Rolling element bearings are widely employed in almost every rotating machine. The health status of bearings plays an important role in the reliability of rotating machines. This paper deals with the principle and application of an effective multi-sensor data fusion fault diagnosis approach for rolling element bearings. In particular, two single-axis accelerometers are employed to improve classification accuracy. By applying the improved detrended fluctuation analysis (IDFA), the corresponding fluctuations detrended by the local fit of vibration signals are evaluated. Then the polynomial fitting coefficients of the fluctuation function are selected as the fault features. A multi-sensor data fusion classification method based on linear discriminant analysis (LDA) is presented in the feature classification process. The faults that occurred in the inner race, cage, and outer race are considered in the paper. The experimental results show that the classification accuracy of the proposed diagnosis method can reach 100%.

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The maglev vibration isolation system exhibits excellent micro-vibration isolation performance (0.01 Hz to 100 Hz band) in the space environment. However, a collision between the base and the floating platform may occur in an ultra-low frequency range (≤0.01 Hz). To avoid collision, the relative position and attitude between the base and the floating platform needs to be accurately tracked and controlled. In this study, a novel measurement method with four groups of two-dimensional position-sensitive detectors equipped with four laser light sources was proposed. A high-precision relative position and attitude measurement model was established based on the geometric relationship of space coordinates. A proportional-differential (PD) fixed-point control algorithm was adopted to realize tracking control. The control performance of the system was evaluated through simulation. Experiments were also carried out to verify the stability of the system and the precision of the control algorithm. A maglev vibration isolation system prototype was constructed and a test system was established. The proposed relative position and attitude measurement model was verified and the six degrees of freedom relative position and attitude response of the system was tested. Based on the measurement model, the tracking control of the system was proven to have high precision.

The environment in space provides favorable conditions for space missions. However, low frequency vibration poses a great challenge to high sensitivity equipment, resulting in performance degradation of sensitive systems. Due to the ever-increasing requirements to protect sensitive payloads, there is a pressing need for micro-vibration suppression. This paper deals with the modeling and control of a maglev vibration isolation system. A high-precision nonlinear dynamic model with six degrees of freedom was derived, which contains the mathematical model of Lorentz actuators and umbilical cables. Regarding the system performance, a double closed-loop control strategy was proposed, and a sliding mode control algorithm was adopted to improve the vibration isolation performance. A simulation program of the system was developed in a MATLAB environment. A vibration isolation performance in the frequency range of 0.01–100 Hz and a tracking performance below 0.01 Hz were obtained. In order to verify the nonlinear dynamic model and the isolation performance, a principle prototype of the maglev isolation system equipped with accelerometers and position sensors was developed for the experiments. By comparing the simulation results and the experiment results, the nonlinear dynamic model of the maglev vibration isolation system was verified and the control strategy of the system was proved to be highly effective.

Space vector pulse width modulation (SVPWM) is used in motor control because of its high dynamic response and flexible control. Maximum torque per ampere (MTPA) strategy has become the most commonly used choice for internal permanent magnet synchronous motor (IPMSM). However, complicated coordinate transformations are required in the usual SVPWM algorithm and the performance of the MTPA algorithm can be affected by the parameters variation. This paper proposes a voltage vector directional control (VVDC) method based on the MTPA strategy. The inverse Park transformation is avoided in the proposed control scheme. And the proposed algorithm not only is briefer and clearer in the sector judgment process but also has lesser calculation during the duty cycle generation. In order to realize the MTPA strategy, two voltage feedback closed loops are designed to compensate for the d-axis and q-axis currents. A simple and effective multi-parameter identification method is also presented to solve the parameters variation problem. The effectiveness of the proposed method is verified by simulation and experiments.INDEX TERMS IPMSM, MTPA, motor parameter identification, voltage vector control.