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

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

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dc.contributor.author Usha, Nair
dc.contributor.author Dr. Narayanan Namboothiri, V N
dc.contributor.author Dr. Narayanan Nampoori, V P
dc.date.accessioned 2012-03-12T10:40:20Z
dc.date.available 2012-03-12T10:40:20Z
dc.date.issued 2008-12
dc.identifier.uri http://dyuthi.cusat.ac.in/purl/2813
dc.description Division Of Mechanical Engineering,Cochin University of Science and Technology en_US
dc.description.abstract 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. en_US
dc.language.iso en en_US
dc.publisher Cochin University of Science and Technology en_US
dc.subject Permutation entropy (PE) en_US
dc.subject Charge coupled device (CCD) en_US
dc.subject Turning Process en_US
dc.subject vocal disorders en_US
dc.subject lathe machine en_US
dc.subject Normalized Coarse-grained Information Rate (NCIR). en_US
dc.subject microphone en_US
dc.subject speech signals en_US
dc.subject Mechanical Engineering en_US
dc.title Permutation Entropy Based Analysis of Complex Signals for Characterising Change in System Dynamics en_US
dc.type Thesis en_US


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