A combined generalized Warblet transform and second order synchroextracting transform for analyzing nonstationary signals of rotating machinery

In recent years, considerable attention has been paid in time–frequency analysis (TFA) methods, which is an effective technology in processing the vibration signal of rotating machinery. However, TFA techniques are not sufficient to handle signals having a strong non-stationary characteristic. To overcome this drawback, taking short-time Fourier transform as a link, a TFA methods that using the generalized Warblet transform (GWT) in combination with the second order synchroextracting transform (SSET) is proposed in this study. Firstly, based on the GWT and SSET theories, this paper proposes a method combining the two TFA methods to improve the TFA concentration, named GWT–SSET. Secondly, the method is verified numerically with single-component and multi-component signals, respectively. Quantized indicators, Rényi entropy and mean relative error (MRE) are used to analyze the concentration of TFA and accuracy of instantly frequency (IF) estimation, respectively. Finally, the proposed method is applied to analyze nonstationary signals in variable speed. The numerical and experimental results illustrate the effectiveness of the GWT–SSET method.

Entropy Analysis of High-Definition Transcranial Electric Stimulation Effects on EEG Dynamics

A foundation of medical research is time series analysis—the behavior of variables of interest with respect to time. Time series data are often analyzed using the mean, with statistical tests applied to mean differences, and has the assumption that data are stationary. Although widely practiced, this method has limitations. Here we present an alternative statistical approach with sample analysis that provides a summary statistic accounting for the non-stationary nature of time series data. This work discusses the use of entropy as a measurement of the complexity of time series, in the context of Neuroscience, due to the non-stationary characteristic of the data. To elucidate our argument, we conducted entropy analysis on a sample of electroencephalographic (EEG) data from an interventional study using non-invasive electrical brain stimulation. We demonstrated that entropy analysis could identify intervention-related change in EEG data, supporting that entropy can be a useful “summary” statistic in non-linear dynamical systems.

Bistatic Forward-Looking SAR Moving Target Detection Method Based on Joint Clutter Cancellation in Echo-Image Domain with Three Receiving Channels

In bistatic forward-looking synthetic aperture radar (BFSAR) ground moving target detection (GMTD), the suppression of the strong and heterogeneous ground clutter is one of the most crucial and challenging issues. Due to the bistatic forward-looking mode and long observation time, Doppler ambiguity, range and Doppler cells migration and non-stationary characteristics will exist in clutter receives, which leads to severe performance degradation of the traditional method. Hence, this paper proposes a GMTD method based on joint clutter cancellation in echo-image domain for BFSAR to achieve effective GMTD in heterogeneous BFSAR clutter. First, the pre-filtering and keystone transform are applied to suppress Doppler ambiguity and correct range cell migration, respectively. Then, time-division space-time adaptive clutter cancellation is adopted to suppress clutter at the first time in the echo domain, which can eliminate the effect of the migration of Doppler cells. However, its performance will be severely degraded due to the strong non-stationary characteristic of BFSAR clutter. Finally, adaptive displaced phase center antenna is exploited to suppress the residual non-stationary BFSAR clutter in image domain. Experimental results have shown that the strong non-stationary clutter of BFSAR has been sufficiently suppressed by the proposed method and the SCNR provided is enough to detect a moving target well.

Study on Feature Extraction Method for Diesel Engine Valve Faults

Valve faults diagnosis technique of a diesel engine is studied deeply in this paper. The experiment of valve clearance and air leakage faults are done in a diesel engine, and cylinder head vibration and transient speed signals are measured synchronously on normal and fault conditions respectively. These signals are used to feature extraction. In order to avoid the leakage and aliasing of vibration signal’s frequent spectrum, resample method based on order tracking is proposed, and vibration signal was transformed from time domain to crank angle domain accurately. Considering the non-stationary characteristic of vibration signal, a series of intrinsic mode functions with different scales were obtained using the empirical mode decomposition method, and fault features parameters were extracted through 3D Hilbert spectrums of the intrinsic mode functions. Experimental results show that the method can effectively extract fault features of diesel engine and use them to realize the valve system faults diagnosis further.

Pedestal looseness fault diagnosis in a rotating machine based on variational mode decomposition

According to the non-stationary characteristic of rotating machinery vibration signals of a rotor system with a loose pedestal fault, variational mode decomposition was applied in the pedestal looseness fault diagnosis for such a rotor system. Variational mode decomposition is used to decompose the rotor vibration signal into several stable components. This can achieve the separation of the pedestal looseness fault signal from the background signals, and extract the fault characteristic of a vibration signal from a rotor system with pedestal looseness. Experimental data from a rotor system with pedestal looseness were used to verify the proposed method. The results showed that the stable components of the rotor vibration signal obtained by variational mode decomposition have obvious amplitude modulation characteristics. The components which contain fault information were analyzed by envelope demodulation, which can extract the pedestal looseness fault features of a rotor vibration signal. Therefore, the variational mode decomposition method can be effectively applied to the pedestal looseness fault diagnosis of such a rotor system.

Relationship between Inflation and Stock Returns – Evidence from BRICS markets using Panel Co integration Test

Stocks are generally considered to be a good hedge against inflation because of their tendency to move together. This paper examines long term relationship between inflation and stock returns in BRICS markets using panel data for the period from March 2000 to September 2013. Correlation results reveal a significant negative relationship between stock index and inflation rate for Russia and a significantly positive relationship for India & China. ADF, PP and KPSS unit root tests indicate non-stationary characteristic of the data. Further we find no long term co-integrating relationship between stock index values and inflation rates using Pedroni panel co integration test. These findings have important implications for policy makers, regulators and investment community at large. There may seem to be short term contemporaneous relationship between inflation and equity returns but in the long run they do not seem to be significantly integrated. Changes in inflation may bring some short run movement in stock return but certainly equity does not seem to be a good hedge against inflation in long run at least in emerging BRICS markets. Keywords: BRICS, Stock Index, Inflation, Unit root test, Pedroni Panel Co integration Test, Johansen Co integration Test.

Research on Feature Extraction for Ultrasonic Echo Signal Based on EEMD Approach

The bonding quality of composite materials have a critical influence on the quality of the product in modern industry, while the current technology can only make judgments on bonding and de-bonding instead of quantitative evaluation of different de-bonding degrees. We present HHT method to extract features of echo signals used for quantitative recognition of bonding quality of thin plates. For the non-stationary characteristic of the ultrasonic echo signal, empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) are put forward to decompose the signal and calculate its energy torque. The HHT method highlights the time-frequency performance of echo signals effectively. The simulated signals verify that EEMD has more excellent decomposition performance than EMD, that is, EEMD diminishes the mode mixing to some extent generated from EMD decomposition.

The Application of Adaptive Kalman Filter Technology in Aspheric Surface Machining and Detection

The grinding machining parameters of large-caliber aspherics ultra-precision grinding processing depend on the analysis of online profile accuracy detection data .and the detection data has the second order of non-stationary characteristic . The research of second order of non-stationary data processing technique and isolate and reduce the random error of online detection data is key technology of ensure the precision of aspheric surface grinding accuracy . This paper proposed a parallel recursive identification of improved adaptive kalman filtering method , the simulation analysis and φ500mm aspheric surface element grinding experiment proved this method can real-time process second order of non-stationary data of online detection data , isolate and reduce the random error , make a best estimate of time-varying signal, provide correction parameter of grinding process , processed aspherical face proflie accuracy≤4μm.

An Ensemble of Ground Motions Simulation Based on Discrete Stationary Wavelet Transform

Simulation of earthquake ground motion was a hot topic for structure seismic response analysis. According to the problems in simulating ground motion history with harmony superposition method, such as more interference of human factors and simulated ground motion history didn’t have frequency non-stationary characteristic, a novel method of ground motion simulation based on stationary discrete wavelet transform was presented. Using stationary discrete wavelet transform, the parent ground motion history was decomposed into different frequency bands, and the coefficients were modified. Using inverse stationary discrete wavelet transform, an ensemble of ground motions were simulated whose statistics closely resemble those of the parent history. Through a numerical example, the statistic characteristics of simulated histories were compared with the original values, and the feasibility and correctness of presented method was illustrated.