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2018 Chinese Automation Congress, CAC 2018

作者： Li, Xin Xie, Guo Liu, Han Wang, Wenqing Information Processing Xi 'An University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Xi'an China

ISBN: (纸本)9781728113128

Modern industrial equipment plays a vital role in the operation of large industrial systems. Accurate prediction of its remaining useful life will greatly reduce the possibility of unsafe accidents. The internal structure of modern industrial equipment is very complex, and a variety of state monitoring data affect the remaining useful life of the equipment, and the mutual influence between different monitoring data has greatly increased the difficulty of remaining useful life prediction. In this paper, the long short-term memory network in deep learning is used to solve this problem. The long short-term memory network has many advantages in time series prediction. The remaining useful life prediction model of industrial equipment based on long short-term memory network and multivariable monitoring data is established, and aiming at the characteristics of multivariable monitoring data, Mini-batch Gradient Descent algorithm (MBGD) is adopted to train the network. Finally, the engine monitoring data set provided by the NASA prediction center is used to verify the algorithm. The data set is divided into training set and test set, the training set is put into the long short-term memory network to get the prediction model, and then the remaining useful life prediction is carried out by the test set. The prediction accuracy and generalization ability of the algorithm are verified by predicting the remaining useful life of three different turbofan engines. © 2018 IEEE.

关键词： Long short-term memory

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Data Based Fault Diagnosis of Hot Axle for High-Speed Train

Data Based Fault Diagnosis of Hot Axle for High-Speed Train

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2018 Chinese Automation Congress, CAC 2018

作者： Sun, Lanlan Hei, Xinhong Xie, Guo Qian, Fueai Wang, Zhuxin Liu, Ran System Control and Intelligent Information Processing University of Technology Xi'An Shaanxi Key Laboratory of Complex Xi'an China

ISBN: (纸本)9781728113128

The axle temperature is an important performance index for high-speed train axle, and many types of high-precision detection device have been developed. However, because the existing hotbox detection system works by comparing the measured value with fixed and experiential temperature thresholds, without considering the dynamic influence of running environment, the rates of fault and failure alarm are still high. Regarding this problem, this paper is to realize high precision fault diagnosis. Firstly, the characteristics of the temperature rising rates of all axles, the axles on the same side of a carriage, the axles on the same side of a whole train are analyzed. Secondly, the support vector machine (SVM) is employed to establish a data-based fault diagnosis model, and the model is optimized using genetic algorithm. Finally, the proposed method is applied to an actual running data, the analysis results demonstrated the effectiveness and feasibility. © 2018 IEEE.

关键词： Genetic algorithms

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Fault-Tolerant Cooperative Motion Planning of Connected and Automated Vehicles at a Signal-Free and Lane-Free Intersection

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IFAC-PapersOnLine 2018年第24期51卷 60-67页

作者： Li, Bai Zhang, Youmin Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Canada

Compared with a conventional intersection, a signal-free and lane-free intersection enables the vehicles to drive more flexibly, thereby to promote the throughput and to reduce congestions. This work proposes a fault-tolerant cooperative motion planning method for a number of connected and automated vehicles (CAVs) which are passing through a signal-free and lane-free intersection. Partial loss of actuator effectiveness is considered as the fault type. Even a subtle fault may significantly affect the overall team performance. Thus correctly judging whether each faulty/healthy CAV can still achieve its original goal is impossible until a corresponding motion replanning problem turns out to be solvable or not. To maximize the completeness of the original task, we propose a parallel computation framework, wherein the fault-recovery motion replanning problems in multiple levels of task completeness are tried simultaneously. Concretely, i) each faulty/healthy CAV should accomplish its original goal; ii) only each healthy CAV is required to accomplish its original goal; and iii) all the faulty/healthy CAVs try to stop safely without the need to achieve any original goals. Problems iii), ii), and i) are ranking with increasing computational difficulty. Among all the three problems, the achievable solution to the most difficult one is implemented as the fault-recovery strategy. © 2018

关键词： Motion planning Automation Fault tolerance Vehicles Automated vehicles Co operative control connected Intersection managements Optimal controls

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Measurement Method of the Least Mean Square Adaptive Filtering Parameters for Gas-solid Flow

Measurement Method of the Least Mean Square Adaptive Filteri...

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Chinese Automation Congress (CAC)

作者： A. Ruidong Xie B. Yingmin Yi C. Haichuan Yang Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

With regard to the unknown prior knowledge of signal noise in the parameter measurement of gas-solid flow, the present paper puts forward the AC signal processing method based on the least mean square (LMS) adaptive filtering. In addition, the floating processor DSP-TMS320F28335 is employed to collect AC signal of electrostatic sensors and the acquired signals are processed by the aid of LMS adaptive filtering algorithm which adjusts the filter weights according to the criterion of the least mean square error to achieve the optimal filtering. Drawing on the experimental platform, the solid-phase velocity is measured online in real time for the gas-solid flow. The results show that the method can improve the accuracy and stability for the velocity measurement of gas-solid flow.

关键词： Filtering Velocity measurement Filtering algorithms Adaptive filters Solids Atmospheric measurements Particle measurements

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A Forest Fire Detection Method Based on a Deep Learning Approach

A Forest Fire Detection Method Based on a Deep Learning Appr...

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第30届中国过程控制会议（CPCC 2019）

作者： Honggang Yu Zhentian Jiao Youmin Zhang Jing Xin Lingxia Mu Shangbin Jiao Han Liu Ding Liu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Department of Mechanical Industrial and Aerospace Engineering Concordia University

Forest fire detection has great practical significance in actual production and ***, many current solutions are still not reliable in practice and usually affected by many *** paper presents a forest fire detection method based on deep learning approach using aerial images taken by Unmanned Aerial Vehicles(UAVs).We train a full image fire classifier by exploiting YOLOv3, a latest fast and accurate real-time object detection algorithm using deep learning neural ***, the feature map of a certain size is obtained by feature extraction network, such as 13×*** the input image is divided into 13×13 grid ***, if the central coordinate of an object in the ground truth falls in a grid cell, the grid cell will predict the object, because each grid cell will predict a fixed number of bounding *** the largest bounding box with ground truth IOU(Intersection Over Union) is used to predict *** fire patch detector developed in this paper obtains 91% mAP(mean Average Precision) on the data sets used and the FPS(Frames Per Second) average can reach 80.

关键词： Forest fire detection YOLOv3 deep learning Unmanned Aerial Vehicle(UAV)

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Sensorless Speed control of PMSMs based on an Improved Particle Filter

Sensorless Speed Control of PMSMs based on an Improved Parti...

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International Conference on Electrical, control and Instrumentation Engineering (ICECIE)

作者： Hai-Peng Ren Zi-Yuan Nan Jie Li Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Xi'an University of Technology Xi'an China

ISBN: (纸本)9781728139401

Position sensors in permanent magnet synchronous motor (PMSM) are restrained in some applications because of the space restriction and the reliability of the system. A marginalized particle filter (MPF) can used to staminate the speed of the motor by combining a Kalman filter (KF) with a particle filter (PF). In the MPF algorithm, the rotor position of the permanent magnet synchronous motor (PMSM) is represented by a set of particles, and the rotor speed associated with each particle is estimated by using the KF. The PF here is used to handle the non-Gaussianity and nonlinearity of the system. In this paper, the uniform distribution of particles is proposed to replace the traditional Gaussian distribution of particles in the PF to get better performance at the low-speed range. The motor drive system prototype is built using a TMS320F28335 digital signal processor as a controller core. The proposed improved PF is used in the sensorless speed vector control PMSM system. The experimental results show that the proposed PF with a uniform distribution of initial particles enables more accurate speed estimation in the low-speed range compared to the conventional Gaussian distribution, while increasing number of particles also helps to improve the accuracy.

关键词： particle filter sensorless motor control initial distribution Particle filters Permanent magnet synchronous machines Sensors Gaussian distribution PARTICLE DISTRIBUTION Uniform distribution position sensor particles Speed Estimation

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Support Vector Regression Modeling based Data-Driven Iterative Learning control for Czochralski Crystal Growth Process

Support Vector Regression Modeling based Data-Driven Iterati...

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第三十八届中国控制会议

作者： Junchao Ren Ding Liu Yin Wan Ni Zhang National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

In view of the previous Czochralski(Cz) monocrystalline silicon growth control method based on mechanism model, it is difficult to accurately reflect the nonlinear and real-time variation characteristics of crystal growth process. This paper proposes a data-driven iterative learning control method based on support vector regression, which combines crystal diameter prediction model by support vector regression(SVR) off-line training with crystal growth mechanism model, and then control the crystal diameter and output key variable information in the framework of iterative learning algorithm. Simulation results for diameter control of crystal growth process and the analysis of key variable information show that the proposed method is effective and has a fast crystal diameter learning speed, and good performances of setpoint tracking and disturbance rejection, high crystal growth speed.

关键词： Data-Driven Czochralski Monocrystalline Silicon Support Vector Regression Iterative Learning control

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Diameter Detection of Silicon Single Crystal Based on Brain Storm Optimization Algorithm Hough Transform

Diameter Detection of Silicon Single Crystal Based on Brain ...

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第三十八届中国控制会议

作者： Lu Kang Ding Liu National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

In order to improve the speed and accuracy of silicon single crystal diameter detection, this paper proposes an ellipse detection method based on brain storm optimization algorithm Hough transform, this method uses brain storm optimization algorithm to search ellipse parameters that make the accumulator obtains the maximum value after using Hough transform, then the accurate measurement value of silicon single crystal diameter is obtained. The proposed algorithm is simulated on the complete ellipse with Gaussian white noise and silicon single crystal diameter detection engineering experiments, and the results are compared with other intelligent optimization algorithms. Experimental results show that the proposed algorithm can effectively improve the speed and accuracy of ellipse detection with Hough transform, thereby verify the feasibility and effectiveness of the proposed algorithm, and improve the speed and accuracy of silicon single crystal diameter detection.

关键词： Silicon single crystal Diameter detection Ellipse detection Brain storm optimization algorithm Hough transform

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Research on Algorithm for Model Predictive control Based on Robust Optimizition

Research on Algorithm for Model Predictive Control Based on ...

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第三十八届中国控制会议

作者： Xia Zhang Ding Liu Fei Yu National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xian University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

The practicality of model predictive control(MPC) is partially limited by its ability to solve optimization problems in real time. The robust model prediction algorithm based on linear matrix inequality is divided into online and offline parts. In order to improve the response speed and control precision of the system, the upper bound of the objective function obtained by the off-line algorithm is taken as a known quantity, and a series of large asymptotically stable invariant elliptic sets are obtained by re-optimization. The state variables can be determined-lying between two ellipsoid sets within the ellipsoid set sequence, on the basis of online measured state variables. By adding an adjacent small ellipse set, the state variables are precisely located by three ellipse sets optimization, and the system control quantity is obtained. The simulation results show that this algorithm is better than the traditional algorithm. Finally, this algorithm is applied to identify parameters of silicon single crystal thermal field temperature model and the simulation results prove its excellent ability of parameters identification.

关键词： Robust optimization Model predictive control Uncertainty Online optimization

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Multi-carrier Differential Chaos Shift keying Wireless Communication Based On Chaotic Shape-forming Filter

Multi-carrier Differential Chaos Shift Keying Wireless Commu...

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Chinese Automation Congress (CAC)

作者： Hai-Peng Ren Si-Long Guo Chao Bai Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China School of Mechatronic Engineering Xi’an Technological University Xi’an China

In recent years, chaotic communication has shown advantages, such as used the simplest matched filter to make signal-to-noise ratio (SNR) maximization and improve the resistance to multi-path interference. The chaotic shape-forming filter (CSF) has been proposed to generate the chaotic baseband signal in the wireless communication, the additional advantage of the chaotic shape-forming filter is able to generate the chaotic signal as spread sequence in Differential Chaotic Shift key (DCSK), which encodes information in the sequence. In this paper, the chaotic signal generated by CSF is used in DCSK and combined with multi-carrier frequency to improve the bit transmission rate. Because of CSF used, it helps to improve both Bit Error Rate (BER) performance and provide an additional information flow in the spread sequence itself. Simulation results indicate that the proposed method is superior to other methods over the Addition White Gaussian Noise (AWGN) channel and wireless channel.

关键词： Chaotic communication Signal to noise ratio Wireless communication Simulation Demodulation Data communication

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