Adaptive empirical Fourier decomposition (AEFD) is a recently developed approach of nonstationary signal mode separation. However, it requires to set the spectrum segmentation boundary relying on the users' professional experience ahead of time. In this paper, a novel spectral envelope-based adaptive empirical Fourier decomposition (SEAEFD) method is proposed to improve the performance of AEFD for rolling bearing vibration signal analysis. In the proposed SEAEFD approach, fast Fourier transform (FFT) of the raw signal is calculated to obtain the frequency spectrum at first. Then, the spectral envelope processing is implemented on the spectrum signal obtained by FFT to achieve an adaptive segmentation. In the traditional segmentation method, generally, the minima and midpoints between adjacent extreme points are taken as the spectrum segmentation boundary, in which the obtained frequency band contains more interference components. To achieve the effect of denoising and restrain the noise that existed in the collected vibration signal, SEAEFD is proposed to optimize the spectrum segmentation boundary so that the obtained frequency band contains the least noise components. Lastly, the inverse FFT is used to reconstruct the component signal within each frequency band and the gained signals are termed as Fourier intrinsic mode functions (FIMFs). Therefore, SEAEFD enables a nonstationary signal to be decomposed into several single-component signals with instantaneous frequencies of physical significance. The proposed SEAEFD method is compared with recently developed methods, including EAEFD, AEFD, EWT, VMD and EMD methods, by analyzing the simulation signals and the measured data of rolling bearing. The results indicate that SEAEFD is valid in diagnosing rolling bearing faults and gets a better diagnosis performance than the compared methods. 

Spectral envelope-based adaptive empirical Fourier decomposition method and its application to rolling bearing fault diagnosis.

Research on regional differences of China's new energy vehicles promotion policies: A perspective of sales volume forecasting

As a clean energy source, the role of wind power in the energy mix is becoming increasingly important. Reliable and high-quality wind speed prediction results are key to wind energy utilization. The traditional point prediction method cannot effectively analyze the uncertainty of wind speed, and the interval prediction model can provide the possible variation range of wind speed under a certain confidence probability and supply more uncertain information to decision makers. However, the previous interval prediction models generally ignore the random characteristics of capturing wind speed and the importance of objective selection of prediction submodels, leading to poor prediction results. To address these problems, a combined model based on data preprocessing, multi-neural network models, multi-objective optimization, and an improved interval prediction method is proposed. The model is applied to five wind speed forecasting examples in Dalian to test the prediction accuracy, multi-step prediction ability, and universality and generalization ability of the model. The experimental results show that the model proposed in this study is satisfactory for various performance evaluation indexes, has high stability and accuracy, and all the solutions obtained by the model are Pareto optimal solutions. Thus, it provides a reliable reference for the effective utilization of wind energy.

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A Novel Wind Speed Interval Prediction System Based on Neural Network and Multi-objective Grasshopper Optimization

Short- and long-term streamflow forecasting using wavelet neural networks for complex watersheds: A case study in the Mahanadi River, India

This article proposes an approach using surface acoustic wave (SAW) device to decrease passband ripples and sidelobes as two key problems of wavelet transform processors (WTPs). The passband ripples are primarily generated through electrode reflections and transducer end-effects. The proposed method uses unbalanced split-electrode interdigital transducers (IDTs) to prevent electrode reflections. The problem of transducer end-effects is alleviated by attaching SAW absorbers to both ends of the substrate edge, which can effectively absorb the spurious SAW arriving at the edges of the piezoelectric substrate. Moreover, because the radiation energy of bulk acoustic wave (BAW) has a sidelobe distribution, the greater sidelobes are predominantly caused by the BAW radiation. This problem is alleviated by engraving bidirectional slots on the substrate at the back of the WTPs of the unbalanced split-electrode IDTs to suppress the BAW radiation. The experimental results demonstrate that the passband ripples and sidelobes of the WTPs decrease to 0.72 and 6.58 dB, respectively. 

D. Qiao

Papers

Simulating nonstationary non-Gaussian vector process based on continuous wavelet transform