scholarly journals A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 80
Author(s):  
Corbinian Nentwich ◽  
Gunther Reinhart
Keyword(s):  
Condition Monitoring ◽  
Production Systems ◽  
Industrial Robot ◽  
Health Indicators ◽  
Industrial Robots ◽  
Health Indicator ◽  
Vibration Data ◽  
Z Scores ◽  
Lower Temperature ◽  
Indicator Evaluation

Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. In this context, we propose a new method to evaluate health indicators for this application and suggest a new health indicator (HI) based on vibration data measurements, Short-time Fourier transform and Z-scores. By executing the method, we find that the proposed health indicator can detect varying faults better, has lower temperature sensitivity and works better in instationary velocity regimes compared to several state-of-the-art HIs. A discussion of the validity of the results concludes our contribution.

scholarly journals A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

2021 ◽  
Vol 11 (21) ◽  
pp. 10403
Author(s):  
Corbinian Nentwich ◽  
Gunther Reinhart
Keyword(s):  
Anomaly Detection ◽  
Condition Monitoring ◽  
Production Systems ◽  
Detection System ◽  
Industrial Robot ◽  
Health Indicators ◽  
Automated System ◽  
Trend Detection ◽  
Detection Methods ◽  
Application Data

Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this publication, such a system is presented and suitable anomaly detection and trend detection methods for the system are selected based on synthetic and real world industrial application data. A statistical test, namely the Cox-Stuart test, appears to be the most suitable approach for trend detection and the local outlier factor algorithm or the long short-term neural network performs best for anomaly detection in the application of industrial robot gear condition monitoring in the presented experiments.

Finding Features of Positioning Error for Large Industrial Robots Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Daiki Kato ◽  
Kenya Yoshitugu ◽  
Naoki Maeda ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Production Systems ◽  
Industrial Robot ◽  
Three Dimensional ◽  
Industrial Robots ◽  
Numerical Control ◽  
Robot Motion ◽  
Positioning Error ◽  
End Effector

Abstract Most industrial robots are taught using the teaching playback method; therefore, they are unsuitable for use in variable production systems. Although offline teaching methods have been developed, they have not been practiced because of the low accuracy of the position and posture of the end-effector. Therefore, many studies have attempted to calibrate the position and posture but have not reached a practical level, as such methods consider the joint angle when the robot is stationary rather than the features during robot motion. Currently, it is easy to obtain servo information under numerical control operations owing to the Internet of Things technologies. In this study, we propose a method for obtaining servo information during robot motion and converting it into images to find features using a convolutional neural network (CNN). Herein, a large industrial robot was used. The three-dimensional coordinates of the end-effector were obtained using a laser tracker. The positioning error of the robot was accurately learned by the CNN. We extracted the features of the points where the positioning error was extremely large. By extracting the features of the X-axis positioning error using the CNN, the joint 1 current is a feature. This indicates that the vibration current in joint 1 is a factor in the X-axis positioning error.

scholarly journals Wireless Fault Detection System for an Industrial Robot Based on Statistical Control Chart

2017 ◽  
Vol 7 (6) ◽  
pp. 3421
Author(s):  
Alaa Abdulhady Jaber ◽  
Robert Bicker
Keyword(s):  
Fault Detection ◽  
Control Chart ◽  
Production Systems ◽  
Detection System ◽  
Industrial Robot ◽  
Vibration Signal ◽  
Cost Effective ◽  
Base Station ◽  
Industrial Robots ◽  
Statistical Control

Industrial robots are now commonly used in production systems to improve productivity, quality and safety in manufacturing processes. Recent developments involve using robots cooperatively with production line operatives. Regardless of application, there are significant implications for operator safety in the event of a robot malfunction or failure, and the consequent downtime has a significant impact on productivity in manufacturing. Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation and thus reducing the maintenance costs. Developments in electronics and computing have opened new horizons in the area of condition monitoring. The aim of using wireless electronic systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to develop an online and wireless fault detection system for an industrial robot based on statistical control chart approach. An experimental investigation was accomplished using the PUMA 560 robot and vibration signal capturing was adopted, as it responds immediately to manifest itself if any change is appeared in the monitored machine, to extract features related to the robot health conditions. The results indicate the successful detection of faults at the early stages using the key extracted parameters.

scholarly journals An integrated condition monitoring method for rolling element bearings based on perceptual vibration hashing and SOM

2021 ◽  
Vol 1207 (1) ◽  
pp. 012012
Author(s):  
Shiwei Yan ◽  
Haining Liu ◽  
Fajia Li ◽  
Huanyong Cui
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Rolling Element Bearings ◽  
Health Indicator ◽  
Self Organizing Maps ◽  
Monitoring Method ◽  
Vibration Data ◽  
Healthy State ◽  
Rolling Element ◽  
Som Network

Abstract Rolling element bearings are widely used in rotating machinery. Bearing faults will result in damage to property. So, the condition monitoring of bearings is of great significance, but few methods can achieve both degradation assessment and fault diagnosis. In this paper, an integrated condition monitoring method for rolling element bearings based on perceptual vibration hashing (PVH) and self-organizing maps (SOM) is proposed. Distance matric based on PVH is used as a health indicator for degradation assessment, in which the baseline of healthy state is selected based on the clustering centre of SOM instead of experience. When the value of health indicator exceeds the pre-set threshold, visualized fault diagnosis can also be achieved by training the SOM network. The effectiveness of the developed method is verified with the vibration data from accelerated degradation tests of rolling element bearings.

SAFETY AND RISK ASSESSMENT AT AUTOMATED WORKPLACE

2018 ◽  
pp. 78-84
Author(s):  
Marek Vagas
Keyword(s):  
Risk Assessment ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Added Value ◽  
Growing Up ◽  
Target Setting ◽  
Clear View ◽  
General Safety

Urgency of the research. Automated workplaces are growing up in present, especially with implementation of industrial robots with feasibility of various dispositions, where safety and risk assessment is considered as most important issues. Target setting. The protection of workers must be at the first place, therefore safety and risk assessment at automated workplaces is most important problematic, which had presented in this article Actual scientific researches and issues analysis. Actual research is much more focused at standard workplaces without industrial robots. So, missing of information from the field of automated workplaces in connection with various dispositions can be considered as added value of article. Uninvestigated parts of general matters defining. Despite to lot of general safety instructions in this area, still is missed clear view only at automated workplace with industrial robots. The research objective. The aim of article is to provide general instructions directly from the field of automated workplaces The statement of basic materials. For success realization of automated workplace is good to have a helping hand and orientation requirements needed for risk assessment at the workplace. Conclusions. The results published in this article increase the awareness and information of such automated workplaces, together with industrial robots. In addition, presented general steps and requirements helps persons for better realization of these types of workplaces, where major role takes an industrial robot. Our proposed solution can be considered as relevant base for risk assessment such workplaces with safety fences or light barriers.

A Polishing Robot Force Control System Based on Time Series Data in Industrial Internet of Things

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Chen Zhang ◽  
Zhuo Tang ◽  
Kenli Li ◽  
Jianzhong Yang ◽  
Li Yang
Keyword(s):  
Time Series ◽  
Control System ◽  
Force Control ◽  
Time Series Data ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Series Data ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Force Control System

Installing a six-dimensional force/torque sensor on an industrial arm for force feedback is a common robotic force control strategy. However, because of the high price of force/torque sensors and the closedness of an industrial robot control system, this method is not convenient for industrial mass production applications. Various types of data generated by industrial robots during the polishing process can be saved, transmitted, and applied, benefiting from the growth of the industrial internet of things (IIoT). Therefore, we propose a constant force control system that combines an industrial robot control system and industrial robot offline programming software for a polishing robot based on IIoT time series data. The system mainly consists of four parts, which can achieve constant force polishing of industrial robots in mass production. (1) Data collection module. Install a six-dimensional force/torque sensor at a manipulator and collect the robot data (current series data, etc.) and sensor data (force/torque series data). (2) Data analysis module. Establish a relationship model based on variant long short-term memory which we propose between current time series data of the polishing manipulator and data of the force sensor. (3) Data prediction module. A large number of sensorless polishing robots of the same type can utilize that model to predict force time series. (4) Trajectory optimization module. The polishing trajectories can be adjusted according to the prediction sequences. The experiments verified that the relational model we proposed has an accurate prediction, small error, and a manipulator taking advantage of this method has a better polishing effect.

scholarly journals Comparative Analysis of the Life-Cycle Cost of Robot Substitution: A Case of Automobile Welding Production in China

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 226
Author(s):  
Xuyang Zhao ◽  
Cisheng Wu ◽  
Duanyong Liu
Keyword(s):  
Life Cycle ◽  
Systems Engineering ◽  
Life Cycle Cost ◽  
Manufacturing Systems ◽  
Large Scale ◽  
Cost Allocation ◽  
Industrial Robot ◽  
Case Analysis ◽  
Industrial Robots ◽  
Analysis Model

Within the context of the large-scale application of industrial robots, methods of analyzing the life-cycle cost (LCC) of industrial robot production have shown considerable developments, but there remains a lack of methods that allow for the examination of robot substitution. Taking inspiration from the symmetry philosophy in manufacturing systems engineering, this article further establishes a comparative LCC analysis model to compare the LCC of the industrial robot production with traditional production at the same time. This model introduces intangible costs (covering idle loss, efficiency loss and defect loss) to supplement the actual costs and comprehensively uses various methods for cost allocation and variable estimation to conduct total cost and the cost efficiency analysis, together with hierarchical decomposition and dynamic comparison. To demonstrate the model, an investigation of a Chinese automobile manufacturer is provided to compare the LCC of welding robot production with that of manual welding production; methods of case analysis and simulation are combined, and a thorough comparison is done with related existing works to show the validity of this framework. In accordance with this study, a simple template is developed to support the decision-making analysis of the application and cost management of industrial robots. In addition, the case analysis and simulations can provide references for enterprises in emerging markets in relation to robot substitution.

scholarly journals Research of Calibration Method for Industrial Robot Based on Error Model of Position

2021 ◽  
Vol 11 (3) ◽  
pp. 1287
Author(s):  
Tianyan Chen ◽  
Jinsong Lin ◽  
Deyu Wu ◽  
Haibin Wu
Keyword(s):  
Coordinate System ◽  
Industrial Robot ◽  
Calibration Method ◽  
Error Model ◽  
Position Error ◽  
Industrial Robots ◽  
Positioning Error ◽  
Robot Position ◽  
General Measurement ◽  
Parameter Error

Based on the current situation of high precision and comparatively low APA (absolute positioning accuracy) in industrial robots, a calibration method to enhance the APA of industrial robots is proposed. In view of the "hidden" characteristics of the RBCS (robot base coordinate system) and the FCS (flange coordinate system) in the measurement process, a comparatively general measurement and calibration method of the RBCS and the FCS is proposed, and the source of the robot terminal position error is classified into three aspects: positioning error of industrial RBCS, kinematics parameter error of manipulator, and positioning error of industrial robot end FCS. The robot position error model is established, and the relation equation of the robot end position error and the industrial robot model parameter error is deduced. By solving the equation, the parameter error identification and the supplementary results are obtained, and the method of compensating the error by using the robot joint angle is realized. The Leica laser tracker is used to verify the calibration method on ABB IRB120 industrial robot. The experimental results show that the calibration method can effectively enhance the APA of the robot.

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

scholarly journals Fault severity detection of a worm gearbox based on several feature extraction methods through a developed condition monitoring system

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Berkan Hızarcı ◽  
Rafet Can Ümütlü ◽  
Zeki Kıral ◽  
Hasan Öztürk
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Extraction Methods ◽  
Poincaré Plot ◽  
Vibration Data ◽  
Sliding Motion ◽  
Fault Severity ◽  
Fault Features ◽  
Gearbox Vibration ◽  
Condition Monitoring System

AbstractThis study presents the severity detection of pitting faults on worm gearbox through the assessment of fault features extracted from the gearbox vibration data. Fault severity assessment on worm gearbox is conducted by the developed condition monitoring instrument with observing not only traditional but also multidisciplinary features. It is well known that the sliding motion between the worm gear and wheel gear causes difficulties about fault detection on worm gearboxes. Therefore, continuous monitoring and observation of different types of fault features are very important, especially for worm gearboxes. Therefore, in this study, time-domain statistics, the features of evaluated vibration analysis method and Poincaré plot are examined for fault severity detection on worm gearbox. The most reliable features for fault detection on worm gearbox are determined via the parallel coordinate plot. The abnormality detection during worm gearbox operation with the developed system is performed successfully by means of a decision tree.

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