Development of a Condition Monitoring Algorithm for Industrial Robots based on Artificial Intelligence and Signal Processing Techniques

TLDR

Signal processing is essential for condition monitoring, enabling extraction of fault‑related features from diverse signals such as vibration, with technique choice guided by signal characteristics. The chapter introduces a robot condition‑monitoring algorithm. The algorithm employs time‑domain and discrete wavelet transform analyses, selected after evaluating the advantages and disadvantages of various signal‑processing methods.

Abstract

Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected.