###### Abstract
However, combining multiple features using fusion techniques may increase the complexity of the used methods, and mostly the results cannot be interpreted, in addition to the appearance of key issues of feature extraction, i.e., the high dimensionality of feature space and the large number of features. Thus, a dimensionality reduction technique and a single feature that can include rich information about the system's health conditions should be considered. Therefore, this paper uses an image, which is known as the best way that includes rich information for the efficient recognition and classification of different objects [24], and a modified PCA-based technique, which is one of the most used techniques for dimensional reduction, for vibration-based BFDD under constant and variable speed conditions (i.e., non-stationary conditions). One has to consider the above-mentioned issues when using a single time domain, frequency domain, or time-frequency domain feature that represents only some characteristics of the vibration signals, or fusing multiples features that boost the processing method complexity, the dimensionality of the feature space, and the number of the extracted features. In addition, some researchers used contemporary methods (i.e., shallow learning or deep learning) as (or and) image-processing algorithm to detect and diagnose bearing faults [25; 26; 27; 28], which are different from using image recognition methods [24], as proposed in this paper, which do not require a processing phase (less time-consuming). This paper thus proposed a novel BFDD technique that uses the ProbPlot to generate features in the form of images and then an image recognition technique based on the AVPCA [4], namely, the ProbPlot via IR-AVPCA technique, to detect and diagnose the different bearing faults under constant and variable speed conditions. This paper novelty is about using for the first time the ProbPlot to generate the features (images) directly from the raw vibration signals under different operating conditions (i.e., healthy and faulty) to be the inputs for the AVPCA algorithm that is used as a shape recognition tool for detecting present of faults (i.e., under bearing fault free (BFF) case) and diagnosing different bearing faults (i.e., outer-race fault (ORF), inner-race fault (IRF), and ball bearing fault (BBF)). On the contrary, in the previous paper [4], the authors developed the AVPCA algorithm and used it as a feature extraction and then for classification to detect and diagnose the different bearing faults using only the rotor-speed signal. Furthermore, the PCA method was used for the condition monitoring systems (CMSs) [29; 30; 31] and for bearing fault detection and diagnosis [4; 17; 32; 33]. However, it was never used as an image recognition tool to BFDD as proposed in this paper. It was only used as an image processing method as in [23], but they used the FFT to create the images (the features) and the two-dimensional PCA to reduce the dimensions, and then a minimum distance method was applied to classify the faults of bearings. On the contrary, the proposed paper uses the ProbPlot to generate the features (images) and the AVPCA algorithm for bearing fault detection and diagnosis, which has never been used before. This paper is organized as follows. The proposed ProbPlot via IR-AVPCA technique for bearing fault detection and diagnosis is described in Section 2. The experimental results of the vibration-based ball bearing fault detection and diagnosis are presented in Section 3, and the conclusion is drawn in Section 4.