The Effect of Gravity and Rotor Configuration on Dry Friction Whip and Safe Rotor/Stator Clearance

2019 ◽  
Author(s):  
Rajiv Kumar Vashisht
Keyword(s):  
Mathematical Model ◽  
Fault Diagnosis ◽  
Dry Friction ◽  
Rotating Machinery ◽  
Degree Of Freedom ◽  
System Response ◽  
Rich Variety ◽  
Wide Range ◽  
Clearance Level ◽  
High Degree

Abstract A mathematical model is developed for a real rotor/stator system with high degrees-of-freedoms, multiple disks, flexible bearing supports and couplings. The safe clearance level for coasting up of the rotor is calculated for a general high degree-of-freedom rotor/stator system. The harmful phenomena of dry friction whip, which is generally observable for simple 2 degree-of-freedom Jeffcott rotors in the absence of gravity only, can be proved to exist (in real rotor/stator systems) even in the presence of gravity for a wide range of clearance levels. In case of Jeffcott rotors, by fixing the clearance and increasing the rotor spin frequency, the response of the system follows the pattern: No rub - Forward Annular Rub (FAR) - Partial Forward Whirl (PFW) - Partial Backward Whirl (PBW) - dry whip (WHIP). In case of a real rotor/stator system, at certain frequencies, the system directly jumps to dry whip. The simulated results show a rich variety of system dynamics including FAR, PFW and WHIP in case of vertical rotors where the effect of gravity is neglected. For horizontal rotors, under the effect of gravity, the system response contains multi-harmonics, chaotic responses and multi-period vibrations. Based on these responses, a robust fault diagnosis strategy can be designed to identify the rubbing action in rotating machinery.

scholarly journals Snake Robot with Driving Assistant Mechanism

2020 ◽  
Vol 10 (21) ◽  
pp. 7478
Author(s):  
Junseong Bae ◽  
Myeongjin Kim ◽  
Bongsub Song ◽  
Maolin Jin ◽  
Dongwon Yun
Keyword(s):  
Dynamic Simulation ◽  
Degree Of Freedom ◽  
Rough Terrain ◽  
Snake Robot ◽  
Wide Range ◽  
Locomotion Speed ◽  
High Degree ◽  
Snake Robots

Snake robots are composed of multiple links and joints and have a high degree of freedom. They can perform various motions and can overcome various terrains. Snake robots need additional driving algorithms and sensors that acquire terrain data in order to overcome rough terrains such as grasslands and slopes. In this study, we propose a driving assistant mechanism (DAM), which assists locomotion without additional driving algorithms and sensors. In this paper, we confirmed that the DAM prevents a roll down on a slope and increases the locomotion speed through dynamic simulation and experiments. It was possible to overcome grasslands and a 27 degrees slope without using additional driving controllers. In conclusion, we expect that a snake robot can conduct a wide range of missions well, such as exploring disaster sites and rough terrain, by using the proposed mechanism.

Design of a Lightweight Hyper-Redundant Deployable Binary Manipulator

2004 ◽  
Vol 126 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Vivek A. Sujan ◽  
Steven Dubowsky
Keyword(s):  
Degree Of Freedom ◽  
Robotic Systems ◽  
Muscle Type ◽  
Complex Tasks ◽  
Power Resources ◽  
Future Space ◽  
Wide Range ◽  
Binary Actuators ◽  
High Degree

This paper presents the design of a new lightweight, hyper-redundant, deployable Binary Robotic Articulated Intelligent Device (BRAID), for space robotic systems. The BRAID is intended to meet the challenges of future space robotic systems that need to perform more complex tasks than are currently feasible. It is lightweight, has a high degree of freedom, and has a large workspace. The device is based on embedded muscle type binary actuators and flexure linkages. Such a system may be used for a wide range of tasks, and requires minimal control computation and power resources.

Understanding Conformational Entropy in Small Molecules

2020 ◽  
Author(s):  
Lucian Chan ◽  
Garrett Morris ◽  
Geoffrey Hutchison
Keyword(s):  
Small Molecules ◽  
Degrees Of Freedom ◽  
Absolute Error ◽  
Low Energy ◽  
Standard Entropy ◽  
Free Energies ◽  
Conformational Entropy ◽  
Wide Range ◽  
High Degree ◽  
Empirical Corrections

The calculation of the entropy of flexible molecules can be challenging, since the number of possible conformers grows exponentially with molecule size and many low-energy conformers may be thermally accessible. Different methods have been proposed to approximate the contribution of conformational entropy to the molecular standard entropy, including performing thermochemistry calculations with all possible stable conformations, and developing empirical corrections from experimental data. We have performed conformer sampling on over 120,000 small molecules generating some 12 million conformers, to develop models to predict conformational entropy across a wide range of molecules. Using insight into the nature of conformational disorder, our cross-validated physically-motivated statistical model can outperform common machine learning and deep learning methods, with a mean absolute error ≈4.8 J/mol•K, or under 0.4 kcal/mol at 300 K. Beyond predicting molecular entropies and free energies, the model implies a high degree of correlation between torsions in most molecules, often as- sumed to be independent. While individual dihedral rotations may have low energetic barriers, the shape and chemical functionality of most molecules necessarily correlate their torsional degrees of freedom, and hence restrict the number of low-energy conformations immensely. Our simple models capture these correlations, and advance our understanding of small molecule conformational entropy.

Cerebellotrigeminal Dermal Dysplasia (Gómez-López-Hernández Syndrome)

2018 ◽  
Vol 16 (05) ◽  
pp. 362-368 ◽  
Author(s):  
Federica Sullo ◽  
Agata Polizzi ◽  
Stefano Catanzaro ◽  
Selene Mantegna ◽  
Francesco Lacarrubba ◽  
...  
Keyword(s):  
De Novo ◽  
Chromosomal Rearrangements ◽  
Number Of Patients ◽  
Wide Range ◽  
Visual Problems ◽  
Neurodevelopmental Abnormalities ◽  
Clinical Triad ◽  
High Degree ◽  
Motor Handicap

Cerebellotrigeminal dermal (CTD) dysplasia is a rare neurocutaneous disorder characterized by a triad of symptoms: bilateral parieto-occipital alopecia, facial anesthesia in the trigeminal area, and rhombencephalosynapsis (RES), confirmed by cranial magnetic resonance imaging. CTD dysplasia is also known as Gómez-López-Hernández syndrome. So far, only 35 cases have been described with varying symptomatology. The etiology remains unknown. Either spontaneous dominant mutations or de novo chromosomal rearrangements have been proposed as possible explanations. In addition to its clinical triad of RES, parietal alopecia, and trigeminal anesthesia, CTD dysplasia is associated with a wide range of phenotypic and neurodevelopmental abnormalities.Treatment is symptomatic and includes physical rehabilitation, special education, dental care, and ocular protection against self-induced corneal trauma that causes ulcers and, later, corneal opacification. The prognosis is correlated to the mental development, motor handicap, corneal–facial anesthesia, and visual problems. Follow-up on a large number of patients with CTD dysplasia has never been reported and experience is limited to few cases to date. High degree of suspicion in a child presenting with characteristic alopecia and RES has a great importance in diagnosis of this syndrome.

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

scholarly journals Controlling Teleportation-Based Locomotion in Virtual Reality with Hand Gestures: A Comparative Evaluation of Two-Handed and One-Handed Techniques

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 715
Author(s):  
Alexander Schäfer ◽  
Gerd Reis ◽  
Didier Stricker
Keyword(s):  
Virtual Reality ◽  
Virtual Worlds ◽  
User Preferences ◽  
Interaction Techniques ◽  
Hand Gestures ◽  
Wide Range ◽  
Effectiveness And Efficiency ◽  
High Degree ◽  
Additional Hardware ◽  
Comprehensive Study

Virtual Reality (VR) technology offers users the possibility to immerse and freely navigate through virtual worlds. An important component for achieving a high degree of immersion in VR is locomotion. Often discussed in the literature, a natural and effective way of controlling locomotion is still a general problem which needs to be solved. Recently, VR headset manufacturers have been integrating more sensors, allowing hand or eye tracking without any additional required equipment. This enables a wide range of application scenarios with natural freehand interaction techniques where no additional hardware is required. This paper focuses on techniques to control teleportation-based locomotion with hand gestures, where users are able to move around in VR using their hands only. With the help of a comprehensive study involving 21 participants, four different techniques are evaluated. The effectiveness and efficiency as well as user preferences of the presented techniques are determined. Two two-handed and two one-handed techniques are evaluated, revealing that it is possible to move comfortable and effectively through virtual worlds with a single hand only.

scholarly journals Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 919
Author(s):  
Wanlu Jiang ◽  
Chenyang Wang ◽  
Jiayun Zou ◽  
Shuqing Zhang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Generative Adversarial Networks ◽  
Extraction Ability ◽  
One Dimensional ◽  
Adversarial Networks ◽  
Diagnosis Model ◽  
The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

scholarly journals Analysis of Dynamic Response of a Two Degrees of Freedom (2-DOF) Ball Bearing Nonlinear Model

2021 ◽  
Vol 11 (2) ◽  
pp. 787
Author(s):  
Bartłomiej Ambrożkiewicz ◽  
Grzegorz Litak ◽  
Anthimos Georgiadis ◽  
Nicolas Meier ◽  
Alexander Gassner
Keyword(s):  
Mathematical Model ◽  
Degrees Of Freedom ◽  
Ball Bearing ◽  
Hertzian Contact ◽  
Two Degrees Of Freedom ◽  
Wide Range ◽  
Shape Errors ◽  
Nonlinear Features ◽  
Fourier Transform Phase ◽  
Hertzian Contact Theory

Often the input values used in mathematical models for rolling bearings are in a wide range, i.e., very small values of deformation and damping are confronted with big values of stiffness in the governing equations, which leads to miscalculations. This paper presents a two degrees of freedom (2-DOF) dimensionless mathematical model for ball bearings describing a procedure, which helps to scale the problem and reveal the relationships between dimensionless terms and their influence on the system’s response. The derived mathematical model considers nonlinear features as stiffness, damping, and radial internal clearance referring to the Hertzian contact theory. Further, important features are also taken into account including an external load, the eccentricity of the shaft-bearing system, and shape errors on the raceway investigating variable dynamics of the ball bearing. Analysis of obtained responses with Fast Fourier Transform, phase plots, orbit plots, and recurrences provide a rich source of information about the dynamics of the system and it helped to find the transition between the periodic and chaotic response and how it affects the topology of RPs and recurrence quantificators.

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