The abnormal wear of a rolling element bearing caused by early failures, such as pitting and spalling, will deteriorate the running state and reduce the life. This paper demonstrates the importance of oil debris monitoring and its effective feature extraction for bearing health assessment. In this paper, a rolling bearing-rotor test rig with forced lubrication is set up and the nonferrous contaminants with higher hardness were introduced artificially to accelerate the occurrence of pitting and spalling. The early failure and abnormal wear of rolling bearings cannot be effectively detected only through the vibration signal; the temperature and oil debris monitoring data are also collected synchronously. Two features regarding the ferrous particle size distribution are extracted and fused with vibration based-features to form a feature set. The sensitive features are extracted from the features set using the Neighborhood Component Analysis method to avoid feature redundancy. Finally, the importance of the oil debris based-features for the diagnosis of abnormal bearing wear is analyzed with different machine learning algorithms. Taking SVM classifier as an example, the experiment results show that the introduction of oil debris based-features increases the diagnostic accuracy by 15.7%.

The output flow pulsation characteristics of the hydraulic pump due to the structural characteristics may cause pump source fluid pressure pulsation and even cause the equipment to vibrate, which will affect the life and working reliability of the equipment. Scholars have done a lot of theoretical and simulation analysis on the characteristics of fluid flow and pressure pulsation caused by the specific structure and structure of the plunger pump, but there are few comparisons and analyses of the simplified model of the plunger pump and the pressure pulsation characteristics with experiments. In this paper, AMESim software is utilized to establish a simplified model of one seven-plunger hydraulic pump, and simulate and analyze the pump source fluid pressure pulsation characteristics of different system load pressures at a constant speed. An experimental platform for testing pump fluid pressure pulsation was designed and built, and the actual measurement and simulation results of pump fluid pressure pulsation were compared and analyzed. The results show that the system simulation data is in good agreement with the measured data, which verifies the correctness of the simplified model of the plunger pump. At the same time, it is found that the fluid pressure pulsation of the pump source exhibits broadband and multi-harmonic characteristics. At a constant speed, as the load pressure of the hydraulic system increases, the pump source fluid pressure pulsation amplitude increases, the pressure pulsation rate decreases, and the impact on the fundamental frequency amplitude is the most significant. The research results can provide a theoretical basis for suppressing the pressure pulsation of the pump source fluid and reducing the vibration response of a hydraulic pipeline under the action of the pulsating harmonic excitation.

In this paper, a pedestal looseness fault model of a rotor‐bearing‐seal system is established. Under two working conditions of the same direction eccentricity (Working Condition 1) and reverse eccentricity (Working Condition 2), rotor orbits, vibration waveforms, spectrum cascade, and Poincaré maps are used to study the dynamic characteristics of the system when the sliding bearing support is loosened. The influence of speed, the unbalance of two discs, the looseness clearance, and the mass of bearing support on system characteristics are analyzed. The study found that Working Condition 2 is more likely to cause looseness of the bearing support. Moreover, for the rotor system in this paper, the pedestal looseness fault is more likely to occur near the second‐order critical speed. Through analysis of the spectrum, it is found that the spectrum of the looseness fault will show multiple frequencies or continuous spectra, and the rotor orbits will appear “cylindrical.”

The driving mechanism of an axisymmetric vectoring exhaust nozzle (AVEN) is a 3-degrees-of-freedom (3-DOF) parallel manipulator (PM) with a centering device. According to the characteristics of the vectoring nozzle’s movement mechanism, the 3-DOF kinematics model of the 3SPS + 3PRS PM is established. The forward and inverse positional posture models are built based on the mechanism architecture of PM and the closed-loop vector method. The Jacobian matrix and Hessian matrix are derived using screw theory, and velocity and acceleration models are established. Based on the principle of virtual work and combined with the kinematics model of PM, a stiffness model was established to analyze stiffness performance. Finally, the effectiveness of the kinematics model of the PM was verified by a simulation, and the variation of stiffness performances with the positional posture of the PM was quantitatively analyzed. The results show that the translational stiffness decreases with forward axial translation of the moving platform and the rotational stiffness is mainly determined by the posture parameters. This study is expected to offer ideas for the kinematic modeling of 3-DOF PM and provide references for application and optimizing of the AVEN’s driving mechanism.

Rotor systems are important parts of rotating machinery. Real-time health monitoring of rotor systems is essential for safe operation. Data-driven modeling based on sensor data is currently the focus of health monitoring, which solves the problem that traditional physical modeling cannot be applied to complex mechanical equipment. Parameters uncertainty of data-driven modeling is inevitable, and analyzing and utilizing this uncertainty is critical to improving the adaptability of data-driven monitoring methods. This article focuses on the uncertainty quantification of fault features under the uncertainty of data-driven model parameters. Combined with Monte Carlo simulation and Latin hypercube sampling, the influence mechanism of system performance degradation on the quantification of feature uncertainty is analyzed. Finally, a Jeffcott rotor test rig with a rub-impact device was built, and the feature uncertainty caused by the nonlinear autoregressive with exogenous input (NARX) model parameter uncertainty was explained. The experimental results show that the reliability of the proposed fault features based on the uncertainty is significantly better than the traditional signal-based features, which significantly improves the adaptability of the data-driven method in the health assessment of the rotor system.

This paper analyzed the effects of boundary conditions on the stress distribution of hydraulic support with the static finite element (FE) model. Five loading conditions are considered in this study, including: 1) canopy torsional loading, 2) canopy eccentric loading, 3) base torsional loading, 4) base diagonal loading, and 5) base symmetrical loading. In order to verify the simulation results obtained from the FE model, the corresponding experiments have also been performed. Based on the comparison between simulation and experimental results, the effects of pin-joint simplified methods and boundary conditions are evaluated in terms of accuracy and efficiency. Moreover, the new elastic-support boundary is also proposed to improve the simulation accuracy under conditions 2, 3, 4, and 5. The results show that bonded contact between the pin and shaft hole has high efficiency and accuracy compared with the frictional contact. The frictional contact boundary is reasonable under condition 1. However, the elastic-support boundary is suggested to be adopted to improve calculation accuracy for the stress distribution of the constraint components (canopy or base) under conditions 2, 3, 4, and 5.