Usha, Nair; Dr.Narayanan Namboothiri, V N; Dr.Narayanan Nampoori, V P(Cochin University of Science and Technology, December , 2008)
[+]
[-]
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.
Description:
Division Of Mechanical Engineering,Cochin University of Science and Technology