Retraction type (multiple responses allowed):  Unreliable findings   Lab error  Inconsistent data  Analytical error  Biased interpretation   Other:  Irreproducible results  Failure to disclose a major competing interest likely to influence interpretations or recommendations  Unethical research   Fraud   Data fabrication  Fake publication  Other: X Plagiarism  Self plagiarism  Overlap  Redundant publication *  Copyright infringement  Other legal concern:  Editorial reasons   Handling error  Unreliable review(s)  Decision error  Other:  Other: Results of publication (only one response allowed):  are still valid. Xwere found to be overall invalid.

Robot manipulator play important role in the field of automobile industry, mainly it is used in gas welding application and manufacturing and assembling of motor parts. In complex trajectory, on each joint the speed of the robot manipulator is affected. For that reason, it is necessary to analyze the noise and vibration of robot's joints for predicting faults also improve the control precision of robotic manipulator. In this study we will propose a new fault detection system for Robot manipulator. The proposed hybrid fault detection system is designed based on fuzzy support vector machine and Artificial Neural Networks (ANNs). In this system the decouple joints are identified and corrected using fuzzy SVM, here non-linear signal are used for complete process and treatment, the Artificial Neural Networks (ANNs) are used to detect the free-swinging and locked joint of the robot, two types of neural predictors are also employed in the proposed adaptive neural network structure. The simulation results of a hybrid controller demonstrate the feasibility and performance of the methodology.