Abstract
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 the previous research on industrial robots health monitoring is focused on monitoring of a limited number of faults, such as backlash in gears, but does not diagnose the 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 bearings of industrial robot joints, such as inner/outer race bearing faults, using vibration signal analysis. 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 the artificial neural network (ANN) is used for faults classification. A data acquisition system based on National Instruments (NI) software and hardware was developed for robot vibration analysis and feature extraction. An experimental investigation was accomplished using the PUMA 560 robot. Firstly, vibration signals are captured from the robot when it is moving one joint cyclically. Then, by utilising the wavelet transform, signals are decomposed into multi-band frequency levels starting from higher to lower frequencies. For each of these levels the standard deviation feature is computed and used to design, train and test the proposed neural network. The developed system has showed high reliability in diagnosing several seeded faults in the robot.
Highlights
IntroductionAvailability and maintainability, which can be defined as the probability of a system operating satisfactorily in any time period and its capability of being repaired, are critical for industrial robots
The Robot Institute of America (RIA) has defined an industrial robot as a reprogrammable multifunctional manipulator designed to move material, parts, tools, or specialized devices through variable programmed motions for the performance of a variety of tasks (Spong et al, 2005)
There are two approaches to condition monitoring, which are model-based and model-free. Either of these approaches or a combination of both could be adopted in industrial robot condition monitoring
Summary
Availability and maintainability, which can be defined as the probability of a system operating satisfactorily in any time period and its capability of being repaired, are critical for industrial robots. Industrial robots are extremely complex mechanism and the application of condition monitoring for them differs from that of ‘simple’ rotating machinery. This is basically due to the instantaneous change of geometrical configuration of the robot arm. There are two approaches to condition monitoring, which are model-based and model-free. Either of these approaches or a combination of both could be adopted in industrial robot condition monitoring.
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