Optimal Design of Novel Electromagnetic-Ring Active Balancing Actuator with Radial Excitation

Abstract

Imbalance vibration is a typical failure mode of rotational machines and has significant negative effects on the efficiency, accuracy, and service life of equipment. To automatically reduce the imbalance vibration during the operational process, different types of active balancing actuators have been designed and widely applied in actual production. However, the existing electromagnetic-ring active balancing actuator is designed based on an axial excitation structure which can cause structural instability and has low electromagnetic driving efficiency. In this paper, a novel radial excitation structure and the working principle of an electromagnetic-ring active balancing actuator with a combined driving strategy are presented in detail. Then, based on a finite element model, the performance parameters of the actuator are analyzed, and reasonable design parameters are obtained. Self-locking torque measurements and comparative static and dynamic experiments are performed to validate the self-locking torque and driving efficiency of the actuator. The results indicate that this novel active balancing actuator has sufficient self-locking torque, achieves normal step rotation at 2000 r/min, and reduces the driving voltage by 12.5%. The proposed novel balancing actuator using radial excitation and a combination of permanent magnets and soft-iron blocks has improved electromagnetic efficiency and a more stable and compact structure.

Real-Time Intelligent Diagnosis of Co-frequency Vibration Faults in Rotating Machinery Based on Lightweight-Convolutional Neural Networks

“…With the growth of intelligent equipment, selfdiagnosis of the equipment has become a necessity [1,2]. Self-recovery regulatory systems such as active balancing and automatic balancing algorithms and shafting alignment algorithm to suppress and locate excessive vibration require real-time fault diagnostics [3,4].…”

Real-time Intelligent Diagnosis of Co-Frequency Vibration Faults in Rotating Machinery Based on Lightweight-Convolutional Neural Networks

“…Meanwhile, with the development of intelligent equipment, one of the inevitable needs is self-diagnosis of the equipment [1,2]. Real-time fault diagnosis is the prerequisite for self-recovery regulation mechanisms such as active balancing and automatic balancing algorithms and shafting alignment algorithm to suppress and location excessive vibration [3,4]. The fault types with the co-frequency characteristics include unbalance, misalignment, shaft bending, and slight loosening faults, etc.…”

Synchronization and Stability of a Nonlinear Vibrating Mechanical System Characterized by Asymmetrical Piecewise Linearity

“…When it comes to vibration, in most cases, it is harmful and can damage the machine sometimes. Hence, some research works [1][2][3] stressed the theme of vibration control and suppression enough. In engineering, however, some machines are designed and operated by utilizing vibrations, such as vibrating feeders, vibrating conveyors, probability screen, most of which are based on the selfsynchronization theory of vibrators (unbalanced rotors driven by induction motors separately).…”