Robot Condition Monitoring and Production Simulation

Abstract: The automated industry is in a growing phase and the human tasks is increasingly replaced by robots and other automation solutions. The increasing industry entails that the automations must be reliable and condition monitoring plays an important role in achieving that ambition. By utilizing condition monitoring of a machine it is possible to detect a wear before it turns into a critical damage that could result in complete failure. A useful tool when monitoring the condition of a machine is by sampling and analyzing vibrations. Vibrations are generated by the moving parts of the machinery and high amplitude vibrations can often be seen as an indication of the developed faults. The frequency of these vibrations can be calculated and then detected in the sampled data. Today there is no condition monitoring system that monitor industrial robots by analyzing vibrations. The problem with analyzing robots, is that they operate with a varying speed. Since the running conditions are changing rapidly all the time, this means that the vibration frequencies also changes constantly. This is due to the fact that the vibration frequencies are dependent and affected of the operation speed. This research is a sequel and continuation of a research from previous year. The purpose of the research is to investigate the possibility to monitor the condition of a gearbox in a industrial robot, by utilizing vibration analysis. The robot that has been tested under tuff conditions in order to reach a failure, is an ABB IRB 6600. To sample data in a stationary way even tough the speed is changing during the sample time, the method order tracking has been utilized. This makes it possible to sample data with numbers of measurement per rotation instead of sampling according to time. This is processed by SKF:s condition monitoring system multilog IMx and the signal is then presented as a time waveform in the software @ptitude Observer. In Observer, it is also possible to show the signal in a spectrum by using Fast Fourier Transform. By utilizing MATLAB, the research has also resulted in a new analyzing method. This method is called Spectral Auto-Correlation. The methodology of this practice is to correlated the time waveform with itself in order to see which frequencies that are reappearing. The correlated result is then calculated with a Fast Fourier Transform to illustrate the signal in a spectrum for further analysis. During the analysis of the parts in the gearbox, critical defects were found on both the cycloidal disks. The fault frequency for the defects were calculated and analyzed from the data. This resulted in trends where the amplitude from the fault frequency had more than doubled over the time the robot has been operating in the project. This report also include a production simulation where a robot cell from SKF is simulated. The robot cell is simulated with and without a condition monitoring system. A comparison was then made to see what advantages there were with utilizing a condition monitoring system. The result of the simulation was an increased productivity with two to three percent.