首页|New Robotics Study Results from University of Tennessee at Knoxville Described ( A Review of Robotic Arm Joint Motors and Online Health Monitoring Techniques)
New Robotics Study Results from University of Tennessee at Knoxville Described ( A Review of Robotic Arm Joint Motors and Online Health Monitoring Techniques)
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Investigators publish new report on ro botics. According to news reporting originating from Knoxville, Tennessee, by Ne wsRx correspondents, research stated, "The employment of robots in numerous emer ging applications, e.g., disaster rescue, nuclear waste remediation, and space e xploration, is of paramount importance due to their improved safety, flexibility , and productivity. Due to the harsh environmental conditions, the robotic arm j oint motors and power electronic drives are vulnerable to electrical faults and mainly contribute to joint failures." Funders for this research include U.S. National Science Foundation. Our news editors obtained a quote from the research from University of Tennessee at Knoxville: "To substantially improve the reliability and robustness of the r obot arms utilized in remote, hazardous, and safety-critical environments, auton omous fault-tolerant and fail-active operation for these robotic arms experienci ng joint failures should be developed. In the literature, many strategies have b een proposed for fault prognosis, diagnosis, and health monitoring of electric m otors and drives using online data analytics of the fault signature information. Thus, this paper presents an extensive up-to-date review of joint motor types, common fault types, and robot joint fault prognostics, diagnostics, and health m anagement. First, various joint motors are introduced and compared, considering their performance advantages, disadvantages, and wide applications. Furthermore, joint motors for collaborative robotic applications are summarized and compared as illustrative examples. After that, fault types are reviewed with a further c lassification by fault locations, namely, stator windings, rotors, and bearings. In addition, health monitoring techniques are classified into methods for stato r winding, rotor, and bearing faults."
University of Tennessee at KnoxvilleKn oxvilleTennesseeUnited StatesNorth and Central AmericaEmerging Technolog iesMachine LearningRobotRoboticsRobots
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