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31.
We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models. 相似文献
32.
Yao Dajie Liu Xiaofei Wu Jian 《International Journal of Control, Automation and Systems》2020,18(9):2251-2258
International Journal of Control, Automation and Systems - In this paper, the tracking problem for a class of uncertain nonlinearly parameterized systems with input delay based the adaptive... 相似文献
33.
34.
This paper summarizes the basics of pulsed thermal nondestructive testing (TNDT) including theoretical solutions, data processing algorithms and practical implementation. Typical defects are discussed along with 1D analytical and multi-dimensional numerical solutions. Special emphasis is focused on defect characterization by the use of inverse solutions. A list of TNDT terms is provided. Applications of active TNDT, mainly in the aerospace industry, are discussed briefly, and some trends in the further development of this technique are described. 相似文献
35.
Cold filamentary microplasma (CFM) pretreatment assisted by thermionic emission (TE) is a promising, cold drying technology for agricultural products. Such novel pretreatment method based on local electroporation process in the air gap. Brief theory and treatment mechanism are explained. A fractional factorial design 33 was used to determine the effect of CFM treatment assisted by TE parameters such as pulse frequency (F = 40, 80 and 120 Hz), pretreatment duration (D = 60, 120 and 180 s) and temperature of thermionic source (T = 700, 850 and 1000 °C) on drying time and drying rate of potato samples. Maximal CFM pretreatment effect was observed at frequency of 80 Hz, treatment duration of 120 s and temperature of thermionic source of 1000 °C, and the corresponding drying rate and drying time were 35·10−5 (kg/kg s−1) and 130 min. The use of CFM treatment had minor effects on the main quality characteristics (total sugar and starch content) of potato samples. Fourier-transformed infrared spectroscopy confirmed the absence of any modifications of functional group composition of potato samples after CFM pretreatment. Results indicate that cold pulsed filamentary microplasma can enhance the drying rate and decrease drying time without changes in quality characteristics.Practical applicationsCold filamentary microplasma pretreatment assisted by thermionic emission can improve the efficiency of potato drying. Furthermore, it has potential application for reducing energy consumption in drying. CFM pretreatment could be potentially applied for agricultural products drying. 相似文献
36.
In this paper, we proposed a robust discrete-time controller. This control system, which is derived from the idea of the normalized plant, does not include plant parameters. Thus, we obtain a control system independent of plant parameters and that has the same structure as a conventional optimal servo control system. Simulation and experimental results show that the proposed method is fairly robust to plant parameter variations and external disturbances. 相似文献
37.
In this paper we provide a convergence analysis of the alternating RGLS (Recursive Generalized Least Square) algorithm used for the identification of the reduced complexity Volterra model describing stochastic non-linear systems. The reduced Volterra model used is the 3rd order SVD-PARAFC-Volterra model provided using the Singular Value Decomposition (SVD) and the Parallel Factor (PARAFAC) tensor decomposition of the quadratic and the cubic kernels respectively of the classical Volterra model. The Alternating RGLS (ARGLS) algorithm consists on the execution of the classical RGLS algorithm in alternating way. The ARGLS convergence was proved using the Ordinary Differential Equation (ODE) method. It is noted that the algorithm convergence canno׳t be ensured when the disturbance acting on the system to be identified has specific features. The ARGLS algorithm is tested in simulations on a numerical example by satisfying the determined convergence conditions. To raise the elegies of the proposed algorithm, we proceed to its comparison with the classical Alternating Recursive Least Squares (ARLS) presented in the literature. The comparison has been built on a non-linear satellite channel and a benchmark system CSTR (Continuous Stirred Tank Reactor). Moreover the efficiency of the proposed identification approach is proved on an experimental Communicating Two Tank system (CTTS). 相似文献
38.
39.
Xu Zhi-Zheng Jiang Feng Zhong Chong-Quan Gou Yong-Jie Teng Hong-Fei 《Structural and Multidisciplinary Optimization》2020,61(1):207-223
Structural and Multidisciplinary Optimization - The overall layout optimization design of an orbital propellant depot involves the optimization of shape, size, and positions of propellant tanks in... 相似文献
40.
Single image super resolution (SISR) is an important research content in the
field of computer vision and image processing. With the rapid development of deep
neural networks, different image super-resolution models have emerged. Compared to
some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods
using traditional convolutional neural networks, SISR based on generative adversarial
networks (GAN) has achieved the most advanced visual performance. In this review, we
first explore the challenges faced by SISR and introduce some common datasets and
evaluation metrics. Then, we review the improved network structures and loss functions
of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of
different networks are analyzed by multiple comparative experiments. Finally, we
summarize the paper and look forward to the future development trends of GAN-based
perceptual SISR. 相似文献