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Modeling and Diagnosis of Friction and Wear in Industrial Robots
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

High availability and low operational costs are critical for industrial systems. While industrial equipments are designed to endure several years of uninterrupted operation, their behavior and performance will eventually deteriorate over time. To support service and operation decisions, it is important to devise methods to infer the condition of equipments from available data.

The monitoring of industrial robots is an important problem considered in this thesis. The main focus is on the design of methods for the detection of excessive degradations due to wear in a robot joint. Since wear is related to friction, an important idea for the proposed solutions is to analyze the behavior of friction in the joint to infer about wear. Based on a proposed friction model and friction data collected from dedicated experiments, a method is suggested to estimate wear-related effects to friction. As it is shown, the achieved estimates allow for a clear distinction of the wear effects even in the presence of large variations to friction associated to other variables, such as temperature and load.

In automated manufacturing, a continuous and repeatable operation of equipments is important to achieve production requirements. Such repetitive behavior of equipments is explored to define a data-driven approach to diagnosis. Considering data collected from a repetitive operation, an abnormality is inferred by comparing nominal against monitored data in the distribution domain. The approach is demonstrated with successful applications for the diagnosis of wear in industrial robots and gear faults in a rotating machine.

Because only limited knowledge can be embedded in a fault detection method, it is important to evaluate solutions in scenarios of practical relevance. A simulation based framework is proposed that allows for determination of which variables affect a fault detection method the most and how these variables delimit the effectiveness of the solution. Based on an average performance criterion, an approach is also suggested for a direct comparison of different methods. The ideas are illustrated for the robotics application, revealing properties of the problem and of different fault detection solutions.

An important task in fault diagnosis is a correct determination of presence of a condition change. An early and reliable detection of an abnormality is important to support service, giving enough time to perform maintenance and avoid downtime. Data-driven methods are proposed for anomaly detection that only require availability of nominal data and minimal/meaningful specification parameters from the user. Estimates of the detection uncertainties are also possible, supporting higher level service decisions. The approach is illustrated with simulations and real data examples including the robotics application.

Abstract [sv]

För industriella system är både hög tillgänglighet och låga driftskostnader avgörande. Industriella system är oftast utformad för att klara flera års oavbruten drift, men över tid kommer beteendet och prestandan så småningom att förändras. Det är därför viktigt att ta fram metoder som kan extrahera information från tillgänglig data och dra slutsatser om systemets beteende, som i sin tur används som stöd för beslut angående systemets fortsatta drift.Denna avhandling handlar om utformning och utvärdering av diagnostiska metoder för att stödja tids- och kostnadseffektiva beslut angående den fortsatta driften för systemet. I synnerhet studeras problemet med att upptäcka för höga nivåer av slitage i respektive led för en industrirobot. Eftersom slitage påverkar friktionen kan det vara en bra id{\'e} att analysera friktionen för att uppskatta hur stort slitage som har uppkommit. Baserat på en föreslagen friktionsmodell och friktionsdata från specialanpassade experiment föreslås en metod för att uppskatta slitagets omfattning. Metoden försöker anpassa modellen så att sannolikheten att mätningarna kommer från den föreslagna modellen maximeras. Det visar sig att tillförlitliga beräkningar av slitaget kan uppnås även vid stora variationer i belastningen på roboten samt temperaturen i robotens leder, vilket gör det möjligt att planera underhåll för roboten innan den går sönder.

Vidare undersöks hur ett systems repetitiva beteende, som är vanligt inom automatiserad tillverkning, kan utnyttjas för att skapa en metod för diagnos som endast använder befintlig data utan hjälp av någon modell. Med hjälp av data som har samlats in från en repetitiv process kan en förändring av processen upptäckas genom att jämföra data från systemet i felfri drift och befintlig drift. Metoden som föreslås utnyttjar den empiriska sannolikhetsfördelningen för systemet i felfri respektive befintlig drift. Det visar sig att metoden med framgång kan detektera slitage i lederna för en industrirobot samt växelfel i en roterande mekanism.I avhandlingen föreslås också metoder för feldetektering. Testet går ut på att man jämför två hypoteser mot varandra genom ett statistiskt ramverk. För att upptäcka en förändring av ett system är det naturligt att de två hypoteserna motsvarar ett system utan fel respektive ett system med fel. Det enda som förutsätts är att data från systemet utan fel är tillgängligt. En annan viktig del är att kunna jämföra olika diagnosmetoder för att se vilken som passar bäst till det aktuella problemet. Ett ramverk baserat på simuleringar har därför föreslagits för utvärdering av diagnosmetoder. Ramverket kan användas för att avgöra vilka variabler som påverkar metoden mest, hur man jämför olika metoder samt hur man bestämmer det effektiva användningsområdet för respektive metod. De föreslagna diagnosmetoderna och ramverket för utvärdering av diagnosmetoderna är generella men illustreras i avhandlingen på tillämpningar för industrirobotar.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. , 208 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1617
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-109335DOI: 10.3384/diss.diva-109335ISBN: 978-91-7519-251-2 (print)OAI: oai:DiVA.org:liu-109335DiVA: diva2:738580
Public defence
2014-09-26, Visionen, B building, Campus Valla, Linköping University, Linköping, 10:15 (English)
Opponent
Supervisors
Projects
Vinnova Excellence Center LINK-SIC
Funder
Vinnova
Available from: 2014-09-08 Created: 2014-08-13 Last updated: 2014-09-29Bibliographically 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 Experiment Design for Identification of Wear in a Robot Joint Under Load and Temperature Uncertainties Based on Friction Data
Open this publication in new window or tab >>Modeling and Experiment Design for Identification of Wear in a Robot Joint Under Load and Temperature Uncertainties Based on Friction Data
2014 (English)In: IEEE/ASME transactions on mechatronics, ISSN 1083-4435, E-ISSN 1941-014X, Vol. 19, no 5, 1694-1706 p.Article in journal (Refereed) Published
Abstract [en]

The effects of wear to friction are studied based on constant-speed friction data collected from dedicated experiments during accelerated wear tests. It is shown how the effects of temperature and load uncertainties produce larger changes to friction than those caused by wear, motivating the consideration of these effects. Based on empirical observations, an extended friction model is proposed to describe the effects of speed, load, temperature, and wear. Assuming the availability of such a model and constant-speed friction data, a maximum likelihood wear estimator is proposed. The performance of the wear estimator under load and temperature uncertainties is found by means of simulations and verified under three case studies based on real data. Practical issues related to experiment length are considered based on an optimal selection of speed points to collect friction data, improving the achievable performance bound for any unbiased wear estimator. As it is shown, reliable wear estimates can be achieved even under load and temperature uncertainties, making condition-based maintenance of industrial robots possible.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2014
Keyword
Condition monitoring; friction; identification; industrial robotics; wear
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-109164 (URN)10.1109/TMECH.2013.2293001 (DOI)000338107600021 ()
Available from: 2014-08-13 Created: 2014-08-11 Last updated: 2017-12-05Bibliographically approved
3. A data-driven approach to diagnostics of repetitive processes in the distribution domain: Applications to gearbox diagnosticsin industrial robots and rotating machines
Open this publication in new window or tab >>A data-driven approach to diagnostics of repetitive processes in the distribution domain: Applications to gearbox diagnosticsin industrial robots and rotating machines
Show others...
2014 (English)In: Mechatronics (Oxford), ISSN 0957-4158, E-ISSN 1873-4006, Vol. 24, no 8, 1032-1041 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a data-driven approach to diagnostics of systems that operate in a repetitive manner. Considering that data batches collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing the monitored data against an available nominal batch. The method proposed considers the comparison of data in the distribution domain, which reveals information of the data amplitude. This is achieved with the use of kernel density estimates and the Kullback–Leibler distance. To decrease sensitivity to disturbances while increasing sensitivity to faults, the use of a weighting vector is suggested which is chosen based on a labeled dataset. The framework is simple to implement and can be used without process interruption, in a batch manner. The approach is demonstrated with successful experimental and simulation applications to wear diagnostics in an industrial robot gearbox and for diagnostics of gear faults in a rotating machine.

Place, publisher, year, edition, pages
Elsevier, 2014
National Category
Robotics
Identifiers
urn:nbn:se:liu:diva-109332 (URN)10.1016/j.mechatronics.2014.01.013 (DOI)000347499900014 ()
Funder
VINNOVA
Available from: 2014-08-13 Created: 2014-08-13 Last updated: 2017-12-05Bibliographically approved
4. Simulation based Evaluation of Fault Detection Algorithms: Applications to Wear Diagnosis in Manipulators
Open this publication in new window or tab >>Simulation based Evaluation of Fault Detection Algorithms: Applications to Wear Diagnosis in Manipulators
Show others...
2014 (English)In: Proceedings of the 19th IFAC World Congress, 2014Conference paper, Published paper (Refereed)
Abstract [en]

Fault detection algorithms (FDAs) process data to generate a test quantity. Test quantities are used to determine presence of a fault in a monitored system, despite disturbances. Because only limited knowledge of the system can be embedded in an FDA, it is important to evaluate it in scenarios relevant in practice. In this paper, simulation based approaches are proposed in an attempt to determine: i) which disturbances affect the output of an FDA the most; ii) how to compare the performance of dierent FDAs; and iii) which combinations of fault change size and disturbances variations are allowed to achieve satisfactory performance. The ideas presented are inspired by the literature of design of experiments, surrogate models, sensitivity analysis and change detection. The approaches are illustrated for the problem of wear diagnosis in manipulators where three FDAs are considered. The application study reveals that disturbances caused by variations in temperature and payload mass error affect the FDAs the most. It is also shown how the size of these disturbances delimit the capacity of an FDA to relate to wear changes. Further comparison of the FDAs reveal which performs "best" in average.

National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-109333 (URN)
Conference
19th IFAC World Congress, Cape Town, South Africa, August 24-29, 2014
Funder
Vinnova
Available from: 2014-08-13 Created: 2014-08-13 Last updated: 2014-09-17
5. Data-Driven Anomaly Detection based on a Bias Change
Open this publication in new window or tab >>Data-Driven Anomaly Detection based on a Bias Change
2014 (English)In: Proceedings of the 19th IFAC World Congress, 2014Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes batch and sequential data-driven approaches to anomaly detection based on generalized likelihood ratio tests for a bias change. The procedure is divided into two steps. Assuming availability of a nominal dataset, a nonparametric density estimate is obtained in the first step, prior to the test. Second, the unknown bias change is estimated from test data. Based on the expectation maximization (EM) algorithm, batch and sequential maximum likelihood estimators of the bias change are derived for the case where the densit yestimate is given by a Gaussian mixture. Approximate asymptotic expressions for the probabilities of error are suggested based on available results. Simulations and real world experiments illustrate the approach.

National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-109334 (URN)
Conference
19th IFAC World Congress, Cape Town, South Africa, August 24-29, 2014
Funder
Vinnova
Available from: 2014-08-13 Created: 2014-08-13 Last updated: 2014-09-17

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Carvalho Bittencourt, André

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