Abstract
Data-driven diagnostic methods allow to obtain a statistical model of time series and to identify deviations of recorded data from the pattern of the monitored system. Statistical analysis of time series of mechanical vibrations creates a new quality in the monitoring of rotating machines. Most real vibration signals exhibit nonlinear properties well described by scaling exponents. Multifractal analysis, which relies mainly on assessing local singularity exponents, has become a popular tool for statistical analysis of empirical data. There are many methods to study time series in terms of their fractality. Comparing computational complexity, a wavelet leaders algorithm was chosen. Using Wavelet Leaders Multifractal Formalism, multifractal parameters were estimated, taking them as diagnostic features in the pattern recognition procedure, using machine learning methods. The classification was performed using neural network, k-nearest neighbours’ algorithm and support vector machine. The article presents the results of vibration acceleration tests in a demonstration transmission system that allows simulations of assembly errors and teeth wear.
Highlights
Monitoring the operation of a machine, we assume that detection, isolation and identification of a damage requires a comparison of the current state to a state taken as a reference
The article analyses the possibility of using wavelet leaders to select features and neural network, k-nearest neighbours and support vector machines in the process of fault classification of recorded data from the pattern of the monitored system and inferring the occurrence of damage
The paper proposed Multifractal Wavelet Leader method for the automated recognition of the condition of the gearbox based on the monitored vibration signal
Summary
Monitoring the operation of a machine, we assume that detection, isolation and identification of a damage requires a comparison of the current state to a state taken as a reference. Traditional methods of signal analysis in engineering applications are based on the assumption of stationarity. Such methods can not reveal local features in time and frequency domains. The estimation of scaling exponents leads to determination of a multifractal spectrum, which is a statistical model of the monitored signal [2] This fact was used to determine the diagnostic features of the test gear being tested and to identify assembly errors and wear. The article analyses the possibility of using wavelet leaders to select features and neural network, k-nearest neighbours and support vector machines in the process of fault classification of recorded data from the pattern of the monitored system and inferring the occurrence of damage
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