Variable reluctance bearing generators applicable in condition monitoring of bearing cages. (1st July 2023)

• Record Type: Journal Article
• Authors: Miao, Yijun; Gao, Shuai; Kong, Yun; Jiang, Ziyuan; Han, Qinkai; Chu, Fulei
• Is Part Of: Mechanical systems and signal processing. Volume 194(2023)
• Publication Date: 2023-07-01
• Subjects: Variable reluctance -- Rolling bearing -- Energy harvesting -- Condition monitoring -- Localized faults; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2023.110249
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: This study proposes a variable reluctance bearing generator (VRBG) based on a ribbon-shaped soft ferromagnetic cage, that is used in the condition monitoring of bearing cages. A VRBG is composed of an iron coil and a permanent magnet, which are placed on the side of the rolling bearing and fixed to the outer ring of the bearing. Given that the rotating components of the bearing are unchanged, the VRBG utilizes the variable reluctance effect caused by the rotation of the ribbon-shaped soft ferromagnetic cage. Furthermore, it realizes the purpose of converting part of the rotation energy of bearings into electric energy in a non-contact manner. After the structural introduction, modeling and simulation analysis of the VRBG were conducted to explain the power generation mechanism and predict the output characteristics. Based on the VRBG prototype, the variation in the frequencies of both the output voltage and current with the rotating speed was analyzed, and the voltage waveforms predicted using the simulation model were compared and verified with the tested waveforms. The effects of the design parameters (including the number of turns of the coil and the gap between the cage and coil) on the output voltage were discussed. For a single VRBG and multiple VRBGs in series, the variation curves of both the output voltage and current with the load resistance were tested. The optimal resistance values corresponding to the maximum output power were then obtained. A rolling bearing cage fault-test bench was built and the fault characteristic frequencies of the VRBG output voltage were identified when cracks appeared on the cage surface. Combined with fast Fourier transformation and deep convolutional neural networks, the classification and identification of cage surface crack size were studied. The results indicate that the output signal of the VRBG can be used to determine the cage surface crack size, and the classification accuracy exceeds 99%. The proposed VRBG has good application prospects for the condition monitoring of rotating machinery. Highlights: VRBG based on a ribbon-shaped soft ferromagnetic cage was proposed. Simulation analysis of the VRBG was conducted to predict output characteristics. Variation in voltage and current frequencies with rotating speeds was analyzed. VRBG output signal can be used to determine cage crack size with accuracy of 99%. It has good application prospects for condition monitoring of rotating machinery.