A Review Robot Fault Diagnosis Part II Qualitative Models and Search Strategies
D. Sivasamy1, M. Dev Anand2, K. Anitha Sheela3

1D. Sivasamy, Assistant Professor, SBMCET, Dindigul (Tamil Nadu), India.
2M. Dev Anand, Professor and 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 18 June 2019 | Revised Manuscript received on 11 July 2019 | Manuscript Published on 17 July 2019 | PP: 977-979 | Volume-8 Issue-1C2 May 2019 | Retrieval Number: A11700581C219/2019©BEIESP
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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: In this part, we review qualitative model representations and search strategies used in industrial robot fault diagnostic systems. Fault diagnosis is increasingly important in modern autonomous or industrial robots. The ability to detect, isolate and tolerate failures allows robots to effectively cope with internal failures and continue performing designated tasks without the need for immediate human intervention. Qualitative models are usually developed based on some fundamental understanding of the applied physical science of the system. Various forms of qualitative models such as causal models and abstraction hierarchies are discussed. The relative advantages and disadvantages of these representations are highlighted. In terms of search strategies, we broadly classify them as topographic and symptomatic search techniques. Topographic searches perform malfunction analysis using a template of normal operation, whereas, symptomatic searches look for symptoms to direct the search to the fault location. Various forms of topographic and symptomatic search strategies are discussed. The important role of robot fault diagnosis in the broader context of operations is also outlined. We also discuss the technical challenges in research and development that need to be addressed for the successful design and implementation of practical new supervisory robot control systems.
Keywords: Fault Diagnosis, Industrial Robot, Hybrid System, Qualitative Models and Search Strategies.
Scope of the Article: Robotics Engineering