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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 use of anisotropic conductive adhesives (ACA) in flip chip interconnection technology has become very popular because of their numerous advantages. The ACA process can be used in high-density applications and with various substrates as the bonding temperature is lower than that in the soldering process. In this paper, six test lots were assembled using two anisotropic conductive adhesive films (ACF) and four different FR-4 substrates. FR-4 was chosen as it is an interesting alternative for making low-cost high-density interconnections. Some of the chips were thinned to study the effect on reliability. To study the effect of bonding pressure, four different pressures were used in every test lot. The reliability of the assembled test samples was studied in a temperature cycling test carried out between temperatures of −40°C and 125°C for 10 000 cycles. A finite element model (FEM) was used to study the shear stresses in the interconnections during the test. Marked differences between the substrates were seen. The substrate thinning and also the chip thinning increased the reliability of the test samples. From the FEM, it was seen that both decreased the shear stress in the adhesive, which is assumed to be the reason for the increased reliability. A significant difference was seen in the reliability between the ACFs. This was probably caused by differences in the conductive particle materials and the T g values and of the ACFs. In addition, the bump material used with the ACFs varied, which most likely affected the reliability of the test samples.