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The bifurcation characteristics of the active magnetic bearing-rotor system subjected to the external excitation were investigated analytically when it was operating at a speed far away from its natural frequencies. During operation of the system, some nonlinear factors may be prominent, for example, the nonlinearity of bearing force and current saturation. Nonlinear factors can lead to some complicated behaviors, which have negative effects on the operating performance and stability. To analyze the bifurcations happening at the speed far away from harmonic resonances, an approximate analytical method that can be applicable to the bifurcation analysis of the forced vibration system was proposed. By applying it to the active magnetic bearing-rotor system, multiple static equilibriums and periodic solutions were obtained, and then, the stability analysis was conducted based on Floquet theory. The validity and accuracy of the approximate analytical method were verified by the numerical integration method and generalized cell mapping digraph method. It was found that there was supercritical pitchfork bifurcation of static equilibrium in the active magnetic bearing-rotor system. The influences of external excitation and controller parameters on dynamical characteristics were discussed. Based on analysis results, controller parameters were also improved to prevent nonlinear behaviors and improve system performance.

The vibration signals captured by multiple sensors can be fused and provide rich information to distinguish faults of rotating machinery. However, previous studies mostly regard multiple signals as individual signals and ignore the coupling relationship between signals resulting in a loss of information. To overcome the above problem, this paper proposes a new multi-sensor data fusion algorithm for identifying faults. First, space-time fragments are constructed to combine multiple signals together considering the space and time relationship between signals. Second, histograms of multi-channel shaft orbit based on space-time fragments are extracted to describe faults. Third, K-nearest neighbor is selected as the classification method. The experiments are carried out on a rig of rotating machinery supported by active magnetic bearings and demonstrate the effectiveness of our proposed algorithm.

Active magnetic bearings (AMBs) are widely applied in high-speed rotating machinery, especially those in special environments. In the designing and adjusting an AMB system, the mathematical model of the system plays an important role. An AMB system is essentially nonlinear, thus the widely applied linear model cannot fully reflect the system characteristics in some cases. Cell mapping (CM) method is a global analysis method for nonlinear dynamic systems, which can obtain the global structure of a nonlinear system and the influences of varying parameters on it. In this paper, a CM type method is applied to analyze the nonlinear characteristics of the AMB system. Operational data from an actual AMB system is analyzed. During the on-site commissioning experiment, this system was unstable under specific conditions and was stabilized after modifying the controller. Analysis based on CM method reveals that the main cause of the instability is the bifurcation due to the nonlinearity of the system and the on-site treatment was valid.

In high temperature reactor pebble-bed modules (HTR-PM), the helium blower is the key equipment which is in the primary loop and drives the helium to exchange the energy. The usage of active magnetic bearing (AMB) is to avoid the lubricating oil leaked and keep the environment of helium pure. However, helium blower test needs to proceed under the condition of helium loop, which is difficult in early construction stage and will result in much more time and money cost. In this paper, we propose and produce a new device to simulate the dynamic force to build a force environment similar to the helium loop. The device called aerodynamic force simulator (AFS) derives from the idea of active magnetic bearing and utilizes electromagnetic force replacing aerodynamic force when helium blower works. Active magnetic bearing commissioning and dropping load test of helium proceed successfully with the utilization of aerodynamic force simulator. Through a series of tests, it is proved that the aerodynamic force simulator is effective and economic, and the design of helium blower and its components is reasonable and reliable.

Multi-sensor data fusion is a feasible technique to achieve accurate and robust results in fault diagnosis of rotating machinery under complex conditions. However, the problem of information losses is always ignored during the fusion process. To solve above problem, an ensemble convolutional neural network model is proposed for bearing fault diagnosis. The framework of the proposed model contains three convolutional neural network branches: one multi-channel fusion convolutional neural network branch and two 1-D convolutional neural network branches. The former branch extracts the coupling features based on multi-sensor data and the latter two branches extract the inherent features based on single-sensor data, which can collect comprehensive fault information and reduce information losses. Furthermore, the support vector machine ensemble strategy is employed to fuse the results of multiple branches, which can improve the generalization and robustness of the proposed model. The experiments show that the proposed can obtain more effective and robust results than other methods.

The DC and 50 Hz AC breakdown voltage of helium between two parallel copper electrodes were measured to obtain the base data for insulation design of the electrical equipment working in the high pressure and high temperature helium gas in High Temperature Reactor (HTR). The measurements were performed in the distance of 0.25–0.53 mm, the temperature of 15–180 °C, and the pressure of 0.05–7 MPa. It has been proved that, for both DC and 50 Hz AC, the breakdown voltage increases with the increasing of the pressure, with the increasing of the distance, and with the decreasing of the temperature. The DC breakdown voltage is about 1.4 times of the value of 50 Hz AC or, in other words, is similar to the peak value of 50 Hz AC. The Paschen law, describing the relationship between the breakdown voltage of helium ( U b ) and the product of gas density and electrode distance ( ρd ), was presented for DC and 50 Hz AC respectively, based on the experimental data at high pressure and high temperature. Good linearity is shown in the data. Furthermore, the linear fitting equation was also presented. The slope of Paschen curve about U b - ρd at the range of large ρd is influenced by the collision cross section and the ionization energy of helium atom, and not influenced by the temperature, for DC. Both for DC and 50 Hz AC, the slope of Paschen curve about U b - ρd is about 6 × 10 5 times of the slope of Paschen curve about U b - Pd , where Pd is the product of gas pressure and electrode distance. Both for DC and 50 Hz AC, the breakdown voltage of helium is less than one fifth of the value of air, at 0.1 MPa and room temperature. However, the breakdown voltage of helium at 180 °C and 7 MPa is larger than the value of air at 0.1 MPa and room temperature.

Magnetic bearings are widely applied in High Temperature Gas-cooled Reactor (HTGR) and auxiliary bearings are important backup and safety components in AMB systems. The performance of auxiliary bearings significantly affects the reliability, safety, and serviceability of the AMB system, the rotating equipment, and the whole reactor. Research on the dynamic behavior during the touchdown process is crucial for analyzing the severity of the touchdown. In this paper, a data-based dynamic analysis method of the touchdown process is proposed. The dynamic model of the touchdown process is firstly established. In this model, some specific mechanical parameters are regarded as functions of deformation of auxiliary bearing and velocity of rotor firstly; furthermore, a machine learning method is utilized to model these function relationships. Based on the dynamic model and the Kalman filtering technique, the proposed method can offer estimation of the rotor motion state from noisy observations. In addition, the estimation precision is significantly improved compared with the method without learning. The proposed method is validated by the experimental data from touchdown experiments.

As important sources in fault diagnosis of rotary machinery, vibration signals are usually processed in the time or frequency domain as features to distinguish different classes of faults. However, these kinds of processing methods always ignore the corresponding relations among multiple signals, resulting in information loss. In this paper, a new fault description strategy named vibration image is proposed, based on which three new kinds of features are extracted, containing coupling information between different channels of vibration signals. Additionally, a new feature fusion method called two-layer AdaBoost is designed to train the fault recognition model, which avoids overfitting when the dataset is not large enough. Features based on vibration images combined with two-layer AdaBoost are adopted to diagnose faults of rotary machinery. Taking an active magnetic bearing-rotor system as the experimental platform, a dataset with four classes of faults is collected and our algorithm achieves good performance. Meanwhile, features based on vibration images and two-layer AdaBoost are both proved to be efficient separately.

Fault diagnosis based on vibration signals in active magnetic bearing-rotor systems is an important research topic. However, it is difficult to obtain discriminative features to represent faults due to the nonlinear and non-stationary characteristics of the vibration signals and diverse sources of failures. Hence, this paper proposes a novel end-to-end learning mechanism of multi-sensor data fusion to learn fault representation based on the structural characteristics of active magnetic bearings. Taking the five displacement sensors of active magnetic bearing as signal sources, generalized shaft orbits are constructed and converted into discrete 2D images. Based these 2D images, a multi-branch convolutional neural network is designed to achieve high discriminative features and fault types. The experiments are performed on the rig supported by active magnetic bearings, and the effectiveness of the proposed algorithm is verified, proving it suitability in cases with changing rotating speeds and sample lengths.

The Active Magnetic Bearing (AMB) is used in the High Temperature Reactor- Pebble-bed Modules (HTR-PM) which is being constructed in Shandong province, China. The auxiliary bearing is one of the most important components guaranteeing the reliability of the AMB. A novel rolling-sliding integrated auxiliary bearing is studied through the drop experiment. For comparison, a traditional rolling auxiliary bearing in the same AMB is also studied experimentally. It was found that the rotor center orbit with the novel auxiliary bearing is much simpler and more focused. The impact force with the novel auxiliary bearing during the drop is much less. The drop process with the novel auxiliary bearing is more stable. All these phenomena are due to the fact that the contact point of the rotor with the novel auxiliary bearing is easy to stay at the lowest position of the circular contact surface during drop process.

Active magnetic bearing technology is used more and more for its high performance, such as high speed and frictionless operation. But the rotor vibrates sometimes during operation due to the existence of residual unbalanced mass, which may affect the security of the whole system. In order to determine the distribution of residual unbalanced mass, this paper proposes a method based on frequency response, control current analysis, and image data processing. The theoretical and calculated results show the validity of the method.