| 1. |
- Carvalho Bittencourt, André, 1984-, et al.
(författare)
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A data-driven approach to diagnostics of repetitive processes in the distribution domain : Applications to gearbox diagnosticsin industrial robots and rotating machines
- 2014
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Ingår i: Mechatronics (Oxford). - : Elsevier. - 0957-4158 .- 1873-4006. ; 24:8, s. 1032-1041
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Carvalho Bittencourt, André, 1984-, et al.
(författare)
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A Data-Driven Method for Monitoring of Repetitive Systems: Applications to Robust Wear Monitoring of a Robot Joint
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents a method for monitoring 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 a 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 unknown 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 method was developed with interests in industrial robotics where a repetitive behavior is commonly found. The problem of wear monitoring in a robot joint is studied based on data collected from a test-cycle. Real data from accelerated wear tests and simulations are considered. Promising results are achieved where the method output shows a clear response to the wear increases.
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| 3. |
- Carvalho Bittencourt, André, et al.
(författare)
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A Data-Driven Method for Monitoring Systems that Operate Repetitively : Applications to Robust Wear Monitoring inan Industrial Robot Joint
- 2011
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 4. |
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| 5. |
- Carvalho Bittencourt, André, et al.
(författare)
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An Extended Friction Model to capture Load and Temperature effects in Robot Joints
- 2010
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Ingår i: Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. - Linköping : Linköping University Electronic Press. - 9781424466740 ; , s. 6161-6167
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Konferensbidrag (refereegranskat)abstract
- Friction is the result of complex interactions between contacting surfaces in a nanoscale perspective. Depending on the application, the different models available are more or less suitable. Available 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, static friction in robot joints is studied with respect to changes in joint angle, load torque and temperature. The effects of these variables are analyzed by means of experiments on a standard industrial robot. 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, reducing the average error a factor of 6 when compared to a standard static friction model.
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| 6. |
- Carvalho Bittencourt, André, 1984-, et al.
(författare)
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Data-Driven Anomaly Detection based on a Bias Change
- 2014
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Ingår i: Proceedings of the 19th IFAC World Congress.
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 7. |
- Carvalho Bittencourt, André, 1984-
(författare)
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Data-driven estimation of Gramian based interaction measures for control structure selection
- 2016
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Interaction measures quantify the input-output relations in MIMO processes and can support the selection of control structures (CSS). Interaction measures are typically computed based on an existing process models. The study of input-output interactions based on data can complement missing information on a model, e.g., revealing unknown relations in a complex system or adjusting for a time dependent behavior. This paper presents a unified approach for data-driven estimation of Gramian based interaction measures from input-output data. Given open or closed-loop data, a high-order Vector ARX (VARX) model is identified and its parameters are used to calculate predictor Markov parameters, together with a covariance estimate. Three interaction measures based on the Hankel, Hilbert-Schmidt-Hankel and H2 norms are calculated from the estimated predictor Markov parameters and uncertainty estimates are provided for the last two, allowing for robust CSS. A solution which is recursive in the data points is presented, making it practical for applications to large datasets. The approach is verified through simulations and several possible extensions are discussed. As the method is suitable for open and closed-loop data and for large datasets, it opens up for data-driven control structure selection based on operational data from entire plants.
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| 8. |
- Carvalho Bittencourt, André, 1984-
(författare)
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Modeling and Diagnosis of Friction and Wear in Industrial Robots
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 9. |
- Carvalho Bittencourt, André, 1984-, et al.
(författare)
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Modeling and Experiment Design for Identification of Wear in a Robot Joint under Load and Temperature Uncertainties based on Constant-speed Friction Data
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- 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 availability of such model and constant-speed friction data, a maximum likelihood wear estimator is proposed. A criterion for experiment design is proposed which selects speed points to collect constant-speed friction data which improves the achievable performance bound for any unbiased wear estimator. Practical issues related to experiment length are also considered. 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.
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| 10. |
- Carvalho Bittencourt, André, et al.
(författare)
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Modeling and Experiment Design for Identification of Wear in a Robot Joint Under Load and Temperature Uncertainties Based on Friction Data
- 2014
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Ingår i: IEEE/ASME transactions on mechatronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1083-4435 .- 1941-014X. ; 19:5, s. 1694-1706
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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