Friction is the result of complex interactions between contacting surfaces in a nanoscale perspective. Depending on the application, the dierent 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 aected by other factors than speed. In this work, static friction in robot joints is studied with respect to changes in joint angle, load torque and temperature. The eects of these variables are analyzed by means of experiments on a standard industrial robot. Justied by their signicance, 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. 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 work, 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.

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.

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 Hellinger metric 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.

The ability to accurately predict lithium-ion battery life-time already at an early stage of battery usage is critical for ensuring safe operation, accelerating technology development, and enabling battery second-life applications. Many models are unable to effectively predict battery life-time at early cycles due to the complex and nonlinear degrading behavior of lithiumion batteries. In this study, two hybrid data-driven models, incorporating a traditional linear support vector regression (LSVR) and a Gaussian process regression (GPR), were developed to estimate battery life-time at an early stage, before more severe capacity fading, utilizing a data set of 124 battery cells with lifetimes ranging from 150 to 2300 cycles. Two type of hybrid models, here denoted as A and B, were proposed. For each of the models, we achieved 1.1 % (A) and 1.4 % (B) training error, and similarly, 8.3 % (A) and 8.2 % (B) test error. The two key advantages are that the error percentage is kept below 10 % and that very low error values for the training and test sets were observed when utilizing data from only the first 100 cycles.The proposed method thus appears highly promising for predicting battery life during early cycles.

Modern engineering techniques require the use of more sophisticated and optimized structural design. One way towards this aim is the use of materials which in some way optimize their inherent properties. A concept which suits this purpose very well is composite/sandwich material. Sandwich/composite materials find numerous applications where high strength and low weight are important criteria. The dynamic behavior of a sandwich beam with different boundary conditions has been investigated. Measurements have been carried out. Free-free and clamped-clamped boundary conditions were included. Modal analysis has been used to estimate resonance frequencies and modal parameters. Further, acoustical parameters of a composite plate are measured. An analytical model for prediction of wave propagation constants in sandwich/composite structure is presented. Measured and predicted results are compared.