Gear Fault Detection Based on Teager-Huang Transform

Gear fault detection based on Empirical Mode Decomposition (EMD) and Teager Kaiser Energy Operator (TKEO) technique is presented. This novel method is named as Teager-Huang transform (THT). EMD can adaptively decompose the vibration signal into a series of zero mean Intrinsic Mode Functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the Intrinsic Mode Functions at any instant. The experimental results provide effective evidence that Teager-Huang transform has better resolution than that of Hilbert-Huang transform. The Teager-Huang transform can effectively diagnose the fault of the gear, thus providing a viable processing tool for gearbox defect detection and diagnosis

Unit root quantile autoregression testing with smooth structural changes

By incorporating the flexible Fourier form into quantile autoregression model, this paper proposes three new unit root test statistics, which are robust to both non-Gaussian condition and structural changes. Since their limiting distributions are non-standard, a bootstrap procedure is developed to calculate their critical values. Monte Carlo simulation results show that, while Koenker and Xiao (2004) tests are quite conservative under various kinds of error distributions and structural changes, the newly proposed tests have good size performance except for a little size distortion occasionally. Moreover, our tests have much higher performance especially when the sample size is small

Estimation and test for quantile nonlinear cointegrating regression

In order to investigate the nonlinear relationship among economic variables at each quantile level, this paper proposes a quantile nonlinear cointegration model in which the nonlinear relationship at each quantile level is approximated by a polynomial. The parameter estimator in the proposed model is shown to follow a nonstandard distribution asymptotically due to serial correlation and endogeneity. Therefore, this paper develops a fully modified estimator which follows a mixture normal distribution asymptotically. Moreover, a test statistic for the linearity and its asymptotic distribution are also derived. Monte Carlo results show that the proposed test has good finite sample performance