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identification and compensation of systematic deviations particular to 5-axis machining centers

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE 2003年第8期43卷 771-780页

作者： Tsutsumi, M Saito, A Tokyo Univ Agr & Technol Grad Sch Bio Applicat & Syst Engn Tokyo 1848588 Japan

This paper presents an algorithm for identifying particular deviations such as angular deviations around linear axes relating to rotary axes in 5-axis machining centers. In this study, three kinds of simultaneous three-axis control motions are designed for each rotary axis to identify the deviations. In the measurement, two translational axes and one rotary axis are simultaneously controlled keeping the distance between a tool and a worktable constant. Telescoping ball bar is an effective instrument for measuring the relative displacement to the reference length in the work volume because its attitude is freely changed. In these three-axis control motions, the sensitive direction of the ball bar is kept constant. In order to determine the deviations, we derive eight equations from the relationship between the eccentricities obtained from the measured circular trajectories and the approximations derived from the mathematical model based on the simulation. In the simulation, a mathematical model considering the particular deviations is developed and then the characteristic diagrams are prepared for every deviation and every three-axis control motion. Based on the results, we propose a procedure for identifying the particular deviations in 5-axis machining centers and its procedure has been, applied to identify the deviations actually. From both the simulation and the experiment, it has been confirmed that the proposed method gives precision results and is able to apply to the measurement of 5-axis machining center which is a tilting rotary table type. (C) 2003 Elsevier Science Ltd. All rights reserved.

关键词： systematic deviation identification algorithm five axis machining center tilting rotary table angular deviation positional deviation

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Automatic identification method for driving risk status based on multi-sensor data

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PERSONAL AND UBIQUITOUS COMPUTING 2021年第3期27卷 1303-1319页

作者： Yan, Lixin Gong, Yike Chen, Zhijun Li, Zhenyun Guo, Junhua East China Jiaotong Univ Sch Transportat & Logist Nanchang Jiangxi Peoples R China Wuhan Univ Technol Intelligent Transport Syst Res Ctr Wuhan Peoples R China Minist Educ Engn Res Ctr Transportat Safety Wuhan Peoples R China

Real risk status detection is an effective way to reflect risky or dangerous driving behaviors and therefore to prevent road traffic accidents. However, a driver's risk status is not only difficult to define but also uncontrollable and uncertain. In this study, a simulated experiment with 30 drivers was conducted using a driving simulator to collect the multi-sensor data of road conditions, humans, and vehicles. The driving risk status was classified into three states (0 - incident, 1 - near crash, or 2 - crash) on the basis of the playback system of the driving simulator. The experimental data were pre-processed using the cubic spline interpolation method and the time-windows theory. A driving risk status identification model was established using the C5.0 decision tree algorithm, and the receiver operating characteristic curve (ROC) was adopted to evaluate the performance of the identification model. The results indicated that respiration (RESP), vehicle speed (SPE), SM_FATIGUE, distance to the left lane (LLD), course angle (CA), and skin conductivity (SC) had a significant correlation (p < 0.05) with the driving risk status. The identification accuracy of the C5.0 decision tree algorithm was 78%, and the areas under the ROC were 0.934, 0.77, and 0.845, respectively. Moreover, compared with other four identification algorithms, the algorithm performance evaluation indexes TPR (0.780), precision (0.753), recall (0.78), F-measure (0.756), and kappa (0.884) of the C5.0 decision tree were all the best. The conclusion can provide reference evidence for danger warning systems and intelligent vehicle design.

关键词： C5 0 decision tree identification algorithm Multi-sensors Risk status Traffic safety

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Mechanical parameter identification of two-mass drive system based on variable forgetting factor recursive least squares method

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2019年第2期41卷 494-503页

作者： Ke, Chen Wu, Ai Bing, Chen CRRC Times Elect Co Ltd Zhuzhou Hunan Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Hubei Peoples R China

Alternating current (AC) motor drive systems are widely used and their performances are greatly affected by motor parameters and load disturbances, so it is necessary to identify and observe their moment of inertia, the viscous friction coefficient and load torque. This paper presents an identification method combined a Luenberger observer and the variable forgetting factor recursive least squares method (FFRLSM), which does not require the torque sensor but a position encoder and can identify the moment of inertia of the drive system, the viscous friction coefficient, and the load torque, simultaneously. The Luenberger observer takes the position and the actual current to estimate speed and angular position. This information, together with variable FFRLSM is used to identify the moment of inertia, viscous friction coefficient and load torque. The identified moment of inertia of the system is substituted into the Luenberger observer model to correct the error of the system model. According to a probability density function of a normal distribution, the proposed method estimates the identification target in a short time, with stable and high identification precision. Finally, a comparison is made between the proposed algorithm and the FFRLSM. Results show that the proposed identification method achieves intended purpose and promise application values with its short consuming-time and high identification accuracy.

关键词： Moment of inertia viscous friction coefficient the load torque drive systems identification algorithm

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identification Problems of Linear Stationary Systems. Part II. Generalization of Prony's Method

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JOURNAL OF AUTOMATION AND INFORMATION SCIENCES 2011年第9期43卷 1-19页

作者： Larin, V. B. Apostolyuk, A. S. Natl Acad Sci Ukraine Inst Mech Dept SP Timoshenko Kiev Ukraine Natl Tech Univ Ukraine Kiev Polytech Inst Kiev Ukraine

The identification algorithms, where the intermediate model with discrete time is not used are described. The problem is considered both in case of regular (through the equidistant time intervals) registration and in a case of irregular one. Such approach is found to be effective also in the case of irregular sequence of registration intervals of measurement results. The efficiency of proposed algorithms is demonstrated by the examples. Particularly, it is shown that in some cases these algorithms application enables one to decrease by one or two orders of magnitude the requirements for the initial information registration accuracy.

关键词： linear stationary system identification algorithm regular and irregular registration registration accuracy

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An introduction to factor analysis for radio frequency interference detection on satellite observations

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METEOROLOGICAL APPLICATIONS 2015年第3期22卷 436-443页

作者： Islam, Tanvir Srivastava, Prashant K. Dai, Qiang Gupta, Manika Zhuo, Lu NOAA NESDIS Ctr Satellite Applicat & Res College Pk MD USA Colorado State Univ Cooperat Inst Res Atmosphere Ft Collins CO 80523 USA Univ Bristol Dept Civil Engn Bristol BS8 1TH Avon England NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA Univ Maryland Earth Syst Sci Interdisciplinary Ctr College Pk MD 20742 USA Indian Inst Technol Delhi Dept Civil Engn Delhi India

A novel radio frequency interference (RFI) detection method is introduced for satellite-borne passive microwave radiometer observations. This method is based on factor analysis, in which variability among observed and correlated variables is described in terms of factors. In the present study, this method is applied to the Tropical Rainfall Measuring Mission (TRMM)/TRMM Microwave Imager (TMI) and Aqua/Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) satellite measurements over the land surface to detect the RFI signals, respectively, in 10 and 6GHz channels. The RFI detection results are compared with other traditional methods, such as spectral difference method and principal component analysis (PCA) method. It has been found that the newly proposed method is able to detect RFI signals in the C- and X-band radiometer channels as effectively as the conventional PCA method.

关键词： radio frequency interference TRMM Microwave Imager Advanced Microwave Scanning Radiometer-Earth Observing System passive microwave radiometry land surface retrieval identification algorithm

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Approach based on combination of vector neural networks for emitter identification

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IET SIGNAL PROCESSING 2010年第2期4卷 137-148页

作者： Liu, H. -J. Liu, Z. Jiang, W. -L. Zhou, Y. -Y. Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China

To deal with the problem of emitter identification caused by the measurement uncertainty of emitter feature parameters, this study proposes a new identification algorithm based on combination of vector neural networks (CVNN), which is deduced from the backpropagation vector neural network and can realise the non-linear mapping between the interval-value input data and the interval-value output emitter types. The key idea of CVNN is to adopt a combination of multiple multi-input/single-output neural networks to construct an identification system;each of the networks can only realise the identification function between two emitter types. Through quantitative analysis, it can be concluded that the proposed algorithm requires less computational load in the training stage. A number of simulations are presented to demonstrate the identification capability of the CVNN algorithm for emitter signals with and without additive noise. Simulation results show that the proposed algorithm not only has better identification capability, but also is relatively more insensitive to noise.

关键词： radar signal processing Radar theory additive noise Signal processing and detection Neural computing techniques emitter signals radar computing CVNN Electrical engineering computing identification backpropagation vector neural network multiple multi input/single output neural networks neural nets Signal processing theory measurement uncertainty emitter identification backpropagation identification algorithm combination-of-vector neural networks nonlinear mapping

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A typical dynamic parameter identification method of 6-degree-of-freedom industrial robot

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2017年第9期231卷 740-752页

作者： Jiang, Surong Jiang, Meng Cao, Yanfei Hua, Daren Wu, Hongtao Ding, Yadong Chen, Bai Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Jiangsu Peoples R China

Accurate dynamic parameters are required in the dynamic model-based robot motion controller designing. Experiment-based robot parameter identification is the only way to obtain them effectively, in which motion parameters and actuator torques are measured and used to estimate the actual dynamic model. To simplify the identification procedure and improve the identification precision, a step-by-step identification method was proposed and investigated in this article with dynamic parameters of wrist and elbow joint estimated separately considering the difference in inertia parameters between wrist joint and elbow joint of typical no-load 6-DOF robots. The effectiveness, simplification and high precision compared with the traditional one-step identification method were demonstrated through comparative experiments.

关键词： Dynamic parameter identification 6-DOF joint serial robot identification algorithm stimulation trajectory

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FIR system identification under either-or communication with quantised inputs and quantised output observations

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IET CONTROL THEORY AND APPLICATIONS 2019年第8期13卷 1204-1210页

作者： Guo, Jin Diao, Jing-Dong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China

This study investigates the quantised-input FIR (finite impulse response) system identification under quantised and event-triggered output observations. For the binary-valued quantisation, they introduce a so-called either-or communication scheme to save communication resources. Based on the received data and the triggered indicator, an identification algorithm is proposed to estimate the unknown parameter by use of the empirical measure and weighted least squares. The convergence performance is established and the communication rate is derived. For the multi-threshold quantisation, it can be seen as a vector-valued observation in which each component represents a binary-valued output, and the corresponding identification problem is discussed. A numerical example is included to illustrate the main results obtained.

关键词： quantisation (signal) FIR filters parameter estimation least squares approximations quantised output observations quantised-input FIR system identification event-triggered output observations binary-valued quantisation communication resources triggered indicator identification algorithm communication rate multithreshold quantisation vector-valued observation binary-valued output identification problem quantised input observations finite impulse response system identification either-or communication scheme unknown parameter estimation weighted least squares empirical measure

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Series Arc Fault identification for Photovoltaic System Based on Time-Domain and Time-Frequency-Domain Analysis

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IEEE JOURNAL OF PHOTOVOLTAICS 2017年第4期7卷 1105-1114页

作者： Chen, Silei Li, Xingwen Xiong, Jiayu Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian 710049 Shaanxi Peoples R China

This paper aims at providing a reliable algorithm to identify photovoltaic (PV) series arc faults regardless of complex fault-like interferences. Through conducting various arc fault experiments with different PV current levels, arc gap lengths, and load types, PV series arc fault features have been understood comprehensively. To avoid unwanted nuisance tripping, fault-like conditions are analyzed to confirm the unique arc fault features. Based on the loop current signature, a greater unstable fluctuation in the time domain and extra arc noises in the time-frequency domain are chosen as identification features. By quantificational evaluations, optimal detection variables with the Hamming window and the proper time resolution have been established to achieve the best identification results. By building fusion coefficients, two variables are arithmetically fused to achieve the arc fault discovery. The algorithm could also classify fault-like into normal and adjust the threshold value dynamically to fit different normal current levels. Its validity has been verified by experimental results on the simulated platform.

关键词： Fault-like identification algorithm multiple detection variables optimal parameter selections photovoltaic (PV) series arc fault

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identification and experimental assessment of two-input Preisach model for coupling hysteresis in piezoelectric stack actuators

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SENSORS AND ACTUATORS A-PHYSICAL 2014年 220卷 92-100页

作者： Dong, Yangyang Hu, Hong Wang, Hongjun Harbin Inst Technol Shenzhen Grad Sch Shenzhen Peoples R China Chinese Acad Sci Ctr Space Automat Technol & Syst Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China

Piezoelectric stack actuators (PEAs) do not always perform as desired because external loads have an effect on the inclination of the hysteresis loop that causes deterioration of the tracking performance. To take account of this loading factor, the two-input Preisach model (TPM) is introduced to estimate the coupling hysteresis in PEAs for loading applications. This paper tackles the identification problem of TPM using a three-dimension interpolation algorithm based on the first-order reversal curves (FORCs) technique. To prove the feasibility of the TPM in describing piezoelectric hysteresis, the coupling hysteresis properties in PEA via experimental data are discussed. To assess the accuracy of the TPM in predicting expansion in a case where the PEA is subject to two inputs, it is compared with the single-input classical Preisach model (CPM) by performing several experiments under various excitation conditions. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Coupling hysteresis Piezoelectric stack actuator identification algorithm

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