# Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning
###### Abstract
[[https://doi.org/10.3390/app11104602](https://doi.org/10.3390/app11104602)]([https://doi.org/10.3390/app11104602](https://doi.org/10.3390/app11104602))**  
**Citation:**  
[PERSON]; [PERSON]-M. Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning. _Appl. Sci._**2021,** 11, 4602. [[https://doi.org/10.3390/app1104602](https://doi.org/10.3390/app1104602)]([https://doi.org/10.3390/app1104602](https://doi.org/10.3390/app1104602))**  
**Citation:**  
[PERSON]; [PERSON] Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning. _Appl. Sci._**2021,** 11, 4602. [[https://doi.org/10](https://doi.org/10)]([https://doi.org/10](https://doi.org/10)). In general, this method works based on data collection of dynamic properties of the bearing and comparing them with their healthy state. In classical condition monitoring, faults are usually detected by one of the following techniques: vibrations, acoustic emission, and motor current signature analysis [2]. In this work, the vibration sensor is used for data collection. Different methods have been introduced for bearing-fault detection and identification (BFDI) and are categorized into the following four main groups: signal processing-based approaches, model-based schemes, machine/deep learning methods, and hybrid approaches [3,4]. Signal processing-based techniques have been used to diagnose defects in various studies, but robustness in noisy conditions is the principal challenge of these techniques [5,6]. On the other hand, model-based approaches are robust and stable, but modeling the physical systems in uncertain conditions is the main challenge of this method [7]. Most recently, machine/deep learning schemes have been applied in numerous applications, but reliability is the foremost challenge of these methods [4]. Hybrid algorithms are a combination of two or three of the above algorithms to make new reliable and effective techniques for BEDI [8]. In this research, a hybrid algorithm based on the incorporation of the model-based approach, data-driven technique, artificial intelligence method, and machine learning algorithm is prescribed. Digital twins are important to confirm the style of a physical system. This basic concept is introduced to make a connection between the modeling and the real-time measurement from the systems. Moreover, the digital twin is used for state prediction [9]. To design a BEDI based on a digital twin (DT), the combination of the model-based approach, data-driven technique, artificial intelligence approach, and machine learning scheme is used. The central concept of a DT is designed based on physical system modeling. To model the real systems, diverse procedures have been used that are categorized into two principal groups: (i) mathematical-based system modeling such as the newton Euler technique, Lagrange method, and (ii) data-driven-based system modeling such as system identification techniques, artificial intelligence methods, machine learning approaches, and deep learning schemes [10]. Mathematical modeling for vibration signals in the bearings has been presented in [10,11]. The main affirmative point for vibration modeling in the bearing based on the mathematical-based approach (such as multi degrees of freedom vibration bearing modeling) is reliability. However, the complexity and difficulty of the modeling in the presence of uncertain conditions can be the most important challenge of this technique. To reduce the complexity of system modeling based on mathematical algorithms, data-driven approaches have been introduced [12,13]. The applications of the fuzzy technique in function approximation (system modeling) have been reported in [14]. Tuning the gain updating factors and membership functions are significant challenges of the classical fuzzy technique for function approximation. The next candidate for function approximation in nonlinear systems is a neural network.