Rolling Bearing Feature Frequency Extraction using Extreme Average Envelope Decomposition

The vibration signal contains a wealth of sensitive information which reflects the running status of the equipment. It is one of the most important steps for precise diagnosis to decompose the signal and extracts the effective information properly. The traditional classical adaptive signal decomposition method, such as EMD, exists the problems of mode mixing, low decomposition accuracy etc. Aiming at those problems, EAED（extreme average envelope decomposition） method is presented based on EMD. EAED method has three advantages. Firstly, it is completed through midpoint envelopment method rather than using maximum and minimum envelopment respectively as used in EMD. Therefore, the average variability of the signal can be described accurately. Secondly, in order to reduce the envelope errors during the signal decomposition, replacing two envelopes with one envelope strategy is presented. Thirdly, the similar triangle principle is utilized to calculate the time of extreme average points accurately. Thus, the influence of sampling frequency on the calculation results can be significantly reduced. Experimental results show that EAED could separate out single frequency components from a complex signal gradually. EAED could not only isolate three kinds of typical bearing fault characteristic of vibration frequency components but also has fewer decomposition layers. EAED replaces quadratic enveloping to an envelope which ensuring to isolate the fault characteristic frequency under the condition of less decomposition layers. Therefore, the precision of signal decomposition is improved.

Parametric adaptive time-frequency representation based on time-sheared Gabor atoms

A localized parametric time-sheared Gabor atom is derived by convolving a linear frequency modulated factor, modulating in frequency and translating in time to a dilated Gaussian function, which is the generalization of Gabor atom and is more delicate for matching most of the signals encountered in practice, especially for those having frequency dispersion characteristics. The time-frequency distribution of this atom concentrates in its time center and frequency center along energy curve, with the curve being oblique to a certain extent along the time axis. A novel parametric adaptive time-frequency distribution based on a set of the derived atoms is then proposed using a adaptive signal subspace decomposition method in frequency domain, which is non-negative time-frequency energy distribution and free of cross-term interference for multicomponent signals. The results of numerical simulation manifest the effectiveness of the approach in time-frequency representation and signal de-noising processing.

Unintentional modulation microstructure enlargement

Radio frequency fingerprinting(RFF)is a technology that identifies the specific emitter of a received electromagnetic signal by external measurement of the minuscule hardware-level,device-specific imperfections.The RFF-related information is mainly in the form of unintentional modulation(UIM),which is subtle enough to be effectively imperceptible and is submerged in the intentional modulation(IM).It is necessary to minimize the influence of the IM and expand the slight differences between emitters for successful RFF.This paper proposes a UIM microstructure enlargement(UMME)method based on feature-level adaptive signal decomposition(ASD),accompanied by autocorrelation and cross-correlation analysis.The common IM part is evaluated by analyzing a newly-defined benchmark feature.Three different indexes are used to quantify the similarity,distance,and dependency of the RFF features from different devices.Experiments are conducted based on the real-world signals transmitted from 20 of the same type of radar in the same working mode.The visual image qualitatively shows the magnification of feature differences;different indicators quantitatively describe the changes in features.Compared with the original RFF feature,recognition results based on the Gaussian mixture model(GMM)classifier further validate the effectiveness of the proposed algorithm.