The occurrence of the motor shaft voltage and bearing current caused by the inverter will aggravate bearing damage and lead to the premature failure of bearings. Many types of equipment are being shut down due to bearing currents, such as filters, insulated bearings and grounding brushes. Traditional suppression measures cannot eliminate the bearing current and the bearing damage mechanism under the bearing current is not clear. In this paper, the damage caused by the bearing current to bearings is analyzed in detail. The influences of different working conditions on the bearing current and the damage caused are discussed. The source of bearing currents is introduced and the bearing current model under different working conditions is reviewed. An outlook for future studies is proposed, based on the current research status and challenges.

Misalignment is common in hydrodynamic journal bearings and the causes of it can be diversified, making the lubrication performance exhibits stochasticity. Lubricant viscosity often heavily depends on temperature, which may vary during service and result in unexpected deviations. This article analyzes the stochastic lubrications of a cylindrical hydrodynamic journal bearing with misalignment under uncertainties. The stochastic Reynolds equation governing the misaligned journal bearing is discretized by the polynomial chaos expansion (PCE), an efficient uncertainty tracking tool, and then solved by the finite difference method to obtain sampled lubrication. The crude Monte Carlo simulation is used to verify the performance of the PCE frame. Various critical lubrication performance parameters are studied comprehensively by the ensemble mean, standard deviation, probability density function, and cumulative distribution function. Insightful inspections are provided on the stochastic results, and it is found that the misalignment and different stochastic parameters may cause significant effects on the lubrication performance. The new findings in the present study will guide the robust design and analysis of general hydrodynamic journal bearings.

Motor bearings are prone to different degrees of performance degradation, fatigue damage and failure undergoing complex and harsh environments. Vibration signal analysis is a mature method for diagnosing motor bearing faults, while it is not applicable for installing additional vibration sensors on many occasions. Practically, the fault of motor bearings changes the air gap flux between the rotor and stator, which leads to harmonic fluctuations in the stator current. The current signals can be used to diagnose the motor bearing faults without additional sensors. Inevitably the harmonics caused by the motor bearing faults will be coupled with the original signals. This paper combines bi-spectrum and Convolution Neural Network (CNN) to analyze the current signals of motor bearing faults. The CNN diagnosis model is trained based on the local bi-spectrum of current, and the CNN parameters are optimized. Diagnose and analyze motor bearing faults with different fault implantation methods, working conditions, fault degrees and fault locations. The diagnostic accuracy reaches more than 80%.

To study the nonlinear dynamic behavior and system stability of a rubbing overhung rotor with viscoelastic and memory-effect damping and random uncertain parameters, this paper introduces a fractional-order modeling and stochastic dynamic analysis method for the nonlinear overhung rotor system with frictional impact faults. Firstly, the dynamic equations of the overhung rotor considering friction effect and fractional damping effect are established based on the transfer matrix method and fractional order derivative. Then, the time-domain response of the fractional-order dynamic equations is solved by combining the Runge–Kutta method and the continuous fractional expansion, and the steady-state response characteristics of different fractional damping are analyzed in the deterministic case. Finally, to analyze the response of the system under the effect of stochastic parameters, the sparse grid-based PCE metamodel of the system response is developed. Statistical moments, probability distributions, and sensitivity indices of the response of stochastic systems are revealed. The results of this paper provide a theoretical basis for efficient and accurate prediction of the stochastic response of nonlinear rubbing overhung rotor systems.

Rotating systems equipped with oil-film bearings are critical and common in many industrial machines. There are various non-random uncertainties in such fluid-lubricated dynamic systems. It is important to quantify the effects of uncertainties without adequate statistical information on the dynamics of rotor-bearing systems. In this paper, a rotor system with oil-film bearings at both ends is investigated considering many interval uncertainties. The rotating system is modeled in a deterministic sense. The Chebyshev interval method is used to track the propagation of different uncertainties. Deviations in the steady state responses, time history, and shaft orbits are calculated and comparatively discussed. Influence patterns of different interval parameters and dispersions in various dynamics are presented in detail. It is found that there can be global and local impacts as well as cumulative effects caused by multi-source uncertainties. The findings of the present study could be helpful for a more insightful dynamic analysis of rotor-bearing systems as well as their optimal design and maintenance.

This paper provides a comprehensive evaluation of crucial performances of hydrodynamic journal bearings subject to inherent uncertainties from a non-probabilistic perspective. Uncertainties can arise for various reasons such as the evolving service environment, manufacturing errors and assembling imperfections. An efficient non-intrusive frame named the Chebyshev interval finite difference method is proposed to solve the interval Reynolds equations, which allows accurate predictions of variabilities of performance parameters. The uncertain geometrical properties, lubricant viscosity and external load are considered and their effects on the static and dynamic characteristics of the uncertain journal bearing are intensively discussed in different situations. New insightful results are found in various case studies and numerical validations of the proposed method are carried out. Moreover, the sensitivity analysis was carried out and the application of the proposed frame to a Jeffcott rotor was demonstrated. The interval uncertainties are found to have severe effects on the hydrodynamic journal bearings, causing potential critical influences in the corresponding rotating systems. Results and methods in the current study provide references to robust assessment and design of journal bearings, and further guide the investigations on rotor-journal bearing systems.