Analysis Of Large Sets Of Seemingly-Random Experimental Data: The Case Of Electron Emission From Frictional Contacts.
Abstract
Analysis of seemingly random experimental data can be difficult when no
phenomenological model is known. If available data is output for even simple but unknown-dynamics
systems, hypotheses could be made and tested for possible underlying processes, being them from
pure random to deterministic ones, but testing of such hypotheses can be difficult in the practice. For
instance, the study of theoretical systems leading to chaos is an established mathematical field, but the
testing of given experimental data for the hypotheses of deterministic origin versus a stochastic one is
not simple and it may not be possible. Available analysis techniques are reviewed and discussed.
The author and colleagues have carried out extensive experimental research work on emission of
electrons as a probe for in-situ on-time surface monitoring, seeking a better understanding of this
triboemission from dry-sliding contacts and during wear, and of the fractoemission that occurs during
plastic deformation and failure. Particle outputs may be very complex and carry limited information;
Electron triboemission data are typically composed of seemingly deterministic bursts of emission
which are superimposed to lower but seemingly constant levels of random emission. They are large
sets that are acquired in very short-time windows, were the discrete occurrence of counts is matched
to detected particles, but no information about their energy or paths can be simultaneously obtained.
Analysis of triboemission outputs required new approaches and techniques: the author studied
different stochastic-process distributions for fitting to such data, and he also tested the hypothesis of
deterministic-chaos origin. The proposed data analysis can be tools for the study of similar complex
systems. For instance, the author believes that understanding of triboemission and of related
mechanisms is a key to modeling of frictional and charging processes for insulators, mainly for
ceramics, and for semiconductors.
phenomenological model is known. If available data is output for even simple but unknown-dynamics
systems, hypotheses could be made and tested for possible underlying processes, being them from
pure random to deterministic ones, but testing of such hypotheses can be difficult in the practice. For
instance, the study of theoretical systems leading to chaos is an established mathematical field, but the
testing of given experimental data for the hypotheses of deterministic origin versus a stochastic one is
not simple and it may not be possible. Available analysis techniques are reviewed and discussed.
The author and colleagues have carried out extensive experimental research work on emission of
electrons as a probe for in-situ on-time surface monitoring, seeking a better understanding of this
triboemission from dry-sliding contacts and during wear, and of the fractoemission that occurs during
plastic deformation and failure. Particle outputs may be very complex and carry limited information;
Electron triboemission data are typically composed of seemingly deterministic bursts of emission
which are superimposed to lower but seemingly constant levels of random emission. They are large
sets that are acquired in very short-time windows, were the discrete occurrence of counts is matched
to detected particles, but no information about their energy or paths can be simultaneously obtained.
Analysis of triboemission outputs required new approaches and techniques: the author studied
different stochastic-process distributions for fitting to such data, and he also tested the hypothesis of
deterministic-chaos origin. The proposed data analysis can be tools for the study of similar complex
systems. For instance, the author believes that understanding of triboemission and of related
mechanisms is a key to modeling of frictional and charging processes for insulators, mainly for
ceramics, and for semiconductors.
Full Text:
PDFAsociación Argentina de Mecánica Computacional
Güemes 3450
S3000GLN Santa Fe, Argentina
Phone: 54-342-4511594 / 4511595 Int. 1006
Fax: 54-342-4511169
E-mail: amca(at)santafe-conicet.gov.ar
ISSN 2591-3522