Fault diagnosis of industrial robot gears based on discrete wavelet transform and artificial neural network

Industrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. An unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. The majority of previous research on health monitoring of industrial robots has focused on monitoring a limited number of faults, such as backlash in gears, but does not diagnose other gear and bearing faults. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults that could be progressed in the gears of industrial robot joints, such as gear tooth wear. For accurate fault diagnosis, time-frequency signal analysis based on the discrete wavelet transform (DWT) is adopted to extract the most salient features related to faults and an artificial neural network (ANN) is used for fault classification. A data acquisition system based on National Instruments (NI) software and hardware has been developed for robot vibration analysis and feature extraction. An experimental investigation was carried out using the PUMA 560 robot. Firstly, vibration signals were captured from the robot while it was moving one joint cyclically. Then, by utilising the wavelet transform, signals were decomposed into multi-band frequency levels, starting from higher to lower frequencies. For each of these levels, the standard deviation feature was computed and used to design, train and test the proposed neural network. The developed system has shown high reliability in diagnosing several seeded faults in the robot.