# Bearing Fault Diagnosis Approach under Data Quality Issues
## 1 Introduction  
The machine health monitoring (MHM) of rotary machinery plays a big role in reducing the shutdown time and keeping excellent working conditions of the machinery assets. MHM records and analyzes the raw vibrational data signals gathered by means of accelerometers. Then, it extracts the useful features from the measurements and converts them to proper actions applied to the machinery through condition-based maintenance. Fault diagnostic techniques can be classified into signal-based, model-based, and hybrid methods. The signal-based methods depend mainly on the raw data extracted from the machinery by sensors. So, the detection of machine faults is carried out through different analysis techniques applied on the extracted data. The main advantage of this method is that the mathematical modeling for the physical setup is not needed and classical signal processing techniques can be exploited in noise reduction and feature extraction. These methods include several diagnostic techniques in the time and frequency domains, including kernel density estimation (KDE), root mean square (RMS) [1], crest factor (CF) [2], Kurtosis [3, 4], Fast Fourier transform [5], wavelet transform [6], and wavelet packet transform (WPT) [7]. The main challenge is the stage of preprocessing the raw vibration measurements to attenuate noise and extract the original signal. Secondly, the model-based methods are based on developing a mathematical model simulating the behavior of the mechanical system under study. Applying this approach on any system is directly dependent on the real system model and its outputs. In this approach, the dynamic response is collected from the system under faulty and normal operating conditions [8, 9, 10]. Then, a comparison between the output response parameters and simulated data can help in assessing the condition of the mechanical component. The main drawback of this approach is the difficulty of building accurate models under the different uncertainties associated with any system running in a noisy environment. Due to the constraints associated with these approaches, a mixture of the two approaches was proposed as a hybrid method. The hybrid methods are known to be reliableand robust methods compared to other diagnostic techniques. In these methods, one or more diagnostic techniques are integrated to leverage the strength of the different integrated methods [11]. Some of the hybrid diagnostic methods are neuro-fuzzy [12], artificial neural network and support vector machines [13], and modified empirical mode decomposition [14]. The classical statistical methods are considered lengthy processes as they are required to perform feature extraction along with classification. In addition, some of the bearing fault features may be hidden in the data and they cannot be identified using the classical methods. To expedite the fault diagnosis process and unravel the hidden features, several machine learning (ML) approaches have been proposed successfully. These methods include artificial neural networks (ANN) [15], k-nearest neighbors (KNNs) [16], fuzzy cognitive networks (FCNs) [17], multi-agent system (MAS) approach using intelligent classifiers [18], and support vector machine (SVM) [19]. By analyzing and learning from the bearing data, ML methods can apply what they learned to make informed decisions about bearing health. Most ML methods can identify and classify the bearing with a high accuracy rate. However, some challenges, such as rolling element sliding, frequency interplay, external vibration, and feature sensitivity, may result in inaccurate feature extraction and classification [20]. Recently, deep learning (DL) has been used extensively in bearing fault diagnosis. Based on neural networks (NN) and artificial intelligence (AI), the DL methods have better performance [21] and automatic feature extraction [22] compared to other ML methods. Convolutional neural network (CNN) can detect the defect by learning optimal filters and extracting the best features from the raw vibration signals.