scholarly journals Measurement and Characteristic Analysis of Electromagnetic Disturbance of Coal Mine Fan Converter

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhonghua Xin ◽  
Xiaodong Zhang ◽  
Shuqian Yu ◽  
Huijuan Wang ◽  
Xiaoqian Jia ◽  
...  
Keyword(s):  
Coal Mine ◽  
Electromagnetic Interference ◽  
Frequency Converter ◽  
Dominant Frequency ◽  
Small Sample ◽  
Normal Operation ◽  
Electromagnetic Disturbance ◽  
Characteristic Parameters ◽  
Characteristic Analysis ◽  
Sample Data

With the extensive use of converters in high-power ventilator, shearer, road header, conveyor, hoist, and other coal mine equipment, the electromagnetic disturbance problem of the converter is worthy of attention. In this paper, the field measurement and statistical method are used to analyze the radiation interference characteristics of frequency converter. Then, a typical fan converter is taken as an example, and the electromagnetic disturbance of the converter is tested in four key positions and two working frequencies, respectively. Multiple sets of data and spectrum are obtained by using a spectrum analyzer and other instruments, and the dominant frequency characteristic parameters of the converter are analyzed emphatically. The small sample data adopts the Shapiro–Wilk test, and the 80%/80% rule was used for statistical analysis. Finally, we got five common frequencies of electromagnetic interference generated by the converter (electric field dBμV/m). The CH4 sensor and other sensors work near these five dominant frequencies, which may affect the normal operation of the sensor and cause alarm. The test and analysis method proposed in this paper can be used to obtain the characteristic parameters of the converter electromagnetic disturbance, which can be used as a reference for the design of the immunity of sensors or control instruments.

scholarly journals Direct Ink Writing of Highly Conductive MXene Frames for Tunable Electromagnetic Interference Shielding and Electromagnetic Wave-Induced Thermochromism

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Xinyu Wu ◽  
Tingxiang Tu ◽  
Yang Dai ◽  
Pingping Tang ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Electromagnetic Wave ◽  
Electromagnetic Interference ◽  
High Performance ◽  
Normal Operation ◽  
List Type ◽  
Emi Shielding ◽  
Electromagnetic Interference Shielding ◽  
Direct Ink Writing ◽  
Aluminum Ions ◽  
Wave Induced

Highlights 3D printing of MXene frames with tunable electromagnetic interference shielding efficiency is demonstrated. Highly conductive MXene frames are reinforced by cross-linking with aluminum ions. Electromagnetic wave is visualized by electromagnetic-thermochromic MXene patterns. Abstract The highly integrated and miniaturized next-generation electronic products call for high-performance electromagnetic interference (EMI) shielding materials to assure the normal operation of their closely assembled components. However, the most current techniques are not adequate for the fabrication of shielding materials with programmable structure and controllable shielding efficiency. Herein, we demonstrate the direct ink writing of robust and highly conductive Ti3C2Tx MXene frames with customizable structures by using MXene/AlOOH inks for tunable EMI shielding and electromagnetic wave-induced thermochromism applications. The as-printed frames are reinforced by immersing in AlCl3/HCl solution to remove the electrically insulating AlOOH nanoparticles, as well as cross-link the MXene sheets and fuse the filament interfaces with aluminum ions. After freeze-drying, the resultant robust and porous MXene frames exhibit tunable EMI shielding efficiencies in the range of 25–80 dB with the highest electrical conductivity of 5323 S m−1. Furthermore, an electromagnetic wave-induced thermochromic MXene pattern is assembled by coating and curing with thermochromic polydimethylsiloxane on a printed MXene pattern, and its color can be changed from blue to red under the high-intensity electromagnetic irradiation. This work demonstrates a direct ink printing of customizable EMI frames and patterns for tuning EMI shielding efficiency and visualizing electromagnetic waves.

scholarly journals A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

2021 ◽  
Vol 13 (23) ◽  
pp. 4864
Author(s):  
Langfu Cui ◽  
Qingzhen Zhang ◽  
Liman Yang ◽  
Chenggang Bai
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Prediction Method ◽  
Sliding Window ◽  
Small Sample ◽  
Grey Theory ◽  
Sensing System ◽  
Performance Change ◽  
Sample Data ◽  
Platform System

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

scholarly journals Abnormal Detection to Big Data Using Deep Neural Networks

2021 ◽  
Vol 2068 (1) ◽  
pp. 012025
Author(s):  
Jian Zheng ◽  
Zhaoni Li ◽  
Jiang Li ◽  
Hongling Liu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Risk Model ◽  
Small Sample ◽  
Small Samples ◽  
Small Data ◽  
Data Segmentation ◽  
Sample Data ◽  
Segmentation Algorithms ◽  
The Common

Abstract It is difficult to detect the anomalies in big data using traditional methods due to big data has the characteristics of mass and disorder. For the common methods, they divide big data into several small samples, then analyze these divided small samples. However, this manner increases the complexity of segmentation algorithms, moreover, it is difficult to control the risk of data segmentation. To address this, here proposes a neural network approch based on Vapnik risk model. Firstly, the sample data is randomly divided into small data blocks. Then, a neural network learns these divided small sample data blocks. To reduce the risks in the process of data segmentation, the Vapnik risk model is used to supervise data segmentation. Finally, the proposed method is verify on the historical electricity price data of Mountain View, California. The results show that our method is effectiveness.

Variation analysis driven by small-sample data for compliant aero-structure assembly

2019 ◽  
Vol 39 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Biao Mei ◽  
Weidong Zhu ◽  
Yinglin Ke ◽  
Pengyu Zheng
Keyword(s):  
Probability Distributions ◽  
Small Sample ◽  
Accurate Estimation ◽  
Production Volume ◽  
Analysis Method ◽  
Variation Analysis ◽  
Content Type ◽  
Aircraft Manufacturing ◽  
Sample Data ◽  
Structure Assembly

Purpose Assembly variation analysis generally demands probability distributions of variation sources. However, due to small production volume in aircraft manufacturing, especially prototype manufacturing, the probability distributions are hard to obtain, and only the small-sample data of variation sources can be consulted. Thus, this paper aims to propose a variation analysis method driven by small-sample data for compliant aero-structure assembly. Design/methodology/approach First, a hybrid assembly variation model, integrating rigid effects with flexibility, is constructed based on the homogeneous transformation and elasticity mechanics. Then, the bootstrap approach is introduced to estimate a variation source based on small-sample data. The influences of bootstrap parameters on the estimation accuracy are analyzed to select suitable parameters for acceptable estimation performance. Finally, the process of assembly variation analysis driven by small-sample data is demonstrated. Findings A variation analysis method driven by small-sample data, considering both rigid effects and flexibility, is proposed for aero-structure assembly. The method provides a good complement to traditional variation analysis methods based on probability distributions of variation sources. Practical implications With the proposed method, even if probability distribution information of variation sources cannot be obtained, accurate estimation of the assembly variation could be achieved. The method is well suited for aircraft assembly, especially in the stage of prototype manufacturing. Originality/value A variation analysis method driven by small-sample data is proposed for aero-structure assembly, which can be extended to deal with other similar applications.

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