André Carvalho Bittencourt

An extended friction model to capture load and temperature effects in robot joints

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.

Static Friction in a Robot Joint—Modeling and Identification of Load and Temperature Effects

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 ...

Modeling and Experiment Design for Identification of Wear in a Robot Joint Under Load and Temperature Uncertainties Based on Friction Data

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.

A Data-driven Method for Monitoring Systems that Operate Repetitively -Applications to Wear Monitoring in an Industrial Robot Joint1

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. The method 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. Real data from accelerated wear tests are considered. Promising results are achieved, where the method output shows a clear response to the wear increases.

Modeling and Identification of Wear in a Robot Joint under Temperature Uncertainties

Abstract This paper considers the problem of wear estimation in a standard industrial robot joint. The effects of wear on 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 on 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.

Data-Driven Anomaly Detection based on a Bias Change

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 ...

Simulation Based Evaluation of Fault Detection Algorithms with Applications to Wear Diagnosis in Manipulators

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 limit ...

Observers Data Only Fault Detectio

Most fault detection algorithms are based on residuals, i.e. the difference between a measured signal and the corresponding model based prediction. However, in many more advanced sensors the raw measurements are internally processed before refined information is provided to the user. The contribution of this paper is to study the problem of fault detection when only the state estimate from an observer/Kalman filter is available and not the direct measured quantities. The idea is to look at an extended state space model where the true states and the observer states are combined. This extended model is then used to generate residuals viewing the observer outputs as measurements. Results for fault observability of such extended models are given. The approach is rather straightforward in case the internal structure of the observer is exactly known. For the Kalman filter this corresponds to knowing the observer gain. If this is not the case certain model approximations can be done to generate a simplified model to be used for standard fault detection. The corresponding methods are evaluated on a DC motor example. The next step is a real data robotics demonstrator.