scholarly journals Evidence consistent with the multiple-bearings hypothesis from human virtual landmark-based navigation

2015 ◽  
Vol 6 ◽  
Author(s):  
Martha R. Forloines ◽  
Kent D. Bodily ◽  
Bradley R. Sturz
Keyword(s):  
Multiple Bearings

scholarly journals The Use of Landmarks by Clark's Nutcrackers: First Tests of a New Model

2001 ◽  
Vol 54 (3) ◽  
pp. 429-435 ◽  
Author(s):  
Alan C. Kamil ◽  
Aleida J. Goodyear ◽  
Ken Cheng
Keyword(s):  
Path Integration ◽  
Interesting Case ◽  
Olfactory Cues ◽  
Nucifraga Columbiana ◽  
Scatter Hoarding ◽  
New Model ◽  
Desert Ants ◽  
Clark’S Nutcrackers ◽  
Multiple Bearings ◽  
Clark's Nutcrackers

Animals use many different mechanisms to navigate in space. The characteristics of the mechanism employed are usually well-suited to the demands of each particular navigational problem. For example, desert ants navigating in a relatively featureless environment use path integration, birds homing or migrating over long distances use compasses of various sorts, salmon returning to their natal stream home on olfactory cues. The study of navigation requires the study of many different taxa confronting different problems. One interesting case involves scatter-hoarding species that use memory to relocate their hidden food. Such animals face the problem of remembering many locations simultaneously. Clark's nutcrackers (Nucifraga columbiana) are an excellent example, and this paper considers their possible use of multiple bearings from landmarks.

Study of curved surface sliding isolated structure with multiple bearings

2021 ◽  
Vol 147 ◽  
pp. 106814
Author(s):  
Changhong Wang ◽  
Shijie Han ◽  
Baolin Hu ◽  
Wenfu He ◽  
Yonas Keleta
Keyword(s):  
Curved Surface ◽  
Multiple Bearings

High Frequency Incipient Fault Detection for Engine Bearing Components

2005 ◽  
Author(s):  
Pattada Kallappa ◽  
Carl S. Byington ◽  
Patrick W. Kalgren ◽  
Michael DeChristopher ◽  
Sanket Amin
Keyword(s):  
High Frequency ◽  
Vibration Monitoring ◽  
Waveform Analysis ◽  
Turbine Engines ◽  
Diagnostic Features ◽  
Emission Data ◽  
Turbine Engine ◽  
Driven System ◽  
Multiple Bearings ◽  
Contact Sensors

This paper investigates the feasibility of using contact and non-contact sensors to develop an ultra high frequency (UHF) vibration monitoring system for prognostics/diagnostics of turbine engine bearings. The authors have developed ImpactEnergy™, a feature extraction and analysis driven system that integrates high frequency vibration/acoustic emission data, collected using accelerometers and a laser interferometer to assess the health of bearings and gearboxes in turbine engines. ImpactEnergy™ combines advanced diagnostic features derived from waveform analysis, high-frequency enveloping, and more traditional time domain processing like root mean square (RMS) and kurtosis with classification techniques to provide bearing health information. The intelligent UHF concepts based system (System) presented in this paper is tested and validated in a laboratory environment by monitoring multiple bearings on test rigs that replicate the operational loads of a turbomachinery environment. The UHF system is also applied to data collected on test rigs at original equipment manufacturer (OEM) locations.

scholarly journals Parametric Resonances of a Spinning Cyclic Symmetric Rotor Assembled to a Flexible Stationary Housing Via Multiple Bearings

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
W. C. Tai ◽  
I. Y. Shen
Keyword(s):  
Equations Of Motion ◽  
Housing System ◽  
Combination Resonance ◽  
Linear Elastic ◽  
Model Free ◽  
Parametric Resonances ◽  
Symmetric Rotor ◽  
Cyclic Symmetric ◽  
Central Shaft ◽  
Multiple Bearings

This paper is meant to model free vibration of a coupled rotor-bearing-housing system. In particular, the rotor is cyclic symmetric and spins at constant speed while the housing is stationary and flexible. The rotor and housing are assembled via multiple, linear, elastic bearings. A set of equations of motion is derived using component mode synthesis, in which the rotor and the housing each are treated as a component. The equations of motion take the form of ordinary differential equations with periodic coefficients. Analyses of the equations of motion indicate that instabilities could appear at certain spin speed in the form of combination resonances of the sum type. To demonstrate the validity of the formulation, two numerical examples are studied. For the first example, the spinning rotor is an axisymmetric disk, and the housing is a square plate with a central shaft. The rotor and the housing are connected via two linear elastic bearings. For the second example, the rotor is cyclic symmetric in the form of a disk with four evenly spaced radial slots. The housing and bearings remain the same. In both examples, instability appears as a combination resonance of the sum type between a rotor mode and an elastic housing mode. The cyclic symmetric rotor, however, has more instability zones. Finally, effects of damping are studied. Damping of the housing widens the instability zones, whereas the damping of the rotor does the opposite.

scholarly journals Improvement of Adaptive GAs and Back Propagation ANNs Performance in Condition Diagnosis of Multiple Bearing System Using Grey Relational Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Lili A. Wulandhari ◽  
Antoni Wibowo ◽  
Mohammad I. Desa
Keyword(s):  
Grey Relational Analysis ◽  
Back Propagation ◽  
Vibration Signal ◽  
Features Extraction ◽  
Vibration Signals ◽  
Relational Analysis ◽  
Diagnosis Method ◽  
Multiple Bearings ◽  
Grey Relational ◽  
Condition Diagnosis

Condition diagnosis of multiple bearings system is one of the requirements in industry field, because bearings are used in many equipment and their failure can result in total breakdown. Conditions of bearings commonly are reflected by vibration signals data. In multiple bearing condition diagnosis, it will involve many types of vibration signals data; thus, consequently, it will involve many features extraction to obtain precise condition diagnosis. However, large number of features extraction will increase the complexity of the diagnosis system. Therefore, in this paper, we presented a diagnosis method which is hybridization of adaptive genetic algorithms (AGAs), back propagation neural networks (BPNNs), and grey relational analysis (GRA) to diagnose the condition of multiple bearings system. AGAs are used in the diagnosis algorithm to determine the best initial weights of BPNNs in order to improve the diagnosis accuracy. In addition, GRA is applied to determine and select the dominant features from the vibration signal data which will provide good diagnosis of multiple bearings system in less features extraction. The experiments results show that AGAs-BPNNs with GRA approaches can increase the accuracy of diagnosis in shorter processing time, compared with the AGAs-BPNNs without the GRA.

Ground-Based Response of a Spinning, Cyclic Symmetric Rotor Assembled to a Flexible Stationary Housing via Multiple Bearings

2013 ◽  
Author(s):  
W. C. Tai ◽  
I. Y. Shen
Keyword(s):  
Theoretical Analysis ◽  
Coordinate System ◽  
Equation Of Motion ◽  
Periodic Coefficients ◽  
Numerical Example ◽  
Housing Systems ◽  
Rotor Bearing ◽  
Symmetric Rotor ◽  
Cyclic Symmetric ◽  
Multiple Bearings

This paper is to study free response of a spinning, cyclic symmetric rotor assembled to a flexible housing via multiple bearings. In particular, the rotor spins at a constant speed ω3, and the housing is excited via a set of initial displacements. The focus is to study ground-based response of the rotor through theoretical and numerical analyses. The paper consists of three parts. The first part is to briefly summarize an equation of motion of the coupled rotor-bearing-housing systems for the subsequent analyses. The equation of motion, obtained from prior research [1], employs a ground-based and a rotor-based coordinate system to the housing and the rotor, respectively. As a result, the equation of motion takes the form of a set of ordinary differential equations with periodic coefficients of frequency ω3. To better understand its solutions, a numerical model is introduced as an example. In this example, the rotor is a disk with four radial slots and the housing is a square plate with a central shaft. The rotor and housing are connected via two ball bearings. The second part of the paper is to analyze the rotor’s response in the rotor-based coordinate system theoretically. When the rotor is at rest, let ωH be the natural frequency of a coupled rotor-bearing-housing mode whose response is dominated by the housing. The theoretical analysis then indicates that response of the spinning rotor will possess frequency components ωH ± ω3 demonstrating the interaction of the spinning rotor and the housing. The theoretical analysis further shows that this splitting phenomenon results from the periodic coefficients in the equation of motion. The numerical example also confirms this splitting phenomenon. The last part of the paper is to analyze the rotor’s response in the ground-based coordinate system. A coordinate transformation shows that the ground-based response of the spinning rotor consists of two major frequency branches ωH − (k + 1) ω3 and ωH − (k − 1) ω3, where k is an integer determined by the cyclic symmetry and vibration modes of interest. The numerical example also confirms this derivation.

A Hybrid Signal Processing Technique for Bearing Defect Severity Estimation

2005 ◽  
Author(s):  
Robert X. Gao ◽  
Ruqiang Yan
Keyword(s):  
Signal Processing ◽  
Wavelet Packet ◽  
Principal Component ◽  
Processing Technique ◽  
Statistical Features ◽  
Signal Processing Technique ◽  
Severity Estimation ◽  
Bearing Defect ◽  
Multiple Bearings ◽  
Health Diagnosis

This paper presents a hybrid signal processing technique for bearing defect feature extraction and severity estimation. This is achieved by decomposing vibration signals measured on multiple bearings with different defect conditions into multiple sub-bands by means of the wavelet packet transform (WPT). Representative statistical features for each sub-band are then calculated. Subsequently, Principal Component Analysis (PCA) is performed on the statistical features to choose the best-suited representative features as inputs to a diagnostic classifier for bearing health diagnosis.

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