discriminated pattern for different types of faults, to train a CNN [13]. Nevertheless, the known advantages offered by the cepstral domain to enhance cyclostationary components in the envelope spectrum are not sufficiently developed by the community in order to extract functional condition indicators that can train ML models to diagnose REBs faults automatically.