###### Abstract  
This paper addresses the application of an image recognition technique for the detection and diagnosis of ball bearing faults in rotating electrical machines (REMs). The conventional bearing fault detection and diagnosis (BFDD) methods rely on extracting different features from either waveforms or spectra of vibration signals to detect and diagnose bearing faults. In this paper, a novel vibration-based BFDD via a probability plot (ProbPlot) image recognition technique under constant and variable speed conditions is proposed. The proposed technique is based on the absolute value principal component analysis (AVPCA), namely, ProbPlot via image recognition using the AVPCA (ProbPlot via IR-AVPCA) technique. A comparison of the features (images) obtained: (1) directly in the time domain from the original raw data of the vibration signals; (2) by capturing the Fast Fourier Transformation (FFT) of the vibration signals; or (3) by generating the probability plot (ProbPlot) of the vibration signals as proposed in this paper, is considered. A set of realistic bearing faults (i.e., outer-race fault, inner-race fault, and balls fault) are experimentally considered to evaluate the performance and effectiveness of the proposed ProbPlot via the IR-AVPCA method. bearing fault detection and diagnosis (BFDD); vibration signal; probability plot (ProbPlot); image recognition; absolute value principal component analysis (AVPCA) 2018 8 1392 2018 8 1392 2018 8 1392 2018 8 1392 2018 8 1392 2018 8 1392 2018 8 1392 2018 8 1392 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 20  
(REMs). Thus, the established analytical model is often unrealistic and not accurate enough to be a reference due to the complexity of those modern engineered systems. Therefore, the data-driven approaches, which are model-free techniques and often do not need real system input information, but only a bunch of output data (historical data), are considered as alternative solutions to deal with bearing fault detection and diagnosis (BFDD). Different existing data-driven-based BFDD techniques use data acquisition equipment and measurements, such as vibrations; chemical, temperature, and acoustic emissions; sound pressure; stator current monitoring [3]; and rotor speed analysis [4]. In particular, recent surveys [5; 6; 7; 8] indicate a clear tendency toward the vibration monitoring of REMs. These methods usually require the extraction of a set of features from the aforementioned raw signals in order to classify the bearing faults [9]. For extracting useful features, usually the acquired signals are processed using Fourier transform (FT) or its variants (i.e., fast FFT (FFT), discrete FT (DFF)) [10], wavelet transform (WT) or its variants (i.e., continuous WT (CWT), discrete WT (DWT), wavelet packet transform (WPT)) [11; 12], envelope analysis [13; 14], or statistical methods (e.g., principal component analysis (PCA), linear discriminant analysis (LDA)) [15; 16; 17; 18; 19]. Thus, the extracted features could be in time domain, frequency domain, or time-frequency domain, which usually have physical meanings, or purely statistical features, which usually do not have physical meanings [20; 21]. However, these features are generally in the form of a vector or scalar, for which they have some descriptions of the waveform in the time domain and some parameters of the spectrum in the frequency domain, or just a statistical vector index. Therefore, a single feature only describes one aspect of the vibration signals. Thus, many works combine more than one feature to improve the performance of bearing fault detection and diagnosis using different techniques to make full use of multi-features in time domain, frequency domain, time-frequency domain, or the purely statistical domain [22; 23].