Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

The complex working conditions and nonlinear characteristics of the motor drive control system of industrial robots make it difficult to detect faults.In this paper,a deep learning-based observer,which combines the convolutional neural network(CNN)and the long short-term memory network(LSTM),is employed to approximate the nonlinear driving control system.CNN layers are introduced to extract dynamic features of the data,whereas LSTM layers perform time-sequential prediction of the target system.In terms of application,normal samples are fed into the observer to build an offline prediction model for the target system.The trained CNN-LSTM-based observer is then deployed along with the target system to estimate the system outputs.Online fault detection can be realized by analyzing the residuals.Finally,an application of the proposed fault detection method to a brushless DC motor drive system is given to verify the effectiveness of the proposed scheme.Simulation results indicate the impressive fault detection capability of the presented method for driving control systems of industrial robots.

Vibration Diagnosis and Optimization of Industrial Robot Based on TPA and EMD Methods

This paper proposedmethod that combined transmission path analysis(TPA)and empirical mode decomposition(EMD)envelope analysis to solve the vibration problemof an industrial robot.Firstly,the deconvolution filter timedomain TPA method is proposed to trace the source along with the time variation.Secondly,the TPA method positioned themain source of robotic vibration under typically different working conditions.Thirdly,independent vibration testing of the Rotate Vector(RV)reducer is conducted under different loads and speeds,which are key components of an industrial robot.The method of EMD and Hilbert envelope was used to extract the fault feature of the RV reducer.Finally,the structural problems of the RV reducer were summarized.The vibration performance of industrial robots was improved through the RV reducer optimization.From the whole industrial robot to the local RV Reducer and then to the internal microstructure of the reducer,the source of defect information is traced accurately.Experimental results showed that the TPA and EMD hybrid methods were more accurate and efficient than traditional time-frequency analysis methods to solve industrial robot vibration problems.

An Overview of Calibration Technology of Industrial Robots

With the continuous improvement of automation,industrial robots have become an indispensable part of automated production lines.They widely used in a number of industrial production activities,such as spraying,welding,handling,etc.,and have a great role in these sectors.Recently,the robotic technology is developing towards high precision,high intelligence.Robot calibration technology has a great significance to improve the accuracy of robot.However,it has much work to be done in the identification of robot parameters.The parameter identification work of existing serial and parallel robots is introduced.On the one hand,it summarizes the methods for parameter calibration and discusses their advantages and disadvantages.On the other hand,the application of parameter identification is introduced.This overview has a great reference value for robot manufacturers to choose proper identification method,points further research areas for researchers.Finally,this paper analyzes the existing problems in robot calibration,which may be worth researching in the future.

Variable Stiffness Identification and Configuration Optimization of Industrial Robots for Machining Tasks

Industrial robots are increasingly being used in machining tasks because of their high flexibility and intelligence.However,the low structural stiffness of a robot significantly affects its positional accuracy and the machining quality of its operation equipment.Studying robot stiffness characteristics and optimization methods is an effective method of improving the stiffness performance of a robot.Accordingly,aiming at the poor accuracy of stiffness modeling caused by approximating the stiffness of each joint as a constant,a variable stiffness identification method is proposed based on space gridding.Subsequently,a task-oriented axial stiffness evaluation index is proposed to quantitatively assess the stiffness performance in the machining direction.In addition,by analyzing the redundant kinematic characteristics of the robot machining system,a configuration optimization method is further developed to maximize the index.For numerous points or trajectory-processing tasks,a configuration smoothing strategy is proposed to rapidly acquire optimized configurations.Finally,experiments on a KR500 robot were conducted to verify the feasibility and validity of the proposed stiffness identification and configuration optimization methods.

Uncalibrated Workpiece Positioning Method for Peg-in-hole Assembly Using an Industrial Robot

This paper proposes an uncalibrated workpiece positioning method for peg-in-hole assembly of a device using an industrial robot.Depth images are used to identify and locate the workpieces when a peg-in-hole assembly task is carried out by an industrial robot in a flexible production system.First,the depth image is thresholded according to the depth data of the workpiece surface so as to filter out the background interference.Second,a series of image processing and the feature recognition algorithms are executed to extract the outer contour features and locate the center point position.This image information,fed by the vision system,will drive the robot to achieve the positioning,approximately.Finally,the Hough circle detection algorithm is used to extract the features and the relevant parameters of the circular hole where the assembly would be done,on the color image,for accurate positioning.The experimental result shows that the positioning accuracy of this method is between 0.6-1.2 mm,in the used experimental system.The entire positioning process need not require complicated calibration,and the method is highly flexible.It is suitable for the automatic assembly tasks with multi-specification or in small batches,in a flexible production system.

Optimization of Serial Port of General Flex Pendant for Industrial Robot

In order to further improve the serial ports communication mode of the general Flex Pendant for industrial robot,a multiple serial communication mode is put forward. It is used to meet the stability of data transmission,transmission distance,transmission speed,anti-interference and cost-effective. Using ADUM1201 single direction dual channel digital isolator,two pieces of MAX13487 E and a piece of MAX3232 chip to transmit data and files, and to control chip’ s electrical level. Selecting the RS232, RS422 and RS485 communication mode,the serial ports of the general Flex Pendant of industrial robot is optimized.

Trajectory Optimization and Motion Simulation of a Flexible Polishing Industrial Robot for Watchcases

To optimize the working time of the flexible polishing industrial robot for watchcases,the polishing efficiency should be improved.Based on the quintic B-spline fitting curve trajectory planning method associated with the optimal time interval and the trajectory point angle,the trajectory route of the flexible polishing industrial robot for case parts was optimized by the Matlab software.The operation time of the flexible polishing industrial robot could reach the optimal level.The joints of the robot can be cooperated with each other to ensure that the motion track of the end-effector of the robot arm is closer to the expected motion track.Based on the Adams software,the obtained trajectory curve of multi-objective optimization was simulated,which verified the trajectory fitted after multi-objective optimization.The angular acceleration and angular plus acceleration curves were improved.Theoretical guidance was carried out for the subsequent experiment by Matlab and Adams simulation analysis.

The optimization method of industrial robot arm structure based on green manufacturing technology

In order to realize the optimal design of the industrial robot arm structure,an optimization method of the industrial robot arm structure based on green manufacturing technology is proposed.The stability of arm structure parameter acquisition can be controlled.The quantitative adjustment model of structural optimization parameters is constructed.The differential fusion control of the arm structure is realized.This paper analyzes the structure parameter law of the robot arm.We use dynamic parameter prediction and output torque parameter compensation method to control the arm structure.According to the adaptive iterative processing results,the arm structure parameter identification is realized.According to the identification results,the cutting parameter optimization method is adopted for the analytical control of the arm structure,and finally the optimized design of the industrial robot arm structure is realized through the green manufacturing technology.The simulation test results show that for the accuracy of the industrial robot arm structure design,this method is better,the output stability is higher,and the arm motion trajectory has a low deviation from the actual motion trajectory,which improves the optimization control and design capabilities of the industrial robot arm structure.

Application of the Morphological Analysis for the Synthesis Arrangements of Industrial Robots with Parallel Structure Mechanisms

The main features of morphological model of industrial robots are discussed, such as support system, manipulator and gripping device. These features are presented with the alternatives for their realization as separate modules. The examples of synthesis of arrangements of industrial robots are resulted on module principle with writing of their morphological formulas.

A calibration and compensation method for an industrial robot with high accuracy harmonic reducers

Industrial serial robots need high stiffness to keep absolute pose accuracy and meet the requirements in practical applications. However, the weak stiffness feature of robot joints and the payloads affected on robot end-effector, which will also increase the pose error of robot. Especially, the existing calibration methods often consider under no-payload condition without discussing the payload state. In this paper, we report a new industrial serial robot composed by a new harmonic reducer: Model-Y, based on high accuracy and high stiffness, and a kinematic parameter calibration algorithm which is based on a harmonic reducer forcedeformation model. To decrease the accuracy effects of payload, an iterative calibration method for kinematic parameters with payload situation was proposed. Simulation and experiments are conducted to verify the effectiveness of the proposed calibration method using the self-developed industrial serial robot. The results show a remarkably improved accuracy in absolute position and orientation with the robot's payload range. The position mean error has 70% decreased to 0.1 mm and the orientation mean error diminished to less than 0.01° after calibration with compensation. Additionally, online linear and circular tests are carried out to evaluate the position error of the robot during large-scale spatial and low-speed continuous movement. The accuracy is consistent with the previous calibration results, indicating the effectiveness and advantages of the proposed strategy in this article.

Have Industrial Robots Improved Pollution Reduction?A Theoretical Approach and Empirical Analysis

To investigate whether industrial robots have improved the ecological environment,this study integrated the adoption of robot technology and pollution abatement into Melitz's heterogeneous firm model.This showed that using robots in production can lower firms'pollution intensity by increasing their abatement investments,and this reduction effect is greater for higher polluting firms and those subject to weaker local environmental regulations.These theoretical expectations were then confirmed through a series of empirical investigations based on Bartik instrument regressions,with multiple robustness checks as well as heterogeneity and mechanism analyses.This paper adds to the literature on the relationships between automation technologies and green transformation.It shows that in the pursuit of economic growth and environmental protection,it is necessary for policymakers to shift from pollution control to technical support for traditional manufacturing firms.

Effect of industrial robot use on China’s labor market:Evidence from manufacturing industry segmentation

This paper empirically investigates the impact of industrial robot use on China’s labor market using data from 13 segments of manufacturing industry between 2006 and 2016.According to the findings,the use of industrial robots has a displacement effect on labor demand in manufacturing industry.The specific performance is that for every 1%increase in industrial robot stock,labor demand falls by 1.8%.After endogenous processing and a robustness test,this conclusion remains valid.This paper also discusses the effects of industrial robots across industries and genders.According to the results,industrial robot applications have a more pronounced displacement effect in low-skilled manufacturing than in high-skilled manufacturing.In comparison to female workers,industrial robot applications are more likely to decrease the demand for male workers.Moreover,this paper indicates that the displacement effect is significantly influenced by labor costs.Finally,we make appropriate policy recommendations for the labor market’s employment stability based on the findings.

Enabling Technologies for Autonomous Robotic Systems in Manufacturing

Research of autonomous manufacturing systems is motivated both by the new technical possibilities of cyber-physical systems and by the practical needs of the industry.Autonomous operation in semi-structured industrial environments can now be supported by advanced sensor technologies,digital twins,artificial intelligence and novel communication techniques.These enable real-time monitoring of production processes,situation recognition and prediction,automated and adaptive(re)planning,teamwork and performance improvement by learning.This paper summarizes the main requirements towards autonomous industrial robotics and suggests a generic workflow for realizing such systems.Application case studies will be presented from recent practice at HUN-REN SZTAKI in a broad range of domains such as assembly,welding,grinding,picking and placing,and machining.The various solutions have in common that they use a generic digital twin concept as their core.After making general recommendations for realizing autonomous robotic solutions in the industry,open issues for future research will be discussed.

Positioning error compensation of an industrial robot using neural networks and experimental study

Due to the characteristics of high efficiency,wide working range,and high flexibility,industrial robots are being increasingly used in the industries of automotive,machining,electrical and electronic,rubber and plastics,aerospace,food,etc.Whereas the low positioning accuracy,resulted from the serial configuration of industrial robots,has limited their further developments and applications in the field of high requirements for machining accuracy,e.g.,aircraft assembly.In this paper,a neural-network-based approach is proposed to improve the robots’positioning accuracy.Firstly,the neural network,optimized by a genetic particle swarm algorithm,is constructed to model and predict the positioning errors of an industrial robot.Next,the predicted errors are utilized to realize the compensation of the target points at the robot’s workspace.Finally,a series of experiments of the KUKA KR 500–3 industrial robot with no-load and drilling scenarios are implemented to validate the proposed method.The experimental results show that the positioning errors of the robot are reduced from 1.529 mm to 0.344 mm and from 1.879 mm to 0.227 mm for the no-load and drilling conditions,respectively,which means that the position accuracy of the robot is increased by 77.6%and 87.9%for the two experimental conditions,respectively.

Harmonic reducer in-situ fault diagnosis for industrial robots based on deep learning

The harmonic reducer is an essential kinetic transmission component in the industrial robots.It is easy to be fatigued and resulted in physical malfunction after a long period of operation.Therefore,an accurate in-situ fault diagnosis for the harmonic reducers in an industrial robot is especially important.This paper proposes a fault diagnosis method based on deep learning for the harmonic reducer of industrial robots via consecutive time-domain vibration signals.Considering the sampling signals from industrial robots are long,narrow,and channel-independent,this method combined a 1-dimensional convolutional neural network with matrix kernels(1-D MCNN)adaptive model.By adjusting the size of the convolution kernels,it can concentrate on the contextual feature extraction of consecutive time-domain data while retaining the ability to process the multi-channel fusion data.The proposed method is examined on a physical industrial robot platform,which has achieved a prediction accuracy of99%.Its performance is appeared to be superior in comparison to the traditional 2-dimensional CNN,deep sparse automatic encoding network(DSAE),multilayer perceptual network(MLP),and support vector machine(SVM).

Online identification of time-varying dynamical systems for industrial robots based on sparse Bayesian learning

Nowadays, industrial robots have been widely used in manufacturing, healthcare, packaging, and more. Choosing robots in these applications mainly attributes to their repeatability and precision. However, prolonged and loaded operations can deteriorate the accuracy and efficiency of industrial robots due to the unavoidable accumulated kinematical and dynamical errors. This paper resolves these aforementioned issues by proposing an online time-varying sparse Bayesian learning(SBL) method to identify dynamical systems of robots in real-time. The identification of dynamical systems for industrial robots is cast as a sparse linear regression problem. By constructing the dictionary matrix, the parameters of the robot dynamics are effectively estimated via a re-weighted1-minimization algorithm. Online recursive methods are integrated into SBL to achieve real-time system identification. By including sparsity and promoting online learning, the proposed method can handle time-varying dynamical systems and therefore improve operational stability and accuracy. Experimental results on both simulated and real selective compliance assembly robot arm(SCARA) robots have demonstrated the effectiveness of the proposed method for industrial robots.

Tracking and localization for omni-directional mobile industrial robot using reflectors

In this paper, an improved tracking and local-ization algorithm of an omni-directional mobile industrialrobot is proposed to meet the high positional accuracyrequirement, improve the robot＇s repeatability positioningprecision in the traditional trilateral algorithm, and solvethe problem of pose lost in the moving process. Lasersensors are used to identify the reflectors, and by associ-ating the reflectors identified at a particular time with thereflectors at a previous time, an optimal triangular posi-tioning method is applied to realize the positioning andtracking of the robot. The experimental results show thatpositioning accuracy can be satisfied, and the repeatabilityand anti-jamming ability of the omni-directional mobileindustrial robot will be greatly improved via this algorithm.

Deep adaptive control with online identification for industrial robots

Derivation of control equations from data is a critical problem in numerous scientific and engineering fields.The inverse dynamic control of robot manipulators in the field of industrial robot research is a key example.Traditionally,researchers needed to obtain the robot dynamic model through physical modeling methods before developing controllers.However,the robot dynamic model and suitable control methods are often elusive and difficult to tune,particularly when dealing with real dynamical systems.In this paper,we combine an enhanced online sparse Bayesian learning(OSBL)algorithm and a model reference adaptive control method to obtain a data-driven modeling and control strategy from data containing noise;this strategy can be applied to dynamical systems.In particular,we use a sparse Bayesian approach,relying only on some prior knowledge of its physics,to extract an accurate mechanistic model from the measured data.Unmodeled parameters are further identified from the modeling error through a deep neural network(DNN).By combining the identification model with a model reference adaptive control approach,a general deep adaptive control(DAC)method is obtained,which can tolerate unmodeled dynamics.The adaptive update law is derived from Lyapunov’s stability criterion,which guarantees the asymptotic stability of the system.Finally,the Enhanced OSBL identification method and DAC scheme are applied on a six-degree-of-freedom industrial robot,and the effectiveness of the proposed method is verified.

Fault Detection Based on FIR Filters with Variable Discount Factors for Industrial Robot Drive Systems

To enhance dynamic tracking performance and anti-disturbance capacity of finite impulse response(FIR) filters, variable discount factors are introduced to the recursive least squares(RLS) algorithm. By employing improved FIR filters to conduct modelling of industrial robot drive systems, dynamic characteristics of the target systems are identified. Then the fault detection for a target system can be utilized by analyzing the coefficients of the FIR filter. Finally, an application of the fault detection scheme to a kind of brushless DC motor drive system is described. Compared with reference methods, the proposed scheme achieves effective fault detection and performs better in dynamic tracking and robustness according to the final simulation results.

Deflection estimation of industrial robots with flexible joints

The application of industrial robots in manufacturing industries has received considerable concerns due to the high flexibility,multifunctionality,and cost-efficiency.It is well known that the robot positioning accuracy is susceptible to the load and motion of robots owing to the insufficient stiffness of robots.Therefore,the machining accuracy improvement has been a research focus in the robotic manufacturing industries in the last decade.To overcome the measurement difficulty of the joint torque and position as well as the complex dynamic coupling between rotors and links,two forward dynamics algorithms for the robot deflection estimation are proposed in this paper.The robot kinematics and dynamics algorithms considering the dynamic coupling between rotors and links are developed based on Lie theory.The forward dynamics equations of robots are solved via the proposed algorithms:the implicit numerical integration algorithm and numerical iterative estimation algorithm.When only the motor position is available,the implicit numerical integration algorithm is employed to solve the forward dynamics equations to estimate the joint torque and position.At the same time,when both the motor position and torque are available,the forward dynamics equations can be reorganized as algebraic equations and solved by the numerical iterative estimation algorithm.Simulations of a 6-DOF serial robot are performed to verify the accuracy of the proposed algorithms.