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Due to the importance of rotating machinery as one of the most widely used industrial element, development a proper monitoring and fault diagnosis technique to prevent malfunction and failure of machine during operation is necessary. This paper presents a method for gearbox fault diagnosis based on feature extraction technique, distance evaluation technique and the support vector machines (SVMs) ensemble...
This paper proposes a minimum description length (MDL) based method for signal change detection in machine condition monitoring. Our method is grounded on a recently proposed MDL-based sequentially normalized maximum likelihood (SNML) approach to time series and especially signals complexity analysis with an autoregressive (AR) model. Experiments on signal change detection are performed using two...
A nonlinearly discriminant feature extraction and fusion scheme is proposed to recognize the different condition of mechanical faults. As most running statuses of machines are nonlinear and non-stationary, it is difficult to extract the effective features for fault diagnosis by linear feature extractor such as principal component analysis (PCA) and Fisher linear discriminant analysis (FLD). Therefore,...
We present a Bayesian framework to tackle the problem of sensor estimation, a critical step of fault diagnosis in machine condition monitoring. A Gaussian mixture model is employed to model the normal operating range of the machine. A Gaussian random vector is introduced to model the possible deviations of the observed sensor values from their corresponding normal values. Different levels of deviations...
Since a damaged or abnormal-state machine often generates highly nonlinear signals, it is desirable to use a tool that can effectively detect and analyze nonlinear signatures. The bicoherence has been proposed for such nonlinear analysis since it is a measure of the phase coupling between interacting frequency components. However, the bicoherence has some difficulties in machine condition monitoring...
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