Structural Health Monitoring for Rotating Imbalance and Misalignment

Manufacturing ◽  
2003 ◽  
Author(s):  
S. G. Mattson ◽  
S. M. Pandit ◽  
S. A. Kubow
Keyword(s):  
Pulse Signal ◽  
Fixed Number ◽  
Second Development ◽  
Gear Mesh ◽  
Characteristic Roots ◽  
Spectral Estimates ◽  
Spectral Peaks ◽  
Shaft Misalignment ◽  
Single Number

The dynamics of rotating machinery are dominated by responses due to shaft and drive train characteristics such as imbalance, misalignment, gear mesh, and bearings. These phenomena occur a fixed number of times every revolution, and are related to rotational speed. This paper presents a new method for condition monitoring of such phenomena. An autoregressive model used for condition assessment is fit to the pulse signal. Decomposition of the model yields its characteristic roots, which contain the frequency, damping value, and variance of the signal component. The model also provides an analytical spectrum yielding clear spectral peaks with extremely accurate levels. The illustrated results are based on an experiment built to simulate a rotating imbalance, shaft misalignment, or a combination of the two. Three areas of improvement are identified. The first is characterization of extra information in the speed signal, eliminating the need for an added measurement that must be related to the speed. The second development is a single number estimate of energies at defect frequencies, eliminating subjective interpretation of spectral plots. The third enhancement is the technique’s ability to make accurate spectral estimates based on limited data, allowing the technician to handle situations where rapid speed changes are unavoidable.

Characterization of Tissue Microstructure Scatterer Distribution with Spectral Correlation

1993 ◽  
Vol 15 (3) ◽  
pp. 238-254 ◽  
Author(s):  
Tomy Varghese ◽  
Kevin D. Donohue
Keyword(s):  
Formation Process ◽  
Biological Tissue ◽  
Statistical Parameters ◽  
Spectral Correlation ◽  
Tissue Microstructure ◽  
Power Spectral ◽  
Spectral Peaks ◽  
Ultrasound Signal

Characterization of tissue microstructure from the backscattered ultrasound signal using the spectral autocorrelation (SAC) function provides information about the scatterer distribution in biological tissue. This paper demonstrates SAC capabilities in characterizing periodicities in A-scans due to regularity in the scatterer distribution. The A-scan is modelled as a cyclostationary signal, where the statistical parameters of the signal vary in time with single or multiple periodicities. This periodicity manifests itself as spectral peaks both in the power spectral density (PSD) and in the SAC. Periodicity in the PSD will produce a well defined dominant peak in the cepstrum, which has been used to determine the scatterer spacing. The relationship between the scatterer spacing and the spacing of the spectral peaks is established using a stochastic model of the echo-formation process from biological tissue. The distribution of the scatterers within the microstructure is modelled using a Gamma function, which offers a flexible method of simulating parametric regularity in the scatterer spacing. Simulations of the tissue microstructure for lower orders of regularity indicate that the SAC components reveal information about the scatterer spacing that are not seen in the PSD and the cepstrum. The echo-formation process is tested by simulating microstructure of varying regularity and analyzing their effect on the SAC, PSD and cepstrum. Experimental validation of the simulation results are provided using in vivo scans of the breast and liver tissue that show the presence of significant spectral correlation components in the SAC.

Raman Characterization of Protocrystalline Silicon Films

2009 ◽  
Vol 1153 ◽  
Author(s):  
A. J. Syllaios ◽  
S. K. Ajmera ◽  
G. S. Tyber ◽  
C. L. Littler ◽  
R. E. Hollingworth
Keyword(s):  
Substrate Temperature ◽  
Dopant Concentration ◽  
Hydrogen Dilution ◽  
Spectral Peaks ◽  
And Performance ◽  
And Shifts ◽  
P Type ◽  
Hydrogenated Amorphous ◽  
Raman Spectral

AbstractAn increasingly important application of thin film hydrogenated amorphous silicon (α-Si:H) is in infrared detection for microbolometer thermal imaging arrays. Such arrays consist of thin α-Si:H films that are integrated into a floating thermally isolated membrane structure. Among the α-Si:H material properties affecting the design and performance of microbolometers is the microstructure. In this work, Raman spectroscopy is used to study changes in the microstructure of protocrystalline p-type α-Si:H films grown by PECVD as substrate temperature, dopant concentration, and hydrogen dilution are varied. The films exhibit the four Raman spectral peaks corresponding to the TO, LO, LA, and TA modes. It is found that the TO Raman peak becomes increasingly well defined (decreasing line width and increasing intensity), and shifts towards the crystalline TO energy as substrate temperature is increased, H dilution of the reactants is increased, or as dopant concentration is decreased.

Design and Characterization of a Fourier Transform Micro-Spectrometer

2009 ◽  
Vol 74 ◽  
pp. 211-214 ◽  
Author(s):  
Shou Hua Wang ◽  
Hong Bin Yu ◽  
Fook Siong Chau ◽  
X.S. Tang
Keyword(s):  
Fourier Transform ◽  
Optical Resolution ◽  
Input Voltage ◽  
Optical Path Difference ◽  
Path Difference ◽  
Optical Path ◽  
Spectral Peaks ◽  
Grating Interferometer ◽  
Diode Pumped

In this paper, we present a Fourier Transform micro-spectrometer which works based on a lamellar grating interferometer. The spectrometer model is electrostatically driven by parallel plate actuators which change the optical path difference (OPD) between movable grating facets and fixed ones. With a 200V input voltage, a maximum OPD of 48.7μm is achieved. The spectrum of a combined light source of a diode-pumped solid-state (DPSS) laser (λ=532.0nm) and a laser diode (λ=637.2nm) is experimentally acquired to demonstrate the performance of the model. The reconstructed spectrum displays two separate spectral peaks at 530.5nm and 635.2nm and the corresponding full-width at half-maximum (FWHM) resolutions are 9.8nm and 12.8nm respectively, indicating good wavelength accuracy and optical resolution.

Preparation and Characterization of the Active Layer for an Led Based on Oxidized Porous Silicon

1996 ◽  
Vol 452 ◽  
Author(s):  
L. Tsybeskov ◽  
K. D. Hiirschman ◽  
L. F. Moore ◽  
P. M. Fauchet ◽  
P. D. J. Calcott
Keyword(s):  
Porous Silicon ◽  
Carrier Transport ◽  
Strong Dependence ◽  
Band Emission ◽  
Annealing Conditions ◽  
Near Ir ◽  
Spectral Peaks ◽  
Oxidized Porous Silicon ◽  
Ir Emission

AbstractWe have studied the photoluminescence (PL) in oxidized porous silicon (PSi), prepared from anodized crystalline Si followed by annealing at temperatures ranging from 700 to 1000°C. It has been found that two PL bands with spectral peaks at 1.6 e V (near-IR band) and near 2 eV (red band) exist with a strong dependence on preparation (annealing) conditions. Recent experimental results show a correlation between the intensity of the near-IR band and the level of leakage current in the diode-like structure. The suppression of the near-IR emission results in improved carrier transport, and the enhancement of the red band emission maximizes the electroluminescence (EL) efficiency. The PL study of thermally oxidized PSi indicates different recombination mechanisms. The red PL band is associated with a mechanism similar to band-tail-recombination within the quasi-bandgap of Si nanograins, whereas the near infra-red PL is associated with recombination via defect centers. These mechanisms will be discussed.

scholarly journals Optimal Bounds for the Change-Making Problem

1991 ◽  
Vol 20 (371) ◽  
Author(s):  
Dexter Kozen ◽  
Shmuel Zaks
Keyword(s):  
Greedy Algorithm ◽  
Related Problem ◽  
Complete Characterization ◽  
Fixed Number ◽  
Simple Test ◽  
Optimal Representation ◽  
Optimal Bounds ◽  
Np Complete ◽  
The Greedy Algorithm

<p>The change-making problem is the problem of representing a given value with the fewest coins possible. We investigate the problem of determining whether the greedy algorithm produces an optimal representation of all amounts for a given set of coin denominations 1 = c_1 &lt; c_2 &lt; ... &lt; c_m. Chang and Gill show that if the greedy algorithm is not always optimal, then there exists a counterexample x in the range</p><p>c_3 &lt;= x &lt; (c_m(c_m c_m-1 + c_m - 3c_m-1)) \ (c_m - c_m-1).</p><p>To test for the existence of such a counterexample, Chang and Gill propose computing and comparing the greedy and optimal representations of all x in this range. In this paper we show that if a counterexample exists, then the smallest one lies in the range c_3 + 1 &lt; x &lt; c_m + c_m-1, and these bounds are tight. Moreover, we give a simple test for the existence of a counterexample that does not require the calculation of optimal representations.</p><p>In addition, we give a complete characterization of three-coin systems and an efficient algorithm for all systems with a fixed number of coins. Finally, we show that a related problem is <em>co</em>NP-complete.</p>

Effect of an Original Gear Mesh Modeling on the Gearbox Dynamic Behavior

2007 ◽  
Author(s):  
Emmanuel Rigaud ◽  
Joe¨l Perret-Liaudet ◽  
Mohamed-Salah Mecibah
Keyword(s):  
Load Distribution ◽  
Degrees Of Freedom ◽  
Equations Of Motion ◽  
Elastic Coupling ◽  
Gear Mesh ◽  
Meshing Stiffness ◽  
Face Width ◽  
Building Process ◽  
Coupling Terms

Prediction of the vibratory and acoustical behavior of gearboxes is generally based on characterization of the excitation sources, overall modeling of the gearbox, modal analysis and solving of the parametric equations of motion generated by these models. On the building process of such large degrees of freedom models, the elastic coupling induced by the gear mesh is generally described by the parametric meshing stiffness k(t) along the line of action. This kind of model is not able to take into account the load distribution along the tooth face width, even though the resulting moment can constrain rotational angles associated to wheels tilting and flexural deformation of shafts. The scope of this study is to introduce the coupling terms between wheels associated to these phenomena. Some examples show how they can influence the gear modal characteristics and dynamic response and, consequently, the vibratory and acoustical response of the gearbox.

Guided-Wave Approach for Spectral Peaks Characterization of Impact-Echo Tests in Layered Systems

2017 ◽  
Vol 23 (4) ◽  
pp. 04017009 ◽  
Author(s):  
Pablo Gómez ◽  
José-Paulino Fernández-Álvarez ◽  
Alejandro Áres ◽  
Enol Fernández
Keyword(s):  
Guided Wave ◽  
Layered Systems ◽  
Impact Echo ◽  
Wave Approach ◽  
Spectral Peaks

Spectral Estimates for Towers of Noncompact Quotients

1999 ◽  
Vol 51 (2) ◽  
pp. 266-293 ◽  
Author(s):  
Anton Deitmar ◽  
Werner Hoffman
Keyword(s):  
Analogous Result ◽  
Congruence Subgroup ◽  
Kernel Method ◽  
Linear Algebraic Group ◽  
Fixed Number ◽  
Principal Congruence ◽  
Congruence Subgroups ◽  
Spectral Estimates ◽  
The Family ◽  
Upper Estimate

AbstractWe prove a uniform upper estimate on the number of cuspidal eigenvalues of the Γ-automorphic Laplacian below a given bound when Γ varies in a family of congruence subgroups of a given reductive linear algebraic group. Each Γ in the family is assumed to contain a principal congruence subgroup whose index in Γ does not exceed a fixed number. The bound we prove depends linearly on the covolume of Γ and is deduced from the analogous result about the cut-off Laplacian. The proof generalizes the heat-kernel method which has been applied by Donnelly in the case of a fixed lattice Γ.

A characterization of lambda-terms transforming numerals

2016 ◽  
Vol 26 ◽  
Author(s):  
PAWEŁ PARYS
Keyword(s):  
Natural Number ◽  
Linear Transformation ◽  
Fixed Number ◽  
The Other ◽  
Natural Numbers ◽  
Data Types ◽  
Fixed Type ◽  
Intersection Types ◽  
The One

AbstractIt is well known that simply typed λ-terms can be used to represent numbers, as well as some other data types. We show that λ-terms of each fixed (but possibly very complicated) type can be described by a finite piece of information (a set of appropriately defined intersection types) and by a vector of natural numbers. On the one hand, the description is compositional: having only the finite piece of information for two closed λ-terms M and N, we can determine its counterpart for MN, and a linear transformation that applied to the vectors of numbers for M and N gives us the vector for MN. On the other hand, when a λ-term represents a natural number, then this number is approximated by a number in the vector corresponding to this λ-term. As a consequence, we prove that in a λ-term of a fixed type, we can store only a fixed number of natural numbers, in such a way that they can be extracted using λ-terms. More precisely, while representing k numbers in a closed λ-term of some type, we only require that there are k closed λ-terms M1,. . .,Mk such that Mi takes as argument the λ-term representing the k-tuple, and returns the i-th number in the tuple (we do not require that, using λ-calculus, one can construct the representation of the k-tuple out of the k numbers in the tuple). Moreover, the same result holds when we allow that the numbers can be extracted approximately, up to some error (even when we only want to know whether a set is bounded or not). All the results remain true when we allow the Y combinator (recursion) in our λ-terms, as well as uninterpreted constants.

Profile Recognition and Mensuration in Machine Vision

1997 ◽  
Vol 119 (3) ◽  
pp. 417-424 ◽  
Author(s):  
S. M. Pandit ◽  
R. Guo
Keyword(s):  
Machine Vision ◽  
Feature Weighting ◽  
Shop Floor ◽  
Sampled Data ◽  
Noisy Environment ◽  
Object Boundary ◽  
Weighting Method ◽  
Characteristic Roots ◽  
Ar Models

This paper presents a systematic profile recognition and mensuration approach in machine vision. It can be utilized to recognize and measure the profiles of industrial parts in an automated manufacturing process by machine vision systems. A new method of profile representation by sampling the data from the object boundary in a digital image is presented. Autoregressive (AR) models are used to code the sampled data of the profiles into AR coefficients for profile recognition. Characterization of the profiles is accomplished by the Data Dependent Systems (DDS) methodology. The AR coefficients and characteristic roots help construct the AR and DDS descriptors to characterize the signatures of the profiles. The frequency domain information about the profiles can be extracted by DDS analysis. The measurement of the profile variation is obtained from the DDS results using optical mensuration method. Neural network and feature weighting method are utilized as reasoning machines for recognition. The illustrative examples in which the profile sampled data are corrupted by noise show that the profile recognition and mensuration approach is very effective and robust in a typical noisy environment on the shop floor.

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