Remaining Useful Life Estimation for High Speed Industrial Robots Using an Unknown Input Observer for Feature Extraction

In industrial robots, a performance issue is backlash, which is the clearance between mating gears of its joints. Over time, backlash grows through wear and tear, causing inaccuracies in robot positioning. Current methods in backlash detection are performed in low-speed and laboratory settings, or require offline diagnostics. These methods are impractical in actual manufacturing environments, where industrial robots operate continuously at high speeds. Other methods require additional sensors unavailable in typical industrial robots. In this article, we present an online method to quantify backlash and predict the remaining useful life (RUL) in an industrial robot performing cyclic production tasks, using only standard available sensors. To achieve the robot's target position, the input torque oscillates; these oscillations grow as the backlash becomes more severe. We modeled the oscillations as an unknown input, and used an unknown input observer to estimate them and detect/quantify the backlash. Then, a health indicator (HI) is plotted over time and a failure threshold is set based on historical data. Finally, an exponential degradation model is used to predict the RUL of the robot joint. The UIO successfully detected and quantified the backlash through the HI. The degradation model gave a good estimate of the RUL with an accuracy of 20 days after 250 days of operation.

Automated summary

This article presents an online method to quantify backlash and predict the remaining useful life (RUL) in industrial robots using standard available sensors. It models the input torque oscillations and estimates them with an unknown input observer to detect and quantify the backlash. A health indicator (HI) is plotted over time and a failure threshold is set based on historical data. Finally, an exponential degradation model is used to predict the RUL of the robot joint.