Latent component-based gear tooth fault detection filter using advanced parametric modeling
In this paper, a new parametric model-based filter is proposed for gear tooth fault detection. The designing of the filter consists of identifying the most proper latent component (LC) of the undamaged gearbox signal by analyzing the instant modules (IMs) and instant frequencies (IFs) and then using the component with lowest IM as the proposed filter output for detecting fault of the gearbox. The filter parameters are estimated by using the LC theory in which an advanced parametric modeling method has been implemented. The proposed method is applied on the signals, extracted from simulated gearbox for detection of the simulated gear faults. In addition, the method is used for quality inspection of the produced NissanJunior vehicle gearbox by gear profile error detection in an industrial test bed. For evaluation purpose, the proposed method is compared with the previous parametric TAR/AR-based filters in which the parametric model residual is considered as the filter output and also YuleWalker and Kalman filter are implemented for estimating the parameters. The results confirm the high performance of the new proposed fault detection method.
2sls two-stage least squares apd aggregate parameter deviation ar auto regressive arkf ar-based filters with kalman filter estimator aryw ar-based filters with yulewalker estimator arma auto regressive moving average aic/bic akaike/bayesian information criteria dkv difference of kurtosis values dlm dynamic linear model dpe deterministic parameter evolution fs functional-subspace fs-tarma functional-subspace tarma gmr gear motion residual if instant frequency im instant module lc latent component lcs latent components ma moving average med minimum entropy deconvolution ols ordinary least square pe prediction error rss residual sum of squares snr signal-to-noise-ratio svd singular value decomposition tar time-dependant auto regressive tarma time-dependant auto regressive moving average tsa time synchronous averaging