Application of Local Wave Decomposition in Seismic Signal Processing

Local wave decomposition (LWD) method plays an important role in seismic signal processing for its superiority in significantly revealing the frequency content of a seismic signal changes with time variation. The LWD method is an effective way to decompose a seismic signal into several individual components. Each component represents a harmonic signal localized in time, with slowly varying amplitudes and frequencies, potentially highlighting different geologic and stratigraphic information. Empirical mode decomposition (EMD), the synchrosqueezing transform (SST), and variational mode decomposition (VMD) are three typical LWD methods. We mainly study the application of the LWD method especially EMD, SST, and VMD in seismic signal processing including seismic signal de‐noising, edge detection of seismic images, and recovery of the target reflection near coal seams

WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis

Convolutional neural network (CNN), with ability of feature learning and nonlinear mapping, has demonstrated its effectiveness in prognostics and health management (PHM). However, explanation on the physical meaning of a CNN architecture has rarely been studied. In this paper, a novel wavelet driven deep neural network termed as WaveletKernelNet (WKN) is presented, where a continuous wavelet convolutional (CWConv) layer is designed to replace the first convolutional layer of the standard CNN. This enables the first CWConv layer to discover more meaningful filters. Furthermore, only the scale parameter and translation parameter are directly learned from raw data at this CWConv layer. This provides a very effective way to obtain a customized filter bank, specifically tuned for extracting defect-related impact component embedded in the vibration signal. In addition, three experimental verification using data from laboratory environment are carried out to verify effectiveness of the proposed method for mechanical fault diagnosis. The results show the importance of the designed CWConv layer and the output of CWConv layer is interpretable. Besides, it is found that WKN has fewer parameters, higher fault classification accuracy and faster convergence speed than standard CNN

The future of Bitcoin: a Synchrosqueezing Wavelet Transform to predict search engine query trends Contributions to KDWEB Conference, a.d. 2016

Abstract. In recent years search engines have become the go-to methods for achieving many types of knowledge, spanning from detailed descriptions or general information interesting to the user. Likewise several reassignment techniques are capturing the attention of researchers in the field of signal analysis. Particularly, the Synchrosqueezing Wavelet Transform -SST allows signal decomposition and instantaneous frequency extrusion, at the same time promising consistent reconstruction capabilities, hence the possibility to contrive an SST assisted inference engine. We are going to test it using datasets extracted from search engine trends, using a cloud of keywords related to the Bitcoin topic. This could be useful to study the evolution of the cryptocurrency both in time and geographical terms, and to estimate the future number of queries. The importance of Bitcoin queries prediction goes beyond the academic and research environments and, as such, it could lead to valuable commercial applications, such as financial recommender systems or blockchain-based transaction managers development

Bearing fault diagnosis via kernel matrix construction based support vector machine

A novel approach on kernel matrix construction for support vector machine (SVM) is proposed to detect rolling element bearing fault efficiently. First, multi-scale coefficient matrix is achieved by processing vibration sample signal with continuous wavelet transform (CWT). Next, singular value decomposition (SVD) is applied to calculate eigenvector from wavelet coefficient matrix as sample signal feature vector. Two kernel matrices i.e. training kernel and predicting kernel, are then constructed in a novel way, which can reveal intrinsic similarity among samples and make it feasible to solve nonlinear classification problems in a high dimensional feature space. To validate its diagnosis performance, kernel matrix construction based SVM (KMCSVM) classifier is compared with three SVM classifiers i.e. classification tree kernel based SVM (CTKSVM), linear kernel based SVM (L-SVM) and radial basis function based SVM (RBFSVM), to identify different locations and severities of bearing fault. The experimental results indicate that KMCSVM has better classification capability than other methods

A Novel Approach for Ridge Detection and Mode Retrieval of Multicomponent Signals Based on STFT

Time-frequency analysis is often used to study non stationary multicomponent signals, which can be viewed as the surperimposition of modes, associated with ridges in the TF plane. To understand such signals, it is essential to identify their constituent modes. This is often done by performing ridge detection in the time-frequency plane which is then followed by mode retrieval. Unfortunately, existing ridge detectors are often not enough robust to noise therefore hampering mode retrieval. In this paper, we therefore develop a novel approach to ridge detection and mode retrieval based on the analysis of the short-time Fourier transform of multicomponent signals in the presence of noise, which will prove to be much more robust than state-of-the-art methods based on the same time-frequency representation

Direct Signal Separation Via Extraction of Local Frequencies with Adaptive Time-Varying Parameters

In nature, real-world phenomena that can be formulated as signals (or in terms of time series) are often affected by a number of factors and appear as multi-component modes. The natural approach to understand and process such phenomena is to decompose, or even better, to separate the multi-component signals to their basic building blocks (called sub-signals or time-series components, or fundamental modes). Recently the synchro-squeezing transform (SST) and its variants have been developed for nonstationary signal separation. More recently, a direct method of the time-frequency approach, called signal separation operation (SSO), was introduced for multi-component signal separation. While both SST and SSO are mathematically rigorous on the instantaneous frequency (IF) estimation, SSO avoids the second step of the two-step SST method in signal separation, which depends heavily on the accuracy of the estimated IFs. In the present paper, we solve the signal separation problem by constructing an adaptive signal separation operator (ASSO) for more effective separation of the blind-source multi-component signal, via introducing a time-varying parameter that adapts to local IFs. A recovery scheme is also proposed to extract the signal components one by one, and the time-varying parameter is updated for each component. The proposed method is suitable for engineering implementation, being capable of separating complicated signals into their sub-signals and reconstructing the signal trend directly. Numerical experiments on synthetic and real-world signals are presented to demonstrate our improvement over the previous attempts