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On Modeling and Diagnosis of Friction and Wear in Industrial Robots
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Industrial robots are designed to endure several years of uninterrupted operation and therefore are very reliable. However, no amount of design effort can prevent deterioration over time, and equipments will eventually fail. Its impacts can, nevertheless, be considerably reduced if good maintenance/service practices are performed. The current practice for service of industrial robots is based on preventive and corrective policies, with little consideration about the actual condition of the system. In the current scenario, the serviceability of industrial robots can be greatly improved with the use of condition monitoring/diagnosis methods, allowing for condition-based maintenance (cbm).

This thesis addresses the design of condition monitoring methods for industrial robots. The main focus is on the monitoring and diagnosis of excessive degradations caused by wear of the mechanical parts. The wear processes may take several years to be of significance, but can evolve rapidly once they start to appear. An early detection of excessive wear levels can therefore allow for cbm, increasing maintainability and availability. Since wear is related to friction, the basic idea pursued is to analyze the friction behavior to infer about wear.

To allow this, an extensive study of friction in robot joints is considered in this work. The effects of joint temperature, load and wear changes to static friction in robot a joint are modeled based on empirical observations. It is found that the effects of load and temperature to friction are comparable to those caused by wear. Joint temperature and load are typically not measured, but will always be present in applications. Therefore, diagnosis solutions must be able to cope with them.

Different methods are proposed which allow for robust wear monitoring. First, a wear estimator is suggested. Wear estimates are made possible with the use of a test-cycle and a friction model. Second, a method is defined which considers the repetitive behavior found in many applications of industrial robots. The result of the execution of the same task in different instances of time are compared to provide an estimate of how the system changed over the period. Methods are suggested that consider changes in the distribution of data logged from the robot. It is shown through simulations and experiments that robust wear monitoring  is made possible with the proposed methods.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press , 2012. , 66 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1516
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-72975Local ID: LiU-TEK-LIC-2012:1ISBN: 978-91-7519-982-5 (print)OAI: oai:DiVA.org:liu-72975DiVA: diva2:464280
Presentation
2012-01-20, Visionen, Hus B, Campus Valla, Linköpings Universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2011-12-13 Created: 2011-12-13 Last updated: 2012-07-09Bibliographically approved
List of papers
1. Static Friction in a Robot Joint: Modeling and Identification of Load and Temperature Effects
Open this publication in new window or tab >>Static Friction in a Robot Joint: Modeling and Identification of Load and Temperature Effects
2012 (English)In: Journal of Dynamic Systems Measurement, and Control, ISSN 0022-0434, E-ISSN 1528-9028, Vol. 134, no 5Article in journal (Refereed) Published
Abstract [en]

Friction is the result of complex interactions between contacting surfaces in down to a nanoscale perspective. Depending on the application, the different models available are more or less suitable. Static friction models are typically considered to be dependent only on relative speed of interacting surfaces. However, it is known that friction can be affected by other factors than speed.

In this paper, the typical friction phenomena and models used in robotics are reviewed. It is shown how such models can be represented as a sum of functions of relevant states which are linear and nonlinear in the parameters, and how the identification method described in Golub and Pereyra (1973) can be used to identify them when all states are measured. The discussion follows with a detailed experimental study of friction in a robot joint under changes of joint angle, load torque and temperature. Justified by their significance, load torque and temperature are included in an extended static friction model. The proposed model is validated in a wide operating range, considerably improving the prediction performance compared to a standard model.

Place, publisher, year, edition, pages
American Society of Mechanical Engineers (ASME), 2012
Keyword
Robotics, Friction, Identification
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-72971 (URN)10.1115/1.4006589 (DOI)000308418400013 ()
Funder
Vinnova
Available from: 2011-12-13 Created: 2011-12-13 Last updated: 2017-12-08Bibliographically approved
2. Modeling and Identification of Wear in a Robot Joint under Temperature Uncertainties
Open this publication in new window or tab >>Modeling and Identification of Wear in a Robot Joint under Temperature Uncertainties
2011 (English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper considers the problem of wear estimation in a standard industrial robot joint. The effects of wear to the static friction of a robot joint are analyzed from experiments. An extended static friction model is proposed that explains changes related to joint speed, load, temperature and wear. Based on this model and static friction observations, a model-based wear estimator is proposed. The performance of the estimator under temperature uncertainties is found both by means of simulations and experiments in an industrial robot. Special attention is given to the analyses of the best speed region for wear estimation. As it is shown, the method can distinguish the effects of wear even under large temperature variations, opening up for the use of robust joint diagnosis for industrial robots.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-72973 (URN)
Note
Preliminary version in Technical Report LiTH-ISY-R-2981.Available from: 2011-12-13 Created: 2011-12-13 Last updated: 2014-09-09Bibliographically approved
3. A Data-driven Method for Monitoring Systems that Operate Repetitively: Applications to Robust Wear Monitoring inan Industrial Robot Joint
Open this publication in new window or tab >>A Data-driven Method for Monitoring Systems that Operate Repetitively: Applications to Robust Wear Monitoring inan Industrial Robot Joint
2011 (English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper presents a method for condition monitoring of systems that operate in a repetitive manner. A data driven method is proposed that considers changes in the distribution of data samples obtained from multiple executions of one or several tasks. This is made possible with the use of kernel density estimators and the Kullback-Leibler distance measure between distributions. To increase robustness to unknown disturbances and sensitivity to faults, the use of a weighting function is suggested which can considerably improve detection performance. The method is very simple to implement, it does not require knowledge about the monitored system and can be used without process interruption, in a batch manner. The method is illustrated with applications to robust wear monitoring in a robot joint. Interesting properties of the application are presented through a real case study and simulations. The achieved results show that robust wear monitoring in industrial robot joints is made possible with the proposed method.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-72974 (URN)
Note

Preliminary version in Technical Report LiTH-ISY-R-3040.

Available from: 2011-12-13 Created: 2011-12-13 Last updated: 2012-09-21

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Carvalho Bittencourt, André

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