Recurrence Quantification Analysis of System Signals for Detecting Tool and Chatter in Turning

In this thesis, the applications of the recurrence quantification analysis in metal cutting operation in a lathe, with specific objective to detect tool wear and chatter, are presented.This study is based on the discovery that process dynamics in a lathe is low dimensional chaotic. It implies that the machine dynamics is controllable using principles of chaos theory. This understanding is to revolutionize the feature extraction methodologies used in condition monitoring systems as conventional linear methods or models are incapable of capturing the critical and strange behaviors associated with the metal cutting process.As sensor based approaches provide an automated and cost effective way to monitor and control, an efficient feature extraction methodology based on nonlinear time series analysis is much more demanding. The task here is more complex when the information has to be deduced solely from sensor signals since traditional methods do not address the issue of how to treat noise present in real-world processes and its non-stationarity. In an effort to get over these two issues to the maximum possible, this thesis adopts the recurrence quantification analysis methodology in the study since this feature extraction technique is found to be robust against noise and stationarity in the signals.The work consists of two different sets of experiments in a lathe; set-I and set-2. The experiment, set-I, study the influence of tool wear on the RQA variables whereas the set-2 is carried out to identify the sensitive RQA variables to machine tool chatter followed by its validation in actual cutting. To obtain the bounds of the spectrum of the significant RQA variable values, in set-i, a fresh tool and a worn tool are used for cutting. The first part of the set-2 experiments uses a stepped shaft in order to create chatter at a known location. And the second part uses a conical section having a uniform taper along the axis for creating chatter to onset at some distance from the smaller end by gradually increasing the depth of cut while keeping the spindle speed and feed rate constant.The study concludes by revealing the dependence of certain RQA variables; percent determinism, percent recurrence and entropy, to tool wear and chatter unambiguously. The performances of the results establish this methodology to be viable for detection of tool wear and chatter in metal cutting operation in a lathe. The key reason is that the dynamics of the system under study have been nonlinear and the recurrence quantification analysis can characterize them adequately.This work establishes that principles and practice of machining can be considerably benefited and advanced from using nonlinear dynamics and chaos theory.

Analysis of System Signals Based on Cross Recurrence Method for System Dynamics Characterization

Natural systems are inherently non linear. Recurrent behaviours are typical of natural systems. Recurrence is a fundamental property of non linear dynamical systems which can be exploited to characterize the system behaviour effectively. Cross recurrence based analysis of sensor signals from non linear dynamical system is presented in this thesis. The mutual dependency among relatively independent components of a system is referred as coupling. The analysis is done for a mechanically coupled system specifically designed for conducting experiment. Further, cross recurrence method is extended to the actual machining process in a lathe to characterize the chatter during turning. The result is verified by permutation entropy method. Conventional linear methods or models are incapable of capturing the critical and strange behaviours associated with the dynamical process. Hence any effective feature extraction methodologies should invariably gather information thorough nonlinear time series analysis. The sensor signals from the dynamical system normally contain noise and non stationarity. In an effort to get over these two issues to the maximum possible extent, this work adopts the cross recurrence quantification analysis (CRQA) methodology since it is found to be robust against noise and stationarity in the signals. The study reveals that the CRQA is capable of characterizing even weak coupling among system signals. It also divulges the dependence of certain CRQA variables like percent determinism, percent recurrence and entropy to chatter unambiguously. The surrogate data test shows that the results obtained by CRQA are the true properties of the temporal evolution of the dynamics and contain a degree of deterministic structure. The results are verified using permutation entropy (PE) to detect the onset of chatter from the time series. The present study ascertains that this CRP based methodology is capable of recognizing the transition from regular cutting to the chatter cutting irrespective of the machining parameters or work piece material. The results establish this methodology to be feasible for detection of chatter in metal cutting operation in a lathe.

An advanced method for eliminating impacts from frequency deviations in power system signal processing

Frequency deviation is a common problem for power system signal processing. Many power system measurements are carried out in a fixed sampling rate assuming the system operates in its nominal frequency (50 or 60 Hz). However, the actual frequency may deviate from the normal value from time to time due to various reasons such as disturbances and subsequent system transients. Measurement of signals based on a fixed sampling rate may introduce errors under such situations. In order to achieve high precision signal measurement appropriate algorithms need to be employed to reduce the impact from frequency deviation in the power system data acquisition process. This paper proposes an advanced algorithm to enhance Fourier transform for power system signal processing. The algorithm is able to effectively correct frequency deviation under fixed sampling rate. Accurate measurement of power system signals is essential for the secure and reliable operation of power systems. The algorithm is readily applicable to such occasions where signal processing is affected by frequency deviation. Both mathematical proof and numerical simulation are given in this paper to illustrate robustness and effectiveness of the proposed algorithm. Crown Copyright (C) 2003 Published by Elsevier Science B.V. All rights reserved.

Sea level measurement using single GNSS antenna SNR signals

This thesis presents a possible method to calculate sea level variation using geodetic-quality Global Navigate Satellite System (GNSS) receivers. Three antennas are used: two small antennas and a choke ring one, analyzing only Global Positioning System signals. The main goal of the thesis is to test a modified configuration for antenna set up. In particular, measurements obtained tilting one antenna to face the horizon are compared to measurements obtained from antennas looking upward. The location of the experiment is a coastal environment nearby the Onsala Space Observatory in Sweden. Sea level variations are obtained using periodogram analysis of the SNR signal and compared to synthetic gauge generated from two independent tide gauges. The choke ring antenna provides poor result, with an RMS around 6 cm and a correlation coefficients of 0.89. The smaller antennas provide correlation coefficients around 0.93. The antenna pointing upward present an RMS of 4.3 cm and the one pointing the horizon an RMS of 6.7 cm. Notable variation in the statistical parameters is found when modifying the length of the interval analyzed. In particular, doubts are risen on the reliability of certain scattered data. No relation is found between the accuracy of the method and weather conditions. Possible methods to enhance the available data are investigated, and correlation coefficient above 0.97 can be obtained with small antennas when sacrificing data points. Hence, the results provide evidence of the suitability of SNR signal analysis for sea level variation in coastal environment even in the case of adverse weather conditions. In particular, tilted configurations provides comparable result with upward looking geodetic antennas. A SNR signal simulator is also tested to investigate its performance and usability. Various configuration are analyzed in combination with the periodogram procedure used to calculate the height of reflectors. Consistency between the data calculated and those received is found, and the overall accuracy of the height calculation program is found to be around 5 mm for input height below 5 m. The procedure is thus found to be suitable to analyze the data provided by the GNSS antennas at Onsala.

Novel action of a wake-promoting neuropeptide in hippocampus : inhibition of nmda receptor function at excitatory synapses through orexin-a

One of the most intriguing functions of the brain is the ability to learn and memorize. The mechanism through which memory and learning are expressed requires the activation of NMDA receptors (NMDARs). These molecular entities are placed at the postsynaptic density of excitatory synapses and their function is tightly controlled by the actions of several modulators at the extracellular, intracellular and pore sites. A large part of the intracellular modulation comes from the action of G-protein coupled receptors (GPCRs). Through intracellular cascades typically involving kinases and phosphatases, GPCRs potentiate or inhibit NMDARs, controlling the conductive state but also the trafficking within the synapse. The GPCRs are involved in the modulation of a variety of brain functions. Many of them control cognition, memory and learning performance, therefore, their effects on NMDARs are extensively studied. The orexinergic system signals through GPCRs and it is well known for the regulation of waking, feeding, reward and autonomic functions. Moreover, it is involved in potentiating hippocampus-related cognitive tasks. Orexin receptors and fibers are present within the hippocampus, but whether these directly modulate hippocampal cells and synapses has not yet been determined. During my thesis, I studied orexinergic actions on excitatory synaptic transmission via whole-cell patch-clamp recordings in rat acute hippocampal slices. I observed that exogenously applied orexin-A (ox-A) exerted a strong inhibitory action on NMDAR-mediated synaptic potentials at mossy fiber (MF)-CA3 synapses, by postsynaptically activating orexin-2 receptors, a minor inhibition at Schaffer collateral-CAl synapses and did not affect other synapses with the CA3 area. Moreover, I demonstrated that the susceptibility of NMDARs to ox- A depends on the tone of endogenous orexin known to fluctuate during the day-night cycle. In fact, in slices prepared during the active period of the rats, when endogenous orexin levels are high, NMDAR-currents were not affected by exogenously applied ox-A. The inhibitory effect of ox-A was, however, reverted when interfering with the orexinergic system through intraperitoneal injections of almorexant, a dual orexin receptor antagonist, during the active phase prior to slice preparation. This thesis work suggests that the orexinergic system regulates NMDAR-dependent information flow through select hippocampal pathways depending on the time-of-day. The specific orexinergic modulation of NMDARs at MFs dampens the excitability of the hippocampal circuit and could impede the mechanisms related to memory formation, possibly also following extended periods of waking. -- La capacité d'apprentissage et de mémorisation est une des fonctions les plus intrigantes de notre cerveau. Il a été montré qu'elles requièrent l'activation des récepteurs NMDA (NMDARs). Ces entités moléculaires sont présentes au niveau de la densité post-synaptique des synapses excitatrices et leur fonction est étroitement contrôlée par l'action de nombreux modulateurs au niveau extracellulaire, intracellulaire et membranaire de ces récepteurs. Une grande partie de la modulation intracellulaire s'effectue via l'action de récepteurs couplés aux protéines G (GPCRs). Grace à leurs cascades intracellulaires typiquement impliquant des kinases et des phosphatases, les GPCRs favorisent l'activation ou l'inhibition des NMDARs, contrôlant ainsi leur perméabilité mais aussi leur mouvement à la synapse. Les GPCRs sont impliquées dans de nombreuses fonctions cérébrales telles que la cognition, la mémoire ainsi que la capacité d'apprentissage c'est pour cela que leurs effets sur les NMDARs sont très étudiés. Le système orexinergique fait intervenir ces GPCRs et est connu par son rôle dans la régulation de fonctions physiologiques telles que l'éveil, la prise alimentaire, la récompense ainsi que d'autres fonctions du système nerveux autonome. De plus, ce système est impliqué dans la régulation de tâches cognitives liées à l'hippocampe. Bien que les fibres et les récepteurs à l'orexine soient présents dans l'hippocampe, leur mécanisme d'action sur les cellules et les synapses de l'hippocampe n'a pas encore été élucidé. Durant ma thèse, je me suis intéressée aux effets de l'orexine sur la transmission synaptique excitatrice en utilisant la méthode d'enregistrement en patch-clamp en configuration cellule entière sur des tranches aiguës d'hippocampes de rats. J'ai observé que l'application exogène d'orexine A d'une part inhibe fortement les courants synaptiques dépendants de l'activation des NMDARs au niveau de la synapse entre les fibres moussues et CA3 via l'activation post-synaptique des orexine récepteurs 2 mais d'autre part n'inhibe que de façon mineure la synapse entre les collatérales de Schaffer et CAI et n'affecte pas les autres synapses impliquant CA3. J'ai également démontré que la sensibilité des NMDARs à l'orexine A dépend de sa concentration endogène qui fluctue durant le cycle éveil-sommeil. En effet, lorsque les coupes d'hippocampes sont préparées durant la période active de l'animal correspondant à un niveau endogène d'orexine élevé, l'application exogène d'orexine A n'a aucun effet sur les courants dépendants de l'activation des NMDARs. Cependant, l'injection dans le péritoine, durant la phase active de l'animal, d'un antagoniste des orexine récepteurs, l'almorexant, va supprimer l'effet inhibiteur de l'orexine A. Les résultats de ma thèse suggèrent donc que le système orexinergique module les informations véhiculées par les NMDARs via des voies de signalisation sélectives de l'hippocampe en fonction du moment de la journée. La modulation orexinergique des NMDARs au niveau des fibres moussues diminue ainsi l'excitabilité du circuit hippocampal et pourrait entraver les mécanismes liés à la formation de la mémoire, potentiellement après de longues périodes d'éveil.

Traffic Signal Inventory Project, 2001

The Center for Transportation Research and Education performed a traffic signal inventory study for the Iowa Department of Transportation. The purpose of this study was to determine the level of compliance with the Manual on Uniform Traffic Control Devices (MUTCD) and other industry standards of traffic signals on the state highway system. Signals were randomly selected throughout the State of Iowa. Only signals in cities with a population less than 5,000 were considered. Several intersections need to be addressed immediately to correct clearance timing settings. Red clearance intervals were frequently too short. A handful of intersections had inadequate pedestrian clearance times. Six intersections had at least one yellow clearance interval that did not meet Institute of Transportation Engineers standards. Some of the intersections likely would not meet traffic signal warrants and should be investigated for possible removal. The most common problem found with traffic signals was a lack of maintenance. Many of the signals had at least one of the following problems: burned out lights (signals and/or pedestrian heads), pedestrian lenses in need of replacement, dirty cabinet/missing or poor filter, missing visors, or inoperative pedestrian push buttons. Timing sheets were frequently missing or out of date. Another frequent noncompliance issue was the use of backplates. The MUTCD states that backplates should be used on signals viewed against a bright sky. The majority of signals inventoried did not have backplates on the mast-arm mounted signals. The timing at some intersections could likely be improved by reducing the cycle length. Where there were multiple signals in close proximity rarely was there any attempt at signal coordination. Finally, a number of intersections had equipment that by today’s standards would be considered obsolete.

Assimilação do IWV-GPS no Brasil: otimização das estimativas do atraso zenital troposférico em tempo real

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Projeto e simulação de um sistema de controle via LMIs da produção de vapor de uma caldeira aquatubular

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Desempenho do RTK em rede sob efeito da cintilação ionosférica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A novel method for power quality multiple disturbance decomposition based on Independent Component Analysis

In this paper, a novel method for power quality signal decomposition is proposed based on Independent Component Analysis (ICA). This method aims to decompose the power system signal (voltage or current) into components that can provide more specific information about the different disturbances which are occurring simultaneously during a multiple disturbance situation. The ICA is originally a multichannel technique. However, the method proposes its use to blindly separate out disturbances existing in a single measured signal (single channel). Therefore, a preprocessing step for the ICA is proposed using a filter bank. The proposed method was applied to synthetic data, simulated data, as well as actual power system signals, showing a very good performance. A comparison with the decomposition provided by the Discrete Wavelet Transform shows that the proposed method presented better decoupling for the analyzed data. (C) 2012 Elsevier Ltd. All rights reserved.

Biometric recognition system using low bandwidth ECG signals

Biometric recognition has recently emerged as part of applications where the privacy of the information is crucial, as in the health care field. This paper presents a biometric recognition system based on the Electrocardiographic signal (ECG). The proposed system is based on a state-of-the-art recognition method which extracts information from the frequency domain. In this paper we propose a new method to increase the spectral resolution of low bandwidth ECG signals due to the limited bandwidth of the acquisition sensor. Preliminary results show that the proposed scheme reveals a higher identification rate and lower equal error rate when compared to previous approaches.

Forearm surface EMG signals recognition and muscoloskeletal system dynamics identification using intelligent computational methods

Biosignals processing, Biological Nonlinear and time-varying systems identification, Electomyograph signals recognition, Pattern classification, Fuzzy logic and neural networks methods

Permutation Entropy Based Analysis of Complex Signals for Characterising Change in System Dynamics

Timely detection of sudden change in dynamics that adversely affect the performance of systems and quality of products has great scientific relevance. This work focuses on effective detection of dynamical changes of real time signals from mechanical as well as biological systems using a fast and robust technique of permutation entropy (PE). The results are used in detecting chatter onset in machine turning and identifying vocal disorders from speech signal.Permutation Entropy is a nonlinear complexity measure which can efficiently distinguish regular and complex nature of any signal and extract information about the change in dynamics of the process by indicating sudden change in its value. Here we propose the use of permutation entropy (PE), to detect the dynamical changes in two non linear processes, turning under mechanical system and speech under biological system.Effectiveness of PE in detecting the change in dynamics in turning process from the time series generated with samples of audio and current signals is studied. Experiments are carried out on a lathe machine for sudden increase in depth of cut and continuous increase in depth of cut on mild steel work pieces keeping the speed and feed rate constant. The results are applied to detect chatter onset in machining. These results are verified using frequency spectra of the signals and the non linear measure, normalized coarse-grained information rate (NCIR).PE analysis is carried out to investigate the variation in surface texture caused by chatter on the machined work piece. Statistical parameter from the optical grey level intensity histogram of laser speckle pattern recorded using a charge coupled device (CCD) camera is used to generate the time series required for PE analysis. Standard optical roughness parameter is used to confirm the results.Application of PE in identifying the vocal disorders is studied from speech signal recorded using microphone. Here analysis is carried out using speech signals of subjects with different pathological conditions and normal subjects, and the results are used for identifying vocal disorders. Standard linear technique of FFT is used to substantiate thc results.The results of PE analysis in all three cases clearly indicate that this complexity measure is sensitive to change in regularity of a signal and hence can suitably be used for detection of dynamical changes in real world systems. This work establishes the application of the simple, inexpensive and fast algorithm of PE for the benefit of advanced manufacturing process as well as clinical diagnosis in vocal disorders.