Intelligence Decision Making of Fault Detection and Fault Tolerances Method for Industrial Robotic Manipulators
D. Sivasamy1, M. Dev Anand2, K. Anitha Sheela3
1D. Sivasamy, External Research Scholar, Department of ECE, Jawaharlal Nehru Technological University, Hyderabad (Telangana), India.
2M. Dev Anand, Professor & Research Director, Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, Kanyakumari (Tamil Nadu), India.
3K. Anitha Sheela, Professor & Head, Department of ECE, Jawaharlal Nehru Technological University, Hyderabad (Telangana), India.
Manuscript received on 16 July 2019 | Revised Manuscript received on 01 August 2019 | Manuscript Published on 10 August 2019 | PP: 17-24 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B10040782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1004.0782S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: FD methods are usually based on the residual generation and analysis concept. A mathematical model is used to reproduce the dynamic behavior of the fault-free system; the deviation of the output predicted by the model from actual output measurements forms the so-called residuals. Which, when properly analyzed, provides valuable information about failure. Based on the failure an intelligent decision is taken with the help of the neuro fuzzy fault diagnosis system. The main aim of this work is the introduction of a new algorithm for robots fault detection which forms part of a proposed intelligent decision making framework for fault tolerance in robotic manipulator. In developing the model, this work explores the affects of failures in an example robot using a technique called Neuro-Fuzzy Approach. The robot components critical to fault detection are revealed using a Neuro-Fuzzy (NF) approach. To evaluate our NF based fault detection and tolerance method we performed an extensive simulation study with a Scorbot ER 5u plus robot manipulator. In this research work we considered all faults possible to occur in robot manipulator. The Scorbot ER 5u plus model was developing in robotics toolbox for MATLAB using the NF algorithms.
Keywords: Intelligence Method Industrial Robotic Fuzzy Framework.
Scope of the Article: Industrial Engineering