Antonio Zippo

Prof. Dr. Antonio Zippo

University of Modena and Reggio Emilia, Modena, Emilia-Romagna, Italy
orcid

Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2015 in Composite Structures
Verified
Journal Article
Closed access
Antonio ZippoAntonio Zippo
Antonio Zippo
GFGiovanni Ferrari
Giovanni Ferrari
MAMarco Amabili
Marco Amabili
MBMarco Barbieri
Marco Barbieri
Francesco PellicanoFrancesco Pellicano
Francesco Pellicano

22 October 2022 in Journal of Sound and Vibration
Verified
Journal Article
Closed access
MEMoslem Molaie Emamzadeh
Moslem Molaie Emamzadeh
FSFarhad S. Samani
Farhad S. Samani
Antonio ZippoAntonio Zippo
Antonio Zippo
Francesco PellicanoFrancesco Pellicano
Francesco Pellicano

23 January 2019 in Nonlinear Dynamics
Verified
Journal Article
Closed access
PDF file
Antonio ZippoAntonio Zippo
Antonio Zippo
MBMarco Barbieri
Marco Barbieri
GIGiovanni Iarriccio
Giovanni Iarriccio
Francesco PellicanoFrancesco Pellicano
Francesco Pellicano

1 December 2014 in Mechanism and Machine Theory
Verified
Journal Article
Closed access
MBMarco Barbieri
Marco Barbieri
Antonio ZippoAntonio Zippo
Antonio Zippo
Francesco PellicanoFrancesco Pellicano
Francesco Pellicano

1 August 2016 in Insight - Non-Destructive Testing and Condition Monitoring
Verified
Journal Article
Open access
LGL Gelman
L Gelman
NCN Harish Chandra
N Harish Chandra
RKR Kurosz
R Kurosz
FPF Pellicano
F Pellicano
MBMarco Barbieri
Marco Barbieri
Antonio ZippoAntonio Zippo
Antonio Zippo
In this paper, the novel wavelet spectral kurtosis (WSK) technique is applied for the early diagnosis of gear tooth faults. Two variants of the wavelet spectral kurtosis technique, called variable resolution WSK and constant resolution WSK, are considered for the diagnosis of pitting gear faults. The gear residual signal, obtained by filtering the gear mesh frequencies, is used as the input to the SK algorithm. The advantages of using the wavelet-based SK techniques when compared to classical Fourier transform (FT)-based SK is confirmed by estimating the toothwise Fisher's criterion of diagnostic features. The final diagnosis decision is made by a three-stage decision-making technique based on the weighted majority rule. The probability of the correct diagnosis is estimated for each SK technique for comparison. An experimental study is presented in detail to test the performance of the wavelet spectral kurtosis techniques and the decision-making technique

1 May 2016 in Journal of Sound and Vibration
Verified
Journal Article
Closed access
Francesco PellicanoFrancesco Pellicano
Francesco Pellicano
MBMarco Barbieri
Marco Barbieri
Antonio ZippoAntonio Zippo
Antonio Zippo
Matteo StrozziMatteo Strozzi
Matteo Strozzi