# Fault Detection in Active Magnetic Bearings Using Digital Twin Technology
## 1 Introduction  
Active magnetic bearings (AMBs) are a form of bearing technology that levitates and supports spinning equipment without any physical contact using magnetic fields and advanced control systems [1]. AMBs provide many benefits over conventional mechanical bearings due to eliminating physical contact, including less wear and friction, increased dependability, and the capacity to function at high speeds and in challenging conditions [2]. Equipment safety has commanded increasing attention recently, and magnetic bearing defect diagnostics is a popular study area. Many industrial applications, such as turbomachinery, compressors, generators, and high-speed trains, need magnetic bearings [3]. Because of the natural instability of AMBs, a stabilizing feedback control is required for their efficient operation. The AMB system has many components, such as a controller, position sensor, and actuators. The controller is an integral part of the AMB, and choosing it is very influential to the system. PID (Proportional-Integral-Derivative) control is widely used in AMB systems because of its low power consumption and ability to provide adaptability, precision, efficiency, and cost-effectiveness [4]. In an active magnetic bearing (AMB) system, the position sensor plays a role because it presents accurate and real-time monitoring on the rotor's position with respect to the stator. The simulation tool is used by [PERSON], [PERSON] et al. to help researchers compare several position sensor options and choose the best one for their particular AMB rotor-supported system [5]. Understanding the possibility of failure mechanisms in an AMB is essential to developing a reliable and safe AMB for various applications [6]. Therefore, many studies introduce methods to detect many types of failures in the magnetic bearing. [PERSON] et al. [7] developed simulation data-driven principal component analysis (PCA) based fault detection and diagnosis (FDD)with statistical analysis for fault detection and diagnosis in position sensors and actuators. They correctly detected a variety of defect categories, including bias, multiplicative, and noise addition. [PERSON] and [PERSON] [8] have presented a failure detection methodology that relies on a mathematical model and the correlation between determined states associated with the rotor and the control. [PERSON], [PERSON] et al. [9] provided a collection of three convolution kernel-based techniques and demonstrate their effectiveness in detecting faults in vibration signals obtained from planetary gearboxes in a test rig. They surpass other methods and achieve an accuracy above 98.8%. Artificial neural networks were used to establish mappings for unmeasurable conditions, while the identification of errors was accomplished by the comparison of output correlations across neural networks. [PERSON], [PERSON] [10] proposed a method for managing energy in active magnetic bearing (AMB) systems by using fault conditions to enhance system performance. [PERSON], [PERSON] [11] used Cepstrum analysis, Wigner-Ville Distributions (WVD), and enveloped Equi-Sampled Discrete Fourier transforms (ESDFT) to monitor the internal components of a radial active magnetic bearing (AMB) system. In order to identify instances of malfunction within the AMB system, it is customary to conduct a comparative analysis between the observed behaviors of the system and its expected performance under normal operating circumstances [8]. An alternative approach for identifying problems in active magnetic bearings involves conducting a simulation of the model before its physical construction. The conventional approaches to failure diagnosis involve analyzing signals in both the time and frequency domains [12]. Digital twins may be useful in this case. A digital twin is a virtual representation of a physical system that is continually updated to reflect the state of the system based on information from sensors and other sources.