Search

The focus of this study is development of an intelligent fault detection, diagnosis and health evaluation system for real industrial robots. The system uses principal component analysis based statistical process control with Nelson rules for online fault detection. Several suitable Nelson rules are chosen for sensitive detection. When a variation is detected, the system performs a diagnostic operation to acquire features of the time domain and the frequency domain from the motor encoder, motor current sensor and external accelerometer for fault diagnosis with a multi-class support vector machine. Additionally, a fuzzy logic based robot health index generator is proposed for evaluating the health of the robot, and the generator is an original design to reflect the health status of the robot. Finally, several real aging-related faults are implemented on a six-axis industrial robot, DRV90L7A6213N by Delta Electronics, and the proposed system is validated effectively by the experimental results.

SUMMARYThe variable stiffness actuator (VSA) is helpful to realize the post-collision safety strategies for safe human–robot interaction.1 The stiffness of the robot will be reduced to protect the user from injury when the collision between the robot and human occurs. However, The VSA has a mechanism limit constraint that can cause harm to users even if the stiffness is minimized. Accordingly, in this article, a concept combining danger index and robust fault detection and isolation is presented and applied to active–passive variable stiffness elastic actuator (APVSEA). APVSEA can actively change joint stiffness with the change of danger index. Experimental results show that this concept can effectively confirm the fault mode and provide additional protection measures to ensure the safety of users when the joint stiffness has been adjusted to the minimum.

Process capability analysis is conducted assuming that the process under study is in statistical control and independent observations are generated over time. However, in practice it is very common to come across process which, due to their inherent natures, generate autocorrelated observations. An approach that has proved useful in dealing with autocorrelated data is to directly model the correlative structure with an appropriate time series model, use that model to remove the autocorrelation from the data. The object of this paper was to generating the tables, which show the lower and upper 90%, 95% and 99% confidence limits for Cp and Cpk, for various level of confidence and autocorrelated. We also will provide tables for the engineers/practitioner to use for their in-plant applications.

Broadcasting is an often seen operation in the wireless network environment. In an ad hoc wireless network, because of the mobility of each host, it is not an easy job for every mobile host to receive the broadcast message. Applications such as current time synchronisation, paging, finding the route to a particular destination, etc., all need to broadcast frequently. A simple broadcasting strategy is flooding. However, flooding leads to many redundant broadcasts and makes poor utilization of bandwidth. We try to improve the performance of broadcasting in an ad hoc wireless network. We propose several methods to reduce the redundancy of rebroadcasting the same message and increase the reachability.