Kari Saarinen

Backlash identification in transmission unit

This work presents a methodology identifying the increase of backlash amount in any type of gearboxes for a movable mechanical unit. No external sensor is required. A dedicated test cycle, of at least 4–8 seconds long, is run regularly on the axis under examination. The motion data, i.e. position, velocity and torque references is logged and stored in a file. The file is later on subject for automatic backlash analysis. The sampled data is analyzed and the so called backlash energy, invented in this work, is calculated and stored in a database. The stored data are trended and the criticality of the eventual backlash enlargement is established using a number of statistical methods for the unit under examination. The proposed method is verified in three different experiments on total 19 (13+5+1) different movable mechanical units and corresponding transmission units with very good results.

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.

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 ...