Monitoring, Diagnostics, and Prognostics for Robot Tool Center Accuracy Degradation

Over time, robots degrade because of age and wear, leading to decreased reliability and increased the potential for faults and failures. The effect of faults and failures impacts robot availability. Economic factors motivate facilities and factories to improve maintenance techniques and operations to monitor robot degradation and detect faults, especially to eliminate unexpected shutdowns. Since robot systems are complex, with sub-systems and components, it is challenging to determine these constituent elements’ specific influences on the overall system performance. The development of monitoring, diagnostic, and prognostic technologies, which is collectively known as Prognostics and Health Management (PHM), can aid manufacturers in maintaining the performance of robot systems by providing intelligence to enhance maintenance and control strategies. This paper presents the strategy of integrating top level and component level PHM to detect robot performance degradation (including robot tool center accuracy degradation), supported by the development of four-layer sensing and analysis structure. The top level PHM can quickly detect robot tool center accuracy degradation through the advanced sensing and test methods developed at the National Institute of Standards and Technology (NIST). The component level PHM supports the deep data analysis for root cause diagnostics and prognostics. A reference data set is collected and analyzed using the integration of top level PHM and component level PHM to understand the influence of temperature, speed, and payload on robot’s accuracy degradation. INTRODUCTION Robot systems play important roles in many manufacturing environments including automotive, electronics, consumer packaged goods, and aerospace manufacturing [1, 2]. The applications of robots in manufacturing systems bring benefits through both improving flexibility and reducing costs [3-5]. Robot work cells have become more complex, especially when considering robot-robot and human-robot operations [6-9]. More complexity leads to more sources of faults and failures, which can compromise the efficiency, quality, and productivity of a manufacturing system. Moreover, new innovative technologies are making robot work cells more accurate and intelligent, enabling them to be applied to some new applications [4, 10, 11]. New applications include material removal, high precision assembly, two-side drilling and fastening, in-process inspection, and three-dimensional (3D) composite material layout. New technologies often introduce new types of challenges that may not be fully understood. The afore-mentioned applications require high accuracy in both robot position and path. The degradation of robot tool center accuracy can lead to a decrease in manufacturing quality and production efficiency. It is important to understand robot accuracy degradation so that maintenance and control strategies can be optimized. There are many challenges in developing monitoring, diagnostics, and prognostics for robot tool center accuracy degradation. First, robot tool center accuracy degradation may be difficult to detect, in a timely manner, because the robot may still be operating without any obvious signs of degradation, e.g., the robot being frozen or performing an undesirable activity. Second, with more diverse systems, sub-systems, and components integrated to increase robot work cell capabilities, further challenges are introduced in determining an element’s specific influence(s) on the overall system performance [12, 13]. Third, continuous changes to an existing system give rise to new relationships that may lead to greater complexity. This complexity may include, dynamic robotic configurations (e.g., reconfiguration of the instrument layout and production processes), working parameters (e.g., program changes, temperature, payload, speed, part/grasp changes which causes force and torque changing), controller changes (e.g., control strategy, proportional–integral–derivative (PID) tuning), and worn parts [14]. To address these barriers and challenges,