Position sensor for active magnetic bearing with commercial linear optical encoders

Tantau, Mathias; Morantz, Paul; Shore, Paul

doi:10.1016/j.cirp.2021.04.092

Cited by 3 publications

(2 citation statements)

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“…where φ is the phase of the unbalance with respect to the other axis, and U is the unbalanced magnitude in kgm. In order to exactly reproduce the dynamic model, the machine was experimentally identified to accurately assess the imbalance and to fine-tune the state-space model (3). Some identification methods for AMB systems can be found in [24,25].…”

Section: Amb Closed-loop System Modelingmentioning

confidence: 99%

“…Among these, augmented PIDs are probably the most widespread choice in industry because of their versatility, accuracy, efficiency, and cost-effectiveness. All the controllers of AMB systems rely on precise measurements of the rotor position, for which a common choice is to use inductive or eddy current sensors, even if recently self-sensing sensors and optical sensors have also been introduced [ 3 ]. The effect of the choice of sensors is discussed, for example, in [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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A Convolutional Neural Network for Electrical Fault Recognition in Active Magnetic Bearing Systems

Donati

Basso

Manduzio

et al. 2023

Sensors

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Active magnetic bearings are complex mechatronic systems that consist of mechanical, electrical, and software parts, unlike classical rolling bearings. Given the complexity of this type of system, fault detection is a critical process. This paper presents a new and easy way to detect faults based on the use of a fault dictionary and machine learning. The dictionary was built starting from fault signatures consisting of images obtained from the signals available in the system. Subsequently, a convolutional neural network was trained to recognize such fault signature images. The objective of this study was to develop a fault dictionary and a classifier to recognize the most frequent soft electrical faults that affect position sensors and actuators. The proposed method permits, in a computationally convenient way that can be implemented in real time, the determination of which component has failed and what kind of failure has occurred. Therefore, this fault identification system allows determining which countermeasure to adopt in order to enhance the reliability of the system. The performance of this method was assessed by means of a case study concerning a real turbomachine supported by two active magnetic bearings for the oil and gas field. Seventeen fault classes were considered, and the neural network fault classifier reached an accuracy of 93% on the test dataset.

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Section: Amb Closed-loop System Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%