Analytical model to determine fundamental frequency of free vibration of perforated plate by using unit step functions to express non-homogeneity

In the current study an analytical model to determine fundamental frequency of perforated plate is formulated. Non-homogeneity in Young’s modulus and density due to perforation is expressed by using unit step function in Rayleigh’s Quotient. In the present analysis the boundary condition considered is clamped at all edges. Perforated plate is considered as plate with uniformly distributed mass and holes are considered as nonhomogeneous patches. The deflected middle surface of the plate is approximated by a function which satisfies the boundary conditions. The proposed approach is validated by comparing results with finite element method modal analysis

An analytical model to determine fundamental frequency of rectangular plate having rectangular array of circular perforations

In the current study an analytical model to determine fundamental frequency of perforated plate with circular perforation is formulated. Circular holes are replaced by equivalent square hole and non homogeneity in Young’s modulus and density due to equivalent square perforation is expressed by using unit step functions. Analytical formulation is based on Rayleigh-Ritz method. In present study boundary condition considered is clamped at all edges. The deflected middle surface of the plate is approximated by a function which satisfies the boundary conditions. Proposed approach is validated by comparing numerical analysis results with Finite Element Method (FEM) modal analysis results

Comparing PID and H-infinity controllers on a 2-DoF nonlinear quarter car suspension system

Automotive suspension system is an important part of car comfort and safety. In this article active suspension for 2DOF nonlinear coupled Passenger-Car model with force actuator is designed using PID control and H-infinity control. This paper is focused on comparison of those two controllers. Simulations on an exact nonlinear model of the suspension are performed for control validation

Development of predictive model for vibro-acoustic condition monitoring of lathe

Present day requirements for enhanced reliability of rotating machinery have become critical for the manufacturing sector. Every rotating machine exhibits a unique characteristic vibration and acoustic signature. This can be used to identify the defective parts and estimate the present severity of the problem; most importantly, without opening the machine for inspection. Moreover, it aids in the reduction of unscheduled down time, turnaround time and existing noise levels. The paper deals with the vibro-acoustic condition monitoring of metal lathe and development of predictive models for the detection of probable faults using Machine Learning. Experiments were conducted to obtain vibration signatures using accelerometers and the data was further processed while the acoustic signatures were obtained using noise level meters. Results were obtained for idle running, turning and facing operations using a single point cutting tool for constant spindle speeds, feed and depth of cut. The vibro-acoustic signatures of six metal lathe machines were collected over a period of 5 months and the trends obtained were analyzed. The filtered acceleration (g-peak) signatures were compared with the General Machinery Vibration Severity Chart and based on the velocity classification results, the best machine was chosen for the development of predictive models. Vibration as well as acoustic signatures were isolated using filters, empirical relations and manufacturing data. Predictive models were made using machine learning algorithms to predict the failure of the lathe based on its historical data. These models can be used by industries to detect unhealthy trends and identify troublesome parts in the machine which can be then scheduled for maintenance thereby decreasing production downtimes

Bearing failure prediction using Wigner-Ville distribution, modified Poincare mapping and fast Fourier transform

This study outlines the experimental investigation methods of condition monitoring to predict bearing failures using the experimental vibration signatures. The purpose of condition monitoring is to maximize the machine availability and utility of the machine components. Bearings being one of the most common component in any rotating machinery, it is vital to study the health of the bearing and can predict bearing failure, its location and severity. This prevents machine downtime, monetary loss and unfortunate accidents. A test rig was fabricated to get the vibration signatures of bearings. Prediction of bearing failure relies on the presence of the bearing characteristic frequencies – inner race frequency, outer race frequency, ball pass frequency and fundamental train frequency – and its harmonics in the vibration signal acquired. These frequencies are present in the vibration signature due to the interaction of surfaces of different bearing components that have defects in them. Both time and frequency domain numerical signature analysis were performed on the vibration signatures acquired. Simple frequency domain method like Fast Fourier Transform (FFT), chaotic vibration method like modified Poincare mapping and time-frequency domain Wigner-Ville distribution (WVD) were used in detecting bearing failure. Using the FFT analysis method, it is hard to predict the failures, hence better signal processing methods like modified Poincare mapping and WVD are used. Also, it is observed that the chaotic vibration signatures found in the lower-order mechanical systems like bearings. With the chaotic analysis methods like, Poincare Mapping and Wigner-Ville Distribution, the location and the severity of the bearing failure can be predicted

Development of predictive model for vibro-acoustic condition monitoring of lathe

Present day requirements for enhanced reliability of rotating machinery have become critical for the manufacturing sector. Every rotating machine exhibits a unique characteristic vibration and acoustic signature. This can be used to identify the defective parts and estimate the present severity of the problem; most importantly, without opening the machine for inspection. Moreover, it aids in the reduction of unscheduled down time, turnaround time and existing noise levels. The paper deals with the vibro-acoustic condition monitoring of metal lathe and development of predictive models for the detection of probable faults using Machine Learning. Experiments were conducted to obtain vibration signatures using accelerometers and the data was further processed while the acoustic signatures were obtained using noise level meters. Results were obtained for idle running, turning and facing operations using a single point cutting tool for constant spindle speeds, feed and depth of cut. The vibro-acoustic signatures of six metal lathe machines were collected over a period of 5 months and the trends obtained were analyzed. The filtered acceleration (g-peak) signatures were compared with the General Machinery Vibration Severity Chart and based on the velocity classification results, the best machine was chosen for the development of predictive models. Vibration as well as acoustic signatures were isolated using filters, empirical relations and manufacturing data. Predictive models were made using machine learning algorithms to predict the failure of the lathe based on its historical data. These models can be used by industries to detect unhealthy trends and identify troublesome parts in the machine which can be then scheduled for maintenance thereby decreasing production downtimes

Study of Sarasvati Veena – a South Indian musical instrument using its vibro-acoustic signatures

This study aims to characterize and standardize the Sarasvati Veena of South India. The instrument is very unique in the way in which it is manufactured and played. This instrument is often tuned relative to the accompanying instruments. The aim of the study was to find out the optimum tuning frequency for a particular Veena. The natural frequency of the body of the Veena was determined by conducting an impact test. The strings of the Veena were tuned to the corresponding body frequency to obtain resonance and FFT plots were generated to see the frequency variation. The optimum tuning frequency for a given Veena was obtained such that the body undergoes conductive resonance when tuned to that particular frequency. Acoustic readings were taken using a sound level meter to confirm on the results obtained by impact test

Full potential LAPW calculation of electron momentum density and related properties of Li

Electron momentum density and Compton profiles in Lithium along $$,$$, and $$ directions are calculated using Full-Potential Linear Augmented Plane Wave basis within generalized gradient approximation. The profiles have been corrected for correlations with Lam-Platzman formulation using self-consistent charge density. The first and second derivatives of Compton profiles are studied to investigate the Fermi surface breaks. Decent agreement is observed between recent experimental and our calculated values. Our values for the derivatives are found to be in better agreement with experiments than earlier theoretical results. Two-photon momentum density and one- and two-dimensional angular correlation of positron annihilation radiation are also calculated within the same formalism and including the electron-positron enhancement factor.Comment: 11 pages, 7 figures TO appear in Physical Review

Paracrine signaling by glial cell-derived triiodothyronine activates neuronal gene expression in the rodent brain and human cells

Hypothyroidism in humans is characterized by severe neurological consequences that are often irreversible, highlighting the critical role of thyroid hormone (TH) in the brain. Despite this, not much is known about the signaling pathways that control TH action in the brain. What is known is that the prohormone thyroxine (T4) is converted to the active hormone triiodothyronine (T3) by type 2 deiodinase (D2) and that this occurs in astrocytes, while TH receptors and type 3 deiodinase (D3), which inactivates T3, are found in adjacent neurons. Here, we modeled TH action in the brain using an in vitro coculture system of D2-expressing H4 human glioma cells and D3-expressing SK-N-AS human neuroblastoma cells. We found that glial cell D2 activity resulted in increased T3 production, which acted in a paracrine fashion to induce T3-responsive genes, including ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), in the cocultured neurons. D3 activity in the neurons modulated these effects. Furthermore, this paracrine pathway was regulated by signals such as hypoxia, hedgehog signaling, and LPS-induced inflammation, as evidenced both in the in vitro coculture system and in in vivo rat models of brain ischemia and mouse models of inflammation. This study therefore presents what we believe to be the first direct evidence for a paracrine loop linking glial D2 activity to TH receptors in neurons, thereby identifying deiodinases as potential control points for the regulation of TH signaling in the brain during health and disease.NIHFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Hungarian Scientific Research FundHungarian Academy of SciencesUniv Miami, Miller Sch Med, Div Endocrinol Diabet & Metab, Miami, FL 33136 USAUniversidade Federal de São Paulo, Mol Endocrinol Lab, Div Endocrinol, Dept Med, São Paulo, BrazilHungarian Acad Sci, Inst Expt Med, Lab Endocrine Neurobiol, Budapest, HungaryBrigham & Womens Hosp, Thyroid Sect, Div Endocrinol Diabet & Hypertens, Boston, MA 02115 USATufts Med Ctr, Div Endocrinol Diabet & Metab, Dept Med, Tupper Res Inst, Boston, MA USATufts Univ, Sch Med, Dept Neurosci, Boston, MA 02111 USAUniversidade Federal de São Paulo, Mol Endocrinol Lab, Div Endocrinol, Dept Med, São Paulo, BrazilNIH: DK77086NIH: DK37021FAPESP: 05/55825-8FAPESP: 05/55826-4Hungarian Scientific Research Fund: OTKA K81226Web of Scienc