Weak Fault Feature Extraction of Rolling Bearing Based on SVMD and Improved MOMEDA

In order to solve the problem that it is very difficult to extract fault features directly from the weak impact component of early fault signal of rolling bearing, a method combining continuous variational mode decomposition (SVMD) with modified MOMEDA based on Teager energy operator is proposed. Firstly, the low resonance impulse component in the fault signal is separated from the harmonic component and noise by SVMD, and then the Teager energy operator is used to enhance the impulse feature in the low resonance component to ensure that the accurate fault period is selected by the MOMOEDA algorithm. After further noise reduction by MOMEDA, the envelope spectrum of the signal is analyzed, and finally the fault location is determined. The results of simulation and experimental data show that this method can accurately and effectively extract the characteristic frequency of rolling bearing weak fault.

Combining Evidence from Auditory, Instantaneous Frequency and Random Forest for Anti-Noise Speech Recognition

Due to the shortcomings of acoustic feature parameters in speech signals, and the limitations of existing acoustic features in characterizing the integrity of the speech information, This paper proposes a method for speech recognition combining cochlear feature and random forest. Environmental noise can pose a threat to the stable operation of current speech recognition systems. It is therefore essential to develop robust systems that are able to identify speech under low signal-to-noise ratio. In this paper, we propose a method of speech recognition combining spectral subtraction, auditory and energy features extraction. This method first extract novel auditory features based on cochlear filter cepstral coefficients (CFCC) and instantaneous frequency (IF), i.e., CFCCIF. Spectral subtraction is then introduced into the front end of feature extraction, and the extracted feature is called enhanced auditory features (EAF). An energy feature Teager energy operator (TEO) is also extracted, the combination of them is known as a fusion feature. Linear discriminate analysis (LDA) is then applied to feature selection and optimization of the fusion feature. Finally, random forest (RF) is used as the classifier in a non-specific persons, isolated words, and small-vocabulary speech recognition system. On the Korean isolated words database, the proposed features (i.e., EAF) after fusion with Teager energy features have shown strong robustness in the nosiy situation. Our experiments show that the optimization feature achieved in a speech recognition task display a high recognition rate and excellent anti-noise performance.

Electromagnetic Reflection Field Detection Based on TEO

Active electromagnetic detection explores targets using electromagnetic reflection field of metal objects. In view of the detection of active electromagnetic by detecting target reflection field, a Teager energy operator(TEO) is adopted to improve the electromagnetic reflection amplitude and instantaneous frequency detection method, the method of theoretical description and simulation verification has also carried out in this paper. Results show that this method is able to track the change of the single frequency signal of electromagnetic reflection field with good effect. It is also able to provide full details of the judgment to confirm the target. This method is simple and convenient for real-time processing, and has good application value.

Non-invasive Detection of Ketum Users through Objective Analysis of EEG Signals

Ketum leaves are traditionaly used for treatment of backpain and reduce fatigue. However, in recent years people use ketum leaves to substitute traditional drugs as they can easily be obtained at a low cost. Currently, a robust test for ketum detection is not available. Although ketum usage detection via test strip is available, however, the method is possible to be polluted by other substances and can be manipulated. Brain signals have unique characteristics and are well-known as a robust method for recognition and disease detection. Thus, this study has been done to distinguish between ketum users and non-users via brain signal characteristics. Eight participants were chosen, four of whom are heavy ketum users and four non-users with no health issues. Data were collected using the eegoSports device in relaxed state. In pre-processing, notch filter and Independent Component Analysis (ICA) were used to remove artifacts. Wavelet Packet Transform (WPT) was used to reduce the large data dimension and extract features from the brain signal. To select the most significant features, T-Test was used. Support Vector Machine (SVM), K-Nearest Neighbour, and Ensemble classifier were used to categorize the input data into ketum users and non-users. Ensemble classifier was found to be able to predict the testing instances with 100% accuracy for open and closed eyes task with Teager energy and energy to standard deviation ratio as the features.

Complete Teager Energy Operator: A Novel Method for Enhancing Micro-impact Signal

The output of conventional Teager energy operator (TEO) is approximately equal to the square product of the instantaneous amplitude and the instantaneous frequency ( A 2 Ω 2 ). The original TEO can effectively enhance the transient shock components and suppress the non-impacting elements, and it also changes the frequency distribution of the original shock. In this paper, a complete Teager energy operator is proposed, and its expression is more exact than original method. By keeping the positive and negative distribution of the shock signal x ( t ), the fundamental frequency energy of the impulses can be effectively enhanced. The incipient fault characteristics of large-scale rotating machinery are typically micro shock pulse, extremely weak and mixed with heavy noise. Preprocessing the fault signal and enhancing the micro shock component are essential means to extract the early fault features. In the experiment part, the applicability of the proposed method is verified by the simulated micro impact signal, the common bearing fault data-sets and the practical measured data of the test bench.