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A Data-driven Method for Monitoring Systems that Operate Repetitively: Applications to Robust Wear Monitoring inan Industrial Robot Joint
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
ABB Corporate Research Västerås, Sweden.
ABB Corporate Research Västerås, Sweden.
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.

Place, publisher, year, edition, pages
2011.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-72974OAI: oai:DiVA.org:liu-72974DiVA: diva2:464273
Note

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

Available from: 2011-12-13 Created: 2011-12-13 Last updated: 2012-09-21
In thesis
1. On Modeling and Diagnosis of Friction and Wear in Industrial Robots
Open this publication in new window or tab >>On Modeling and Diagnosis of Friction and Wear in Industrial Robots
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:nbn:se:liu:diva-72975 (URN)LiU-TEK-LIC-2012:1 (Local ID)978-91-7519-982-5 (ISBN)LiU-TEK-LIC-2012:1 (Archive number)LiU-TEK-LIC-2012:1 (OAI)
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

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Carvalho Bittencourt, André

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