# Bearing Severity Fault Evaluation Using Contour Maps--Case Study
## 1 Introduction  
At present, the continuous and correct operation of machines is crucial in an increasingly competitive industrial sector. Thus, fault monitoring and diagnosis are some of the most critical aspects of equipment design and maintenance [1]. According to [PERSON] et al. [2] and [PERSON] et al. [3], rotating machinery plays an important role in modern industries, with the bearing being one of the most critical components. [PERSON] et al. [4] stated that this component is responsible for up to 50% of failures in rotating machinery, while [PERSON] and [PERSON] [5] argued that only 10% of bearings reach the end of their useful life. This has created the need for Condition Monitoring (CM) systems, which ensure the safety and reliability of the operation of the equipment [1, 6, 7, 8]. CM focuses on the study of the behaviour of the machine, by applying non-intrusive techniques such as \"Vibration Analysis,\" given its early appearance [9, 10]. In this way, CM identifies and characterises anomalous changes in operations due to the degradation of the mechanism [11]. There are three key stages to ensure the highest efficiency in fault diagnoses, such as log capture, data processing and information management [12, 13]. On the other hand, the ISO 13373 [14, 15] standard provides a detailed guide of the parameters to be used at each stage. It should be noted that this first stage has been treated in a previous study [16], where the authors presented a low-cost, high-frequency data acquisition system based on a Raspberry Pi, which allows for the application of CM to bearings in rotating machinery. Continuing this research, this work focuses on the second stage, in which data processing methods are studied. The ISO 13373-2 [15] standard defines this stage as the group of activities that, based on the processing of the acquired signals, allows for the identification of characteristics of the mechanism under study (in this case, bearings). In recent years, different methods have been proposed for the extraction of information from vibration signals, related to the state of bearings. One of the most powerful tools used at present is artificial intelligence [1, 17], in which the extraction of characteristics playsan important role in the diagnosis. In this way, feature classifiers such as Support Vector Machine (SVM) algorithms [18; 19], deep learning techniques [20; 21] or both [22] have taken centre stage. [PERSON] et al. [6] proposed a model based on deep learning, which is capable of adapting to industrial environments, where changes in load and noise are the main drawback. On the other hand, [PERSON] et al. [23] presented an unsupervised learning method with better accuracy in the classification of fault characteristics, compared to existing machine learning and deep learning methods. In addition, traditional data processing techniques include diagnostic methods based on different techniques; for example, an autoregressive model has been proposed for filtering signals [24] or the decomposition of the signal for the extraction of characteristics using Empirical Mode Decomposition [25; 26] and Ensemble Empirical Mode Decomposition [27; 28] has been performed. Moreover, [PERSON] et al. [29] and [PERSON] et al. [30] used signal demodulation by envelope analysis to extract characteristic fault frequencies. On the other hand, some authors have used tools such as spectrograms [31; 32], kurtograms [33] or bispectra [33; 34] to detect the presence of transient events in a signal. There are other concepts of surface analysis, such as contour maps (isolines), which have been widely used in various fields to represent the study environment, instead of specific data. In medicine [35; 36], contour maps are used for the segmentation of two-dimensional images, which allows for the elimination of blurring effects and improves the identification of regions corresponding to the study objects. In topography [37; 38; 39], contour maps are used to indicate elevations and features of geographical accidents.