Showing papers on "Bearing (mechanical) published in 2022"
TL;DR: Successful fault diagnosis of rolling element bearings under complicated operating conditions, including early bearing fault signals in run-to-failure test datasets, signals with impulsive noise and planet bearing signals, demonstrates that the proposed FIVMD is a superior approach in extracting weak bearing repetitive transients.
108 citations
TL;DR: In this article, an arc-shaped piezoelectric sheet between the outer race of rolling bearing and bearing pedestal was installed to scavenge rotational energy from rotating machines.
102 citations
TL;DR: Wang et al. as discussed by the authors proposed a fault diagnosis method for bearing of flywheel energy storage system based on parameter optimization Variational Mode Decomposition (VMD) energy entropy, which can effectively extract the bearing fault characteristics and realize accurate fault diagnosis, and the recognition rate reached 97.5%, which was better than the comparison method.
66 citations
TL;DR: In this paper, the static and dynamic lubrication parameters of the floating ring bearing (FRB) were investigated considering the effects of various coupling factors such as clearance ratio, vertical load and rotating speed.
62 citations
TL;DR: The data of bearing inner ring, outer ring and compound faults prove that the method can be applied to bearing fault diagnosis and the proposed method has greater advantages in decomposing noisy signals.
38 citations
TL;DR: A nonlinear gear-shaft-bearing-housing vibration model with fourteen degree of freedom is presented to investigate the vibration responses under the dynamic gear meshing force and progressively changed radial clearances at first, and indicator based on modulation signal bispectrum-sideband estimator (MSB-SE) was proposed.
32 citations
27 citations
TL;DR: In this paper, a stochastic degradation model is established, which integrates the characteristics of multistage and multi-variability of degradation trend, and the statistical process control (SPC) is applied to stage division for the first time, which divides degradation stages and adaptively switches degradation models.
21 citations
TL;DR: In this paper, a self-powered fault diagnosis of rolling bearing (SP-FDRB) is conducted based on the triboelectric effect, where flexible interdigital electrodes are glued to the outer ring of a rolling bearing to form a rolling-type free standing mode nanogenerator (RF-TENG).
20 citations
TL;DR: In this paper, Hjorth's parameters are applied to vibration signals for fault detection in ball bearings and two open-access datasets are used: the NASA bearing dataset of the University of Cincinnati and the Polytechnic of Turin rolling bearing dataset.
20 citations
TL;DR: In this paper, a non-contact triboelectric bearing sensor (NC-TEBS) was proposed for monitoring the speed and skidding of rolling bearing, which is composed of PTFE ring, grid electrode, and sweeping charge supplement device.
TL;DR: In this article, a kinematic-Hertzian contact thermal-elasto-hydrodynamic (KH-TEHD) model with a 5-layer loop structure is proposed.
TL;DR: In this article, four measurement approaches were tested on the same rig for bearing run-to-failure experiments, and their signals were analysed individually and compared, and it was found that IAS and radial load (a proxy for displacement) required less processing to provide a reliable assessment of bearing fault severity, acceleration required sophisticated techniques to extract spall-size estimates, whereas AE could not track fault evolution accurately.
TL;DR: In this paper, the transient acting pressures at the thermal double fluid-film bearing are computed by employing the turbulent lubrication theory, then a numerical model for the whole rotor-bearing system based on the transfer matrix method is developed to investigate its nonlinear vibration behavior.
TL;DR: In this paper, the performance of a thrust bearing equipped with hydrostatic lift pockets under different lubrication modes was investigated, and the results confirmed that the load-carrying capacity of the flat land bearing is poor and the introduction of hydrostatic lubrication improves its performance.
TL;DR: In this paper, a coupled model of the rotor, bearing, and bearing frame was developed, and an experiment was also conducted to measure the displacement of the journal center, and the results showed good consistency with the numerical simulation.
TL;DR: In this article, the authors used two different greases bearing experiments to gain an understanding of the mechanism of wear initiation and found that starvation seems to be a major contribution to wear appearing in the investigated operating conditions (2°-45° osc. angle, 0,2-5 Hz frequency).
TL;DR: The resulting reduction in the relative vibrations of the turbine’s bearing, which is less than the alarm limit, cements the role of ANN process model to be used as an operational excellence tool resulting in vibration reduction of high-speed rotating equipment.
Abstract: The vibrations of bearings holding the high-speed shaft of a steam turbine are critically controlled for the safe and reliable power generation at the power plants. In this paper, two artificial intelligence (AI) process models, i.e., artificial neural network (ANN) and support vector machine (SVM) based relative vibration modeling of a steam turbine shaft bearing of a 660 MW supercritical steam turbine system is presented. After extensive data processing and machine learning based visualization tests performed on the raw operational data, ANN and SVM models are trained, validated and compared by external validation tests. ANN has outperformed SVM in terms of better prediction capability and is, therefore, deployed for simulating the constructed operating scenarios. ANN process model is tested for the complete load range of power plant, i.e., from 353 MW to 662 MW and 4.07% reduction in the relative vibration of the bearing is predicted by the network. Further, various vibration reduction operating strategies are developed and tested on the validated and robust ANN process model. A selected operating strategy which has predicted a promising reduction in the relative vibration of bearing is selected. In order to confirm the effectiveness of the prediction of the ANN process model, the selected operating strategy is implemented on the actual operation of the power plant. The resulting reduction in the relative vibrations of the turbine’s bearing, which is less than the alarm limit, are confirmed. This cements the role of ANN process model to be used as an operational excellence tool resulting in vibration reduction of high-speed rotating equipment.
TL;DR: In this article, an improved cost function ridge detection (ICFRD) method is proposed to detect bearing faults under variable speed conditions, which integrates an adaptive search bandwidth determination technique that varies the search region with signal signatures, as well as a novel cost function that comprehensively considers the trade-off between ridge amplitude and smoothness.
Abstract: Ridge extraction is an effective tacholess order tracking technique for the fault detection of bearings under time-varying speed conditions. Cost function ridge detection (CFRD) is the most widely used ridge detection method. However, improper bandwidth selection and unreasonable cost function construction significantly restrict the performance of the CFRD. To address the two shortcomings of the CFRD, an improved CFRD (ICFRD) method is firstly proposed in this paper. The ICFRD integrates an adaptive search bandwidth determination technique that varies the search region with signal signatures, as well as a novel cost function that comprehensively considers the trade-off between ridge amplitude and smoothness. An iterative characteristic ridge extraction (ICRE) strategy is then presented based on the ICFRD to extract multiple characteristic ridges in a time-frequency plane automatically. The average frequency ratios between the extracted characteristic ridges are calculated to estimate bearing fault characteristic orders and therefore detect bearing faults. The performance of the proposed method was tested using simulated signals and experimental vibration signals collected from a machinery test rig. Results show that the ICRE outperforms the conventional CFRD in terms of detecting bearing faults under variable speed conditions. The average relative errors between the extracted instantaneous frequencies and the theoretical ones of the ICRE are 0.85%, 2.11% and 0.63% for inner race fault, outer race fault, and healthy bearing vibration signal, respectively. These values are much smaller than the results of using the CFRD.
TL;DR: In this paper, a symmetrical rigid bearing-rotor system running in flexible bearing supports was modeled and the influence of the resonance characteristics and rotor eccentric excitation on the fault characteristic frequencies was analyzed via envelope analysis performing on the dynamic response from the numerical simulation at different speeds.
TL;DR: In this paper, a novel aerostatic bearing with back-flow channels is presented, which is designed to connect the feed pocket and low-pressure region of the bearing clearance directly.
TL;DR: This study proposes two novel methods for solution of the responses caused by the bearing waviness excitation in frequency domain, and compares the result with a previously developed, time domain based numerical simulation.
TL;DR: In this article, a test rig for Particle Image Velocimetry (PIV) measurements on the lubricant inside a tapered roller bearing is presented, and the results show that as the speed of rotation increases, bubbles due to aeration phenomena tend to appear and modify the behavior of the bearing.
TL;DR: In this paper, the dynamic responses of a rigid rotor supported with two HPTPBs were investigated. But the rotor was successfully accelerated to 40 krpm and the authors did not consider the influence of the separate gas supply on the dynamic response.
TL;DR: The main contributions of this work are related to the development of a harvester model excited by its tip considering n-vibrational modes, thus allowing the determination of how many vibrational modes should be used in the computational analysis in order to achieve a balance between precision and simulation time.
TL;DR: In this article, the effects of center coefficient and weighting coefficient on adaptive chirp mode decomposition (ACMD) performances are investigated via fractional Gaussian noise numerical simulation experiments.
01 Jan 2022
TL;DR: A novel real-time rotating machinery damage monitoring system that detects, quantifies, and localizes damage in ball bearings in a fast and accurate way using one-dimensional convolutional neural networks (1D-CNNs).
Abstract: This paper presents a novel real-time rotating machinery damage monitoring system. The system detects, quantifies, and localizes damage in ball bearings in a fast and accurate way using one-dimensional convolutional neural networks (1D-CNNs). The proposed method has been validated with experimental work not only for single damage but also for multiple damage cases introduced onto ball bearings in laboratory environment. The two 1D-CNNs (one set for the interior bearing ring and another set for the exterior bearing ring) were trained and tested under the same conditions for torque and speed. It is observed that the proposed system showed excellent performance even with the severe additive noise. The proposed method can be implemented in practical use for online defect detection, monitoring, and condition assessment of ball bearings and other rotatory machine elements.
01 Jan 2022
TL;DR: In this article, the authors proposed two finite-time bearing-based control laws for acyclic leader-follower formations, where the leader moves with a bounded continuous reference velocity and each follower controls its position with regard to three agents in the formation.
Abstract: This letter proposes two finite-time bearing-based control laws for acyclic leader-follower formations. The leaders in formation move with a bounded continuous reference velocity and each follower controls its position with regard to three agents in the formation. The first control law uses only bearing vectors, and finite-time convergence is achieved by properly selecting two state-dependent control gains. The second control law requires both bearing vectors and communications between agents. Each agent simultaneously localizes and follows a virtual target. Finite-time convergence of the desired formation under both control laws is proved by mathematical induction and supported by numerical simulations.