The optimum selection of wavelet transform parameters for the purpose of fault detection in an industrial robot

Abstract

Industrial robots are commonly used in production systems in order to improve productivity, quality and safety in manufacturing. There are many functions that can be carried out by industrial robots, and they represent the basic building blocks of the production sector. The ability to continuously monitor the status and condition of robots has become an important research issue in recent years and is now receiving considerable attention. Many types of signals can be used for the detection of faults in industrial robots, such as vibrations and acoustic emissions. However, the most important thing is how these signals are processed in appropriate ways in order to extract the most salient features related to specific robot faults. Thus, signal processing step plays a significant role in the fault detection process for any machine and especially for industrial robots. Therefore, the wavelet transform has been utilized in this research for the detection of faults in an industrial robot. In order to build an accurate fault detection system a number of parameters in the wavelet analysis need to be adjusted carefully. The main focus of this research is to discuss the appropriate selection of these parameters, and then to build a fault detection system for the robot based on LabView programming.