Abstract
Industrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. There are significant implications for operator safety in the event of a robot malfunction or failure and 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. In this research a fault detection system based on statistical control chart has been designed. An experimental investigation was accomplished using the PUMA 560 robot. Vibration signals are captured from the robot when it executes a repetitive task and then some statistical features are extracted from the signals, by utilising a developed data acquisition system based on National instruments hardware and software. The extracted vibration features, which are related to the robot healthy and faulty states, have subsequently been used for building and testing a statistical control chart. The chart has been validated using part of the measured data set, not used within the design stage, which represents the robot operating conditions. Validation results indicate the successful detection of faults at the early stages using the key extracted parameters.
Highlights
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)
If the points appear in a curvature shape, the indication is that the data are not normally distributed. To achieve this and to calculate the upper and lower control limits, as will be explained later, the Minitab 17 statistical package has been used and the result is shown in Fig. 6; the histogram is indicating a bell-shape distribution and the data in the normal probability plot looks reasonably straight, meaning that the resultant Standard Deviation (STD) is normally distributed, it will be used for designing the control chart
The red colour markers indicate out-of-control samples. From these figures significant differences amongst the backlash levels compared to the healthy state of the robot can be noticed, on the means of the standard deviation ( x ) in the X-bar chart, these differences cannot be distinguish clearly in standard deviation chart (S-chart) graph which monitor the variability of the standard deviation
Summary
The STD results when the backlash fault was simulated and even with the other fault types have shown that the X-axis vibration is the best to be utilized for monitoring the effect of fault development in the robot, as a clear designation can be recognized amongst the different faults. To achieve this and to calculate the upper and lower control limits, as will be explained later, the Minitab 17 statistical package has been used and the result is shown in Fig. 6; the histogram (left) is indicating a bell-shape distribution and the data in the normal probability plot (right) looks reasonably straight, meaning that the resultant STD is normally distributed, it will be used for designing the control chart.
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