Vmd citation12/30/2023 ![]() In order to extract the transient features from the vibration signals, different signal processing techniques have been developed in the area of rotary machine fault diagnosis, such as the wavelet analysis, empirical mode decomposition (EMD), time-frequency analysis (TFA), sparse decomposition, manifold learning, spectral kurtosis (SK), cyclic spectral analysis, and envelope analysis. ![]() It is possible to obtain vital characteristic information from the vibration signals through the use of signal processing techniques. Therefore, the vibration-based methods have received intensive study during the past decades. Vibration signals collected from bearings carry the rich information on machine health conditions. When the surface of one or more of these components develops a localized fault, the impacts generated excite the resonant frequencies of the bearing and adjacent components and induce a modulating phenomenon, which is the basis of bearing fault diagnosis. Rolling element bearings usually consist of an inner race, an outer race, several rollers, and a cage. Currently, the fault diagnosis of rolling bearing early weak fault is not only a hot area but also a difficult area. Besides, there exist the severe signal attenuation phenomenon between the fault source and the sensor collecting the fault signal if the sensor is placed far from the fault-related location. However, the rolling element bearing early incipient defect feature is very weak for reasons of being buried in the strong background noises and the interference of the rotating frequency. Hence, it is necessary to detect bearing faults at an early stage. Rolling element bearings are widely used in rotating machinery to support rotating shafts, and the major cause of machinery breakdown is the bearing failure. The simulation studies and experimental verifications confirm that the proposed method is effective for identifying the multiresonance bands and enhancing rolling element bearing fault diagnosis by comparing with Hilbert transform, EMD-based demodulation, and fast Kurtogram analysis. In addition, an idea of combining the multiresonance bands is constructed to further enhance the fault-related impulses. Meanwhile, the physical meaning of MTEO is also discovered in this paper. Third, Multiresolution Teager Energy Operator (MTEO) is employed to extract the fault-related impulses hidden in the vibration signal and avoid the negative value phenomenon of Teager Energy Operator (TEO). Second, the one or more modes with the information of fault-related impulses are selected through the kurtosis index. First, the VMD is introduced to decompose the raw vibration signal. Specifically, the method is conducted by the following three steps. As a consequence, the novel method for enhancing rolling element bearing fault diagnosis is proposed. ![]() In our paper, the adaptive detection of the multiresonance bands in vibration signal is firstly considered based on variational mode decomposition (VMD). Visualization of Energy Conversion Processes in a Light Harvesting Organelle at Atomic Detail, 1st Place Winner, SC'14 Viz.Vibration signals of the defect rolling element bearings are usually immersed in strong background noise, which make it difficult to detect the incipient bearing defect. ShowcaseĬhromatophore VR demo (VMD + Unreal Game Engine) shown in NVIDIA booth at SC'16Ĭhemical Visualization of Human Pathogens: the Retroviral Capsids, Finalist, SC'15 Viz. VMD test builds for MacOS X 10.15 "Catalina" (April 24, 2020) Past announcements GalleryĪn Accessible Visual Narrative for the Primary Energy Source of Life from the Fulldome Show Birth of Planet Earth, 1st Place Winner, SC'19 Viz. Scalable molecular dynamics on CPU and GPU architectures with NAMD, JCP, 2020 The Coronavirus Unveiled, VMD visualizations of SARS-CoV-2, NYT, 2020 NAMD and VMD part of the team winning the ACM COVID-19 Gordon Bell Prize for 2020 Multiscale modeling and cinematic visualization of photosynthetic energy conversion processes from electronic to cell scales, J. NIH Director's blog highlights neuroscience adaptation of VMDĪI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics, IJHPCA, 2021 Intelligent Resolution: Integrating Cryo-EM with AI-Driven Multi-Resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action, IJHPCA, 2022 #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol, IJHPCA, 2022 The Coronavirus in a Tiny Drop, VMD visualizations of aerosolized SARS-CoV-2, NYT, 2021 Human Learning for Molecular Simulations: The Collective Variables Dashboard in VMD, JCTC, 2022ĪNARI: A 3D Rendering API Standard, CiSE, 2022
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