Open AccessSearch of a robust defect signature in gear systems across adaptive Morlet wavelet of vibration signals
Author(s) -
Ayad Mouloud,
Chikouche Djamel,
Boukazzoula Nacereddine,
Rezki Mohamed
Publication year - 2014
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2013.0439
Subject(s) - morlet wavelet , signature (topology) , wavelet , vibration , computer science , speech recognition , artificial intelligence , acoustics , pattern recognition (psychology) , algorithm , wavelet transform , mathematics , physics , discrete wavelet transform , geometry
Monitoring of rotating machines by vibration analysis is a topic that has received a great interest in recent years. Moreover, the vibrations from a machine are affected greatly by the conditions of its operation (speed, load and so on). A significant challenge remains with the monitoring of gears under fluctuating operating conditions. An unexpected fault of gear may cause huge economic losses, even personal injury. In this study, a new method based on adaptive Morlet wavelet (AMW) is proposed for the analysis of vibration signals produced from a gear system under test in order to detect early the presence of faults. The mother Morlet wavelet is adapted with the gear vibration signal by setting parameters of the wavelet to balance the time–frequency resolution. The obtained optimal pair of parameters results in the best time–frequency resolution for the given vibration signal; and the fault detection problem is considered just as a simple signature search in the time‐scale domain using scalograms. An early indication of the presence of a gear defect is obtained at the 10th day of experimentation using the AMW‐based method. Whereas, the gear system has a defect on the 12th day corresponding to the tooth damage which results in a complete change in the location of the AMW coefficients.