• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.635-640
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• 2014

Detection and diagnosis of incipient bearing fault in an induction motor is important for the prevention of serious motor failure. This paper presents an analysis of the effect of a faulty bearing on the stator current of an induction motor. A bearing fault leads to torque oscillations which result in phase modulation of the stator current. Since the torque oscillations cause specific frequency components at the stator current spectrum to rise sharply, the bearing fault can be detected by checking out the faultrelated frequency. In this paper, a mathematical model of the load torque oscillation caused by a bearing fault is presented. The proposed model can be used to analyze the physical phenomenon of a bearing fault in an induction motor. In order to represent the bearing fault effect, the proposed model is combined with an existing model of vector-controlled induction motors. A set of simulation results demonstrate the effectiveness of the proposed model and represent that bearing fault detection using a stator current is useful for vector-controlled induction motors.

• Journal of the Korean Society of Safety
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• v.25 no.3
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• pp.1-6
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• 2010

Ball bearings are one of main components that support the rotational shaft in high speed rotary machinery. So, it is very important to detect the incipient faults and fault growth of bearing since the damage and failure of bearing can cause a critical failures or accidents of machinery system. In the past, many researchers mainly performed to detect the bearing fault using traditional method such as wavelet, statistics, envelope etc in vibration signals. But study on the detection technique for bearing fault growth has a little been performed. In this paper, we verified the possibility for monitoring of fault growth and detection of fault size in bearing outer-race by using the envelope powerspectrum and probabilistic density function from measured vibration signals.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1558-1565
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• 2015

In this paper, we propose a new method for detecting bearing faults using vibration signals. The proposed method is based on support vector machines (SVMs), which treat the harmonics of fault-related frequencies from vibration signals as fault indices. Using SVMs, the cross-validations are used for a training process, and a two-stage classification process is used for detecting bearing faults and their status. The proposed approach is applied to outer-race bearing fault detection in three-phase squirrel-cage induction motors. The experimental results show that the proposed method can effectively identify the bearing faults and their status, hence improving the accuracy of fault diagnosis.

• Journal of Mechanical Science and Technology
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• v.20 no.5
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• pp.643-651
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• 2006

The theory for a novel fault-tolerant 4-active-pole homopolar magnetic bearing is developed. If any one coil of the four coils in the bearing actuator fail, the remaining three coil currents change via an optimal distribution matrix such that the same opposing pole, C-core type, control fluxes as those of the un-failed bearing are produced. The hompolar magnetic bearing thus provides unaltered magnetic forces without any loss of the bearing load capacity even if any one coil suddenly fails. Numerical examples are provided to illustrate the novel fault-tolerant, 4-active pole homopolar magnetic bearings.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.2958-2973
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• 2024

Sliding bearings are crucial rotating mechanical components in nuclear power plants, and their failures can result in severe economic losses and human casualties. Deep learning provides a new approach to bearing fault diagnosis, but there is currently a lack of a universal fault diagnosis model for studying bearing-rotor systems under various operating conditions, speeds and faults. Research on bearing-rotor systems supported by sliding bearings is limited, leading to insufficient fault data. To address these issues, this paper proposes a fault diagnosis model framework for bearing-rotor systems based on a deep convolutional generative adversarial network (TF-DLGAN). This model not only exhibits outstanding fault diagnosis performance but also addresses the issue of insufficient fault data. An experimental platform is constructed to conduct fault experiments under various operating conditions, speeds and faults, establishing a dataset for sliding bearing-rotor system faults. Finally, the model's effectiveness is validated using this dataset.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.760-765
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• 2004

One of the obstacles for a magnetic bearing to be used in the wide range of industrial applications is the failure modes associated with magnetic bearings, which we don't expect for conventional passive bearings. These failure modes include electric power outage, power amplifier faults, position sensor faults, and the malfunction of controllers. Fault-tolerant magnetic bearing systems have been proposed so that the system can operate in spite of some faults in the system. In this paper, we designed and implemented a fault-tolerant magnetic bearing system for a turbo-molecular vacuum pump. The system can cope with the actuator/amplifier faults as well as the faults in position sensors, which are the two major fault modes in a magnetic bearing system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.304-308
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• 2004

One of the obstacles for a magnetic bearing to be used in the wide range of industrial applications is the failure modes associated with magnetic bearings. These failure modes include power amplifier faults, position sensor faults, and the malfunction of controllers. Fault-tolerant magnetic bearing systems have been proposed so that the system can operate in spite of some faults. In this paper, we designed and tested a fault-tolerant magnetic bearing system. The system can cope with the actuator faults as well as the faults in position sensors, which are the two major fault modes in a magnetic bearing system.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1955-1962
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• 2017

Induction motors are a workhorse for the industry. The condition monitoring and fault analysis are the main concern for the engineers. The bearing is one of the vital segment of the induction machine and the condition of the whole machine is decided based on the condition of the bearing. In the present paper, the vibration signal of the bearing has been used for the analysis. The first line of action is to perform a statistical analysis of the vibration signal which gives trends in signal. To get the location of a fault in the bearing the second action is to develop an index based on Wavelet Packet Transform node energy named as Bearing Damage Index (BDI). Further, Teager-Kaiser Energy Operator (TKEO) has been calculated from higher index value to get the envelope and finally Power Spectral Density (PSD) has been applied to identify the fault frequencies. A performance index has also been developed to compare the usefulness of the proposed method with other existing methods. The result shows that the strong amplitude of fault characteristics and its side bands help to decide the type of fault present in the recorded signal obtained from the bearing.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_1
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• pp.659-667
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• 2021

This paper develops the theory for a novel fault-tolerant, permanent magnet biased, 4-active-pole, double plane, homopolar magnetic bearing. The Lagrange Multiplier optimization with equality constraints is utilized to calculate the optimal distribution matrices for the failed bearing. If any of the 4 coils fail, the remaining three coil currents change via a novel distribution matrix such that the same opposing pole, C-core type, control fluxes as those of the un-failed bearing are produced. Magnetic flux coupling in the magnetic bearing core and the optimal current distribution helps to produce the same c-core fluxes as those of unfailed bearing even if one coil suddenly fails. Thus the magnetic forces and the load capacity of the bearing remain invariant throughout the failure event. It is shown that the control fluxes to each active pole planes are successfully isolated. A numerical example is provided to illustrate the new theory.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.2_1
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• pp.231-238
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• 2023

This paper develops the theory for a novel fault-tolerant, permanent magnet biased, 6-active-pole, homopolar magnetic bearing. The Lagrange Multiplier optimization with equality constraints is utilized to calculate the optimal distribution matrices for the failed bearing. some numerical examples of distribution matrices are provided to illustrate the new theory. Simulations show that very much the same dynamic responses (orbits or displacements) are maintained throughout failure events (up to any combination of 3 coils failed for the 6 pole magnetic bearing) while currents and fluxes change significantly. The overall load capacity of the bearing actuator is reduced as coils fail. The same magnetic forces are then preserved up to the load capacity of the failed bearing.