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Design of an Intelligent Embedded System for Condition Monitoring of an Industrial Robot

Part of the book series: Springer Theses ((Springer Theses))

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Abstract

The thesis title reflects a number of themes, i.e. intelligent system, embedded system, condition monitoring and industrial robot, and previous and recent research in each theme needs to be studied thoroughly; however, there has not been much researches in the field of robot condition monitoring. Thus the purpose of this chapter is to evaluate the condition monitoring methods that have been developed for different machinery with a view to applying the most appropriate one to robots and collectively helps to establish if there is a gap in the area of industrial robot condition monitoring. This chapter will give and describe in its first two sections the necessary background information about the various condition monitoring approaches and techniques. Statistical and Artificial intelligence techniques, such as artificial neural networks (ANN), fuzzy logic system (FLS), genetic algorithm (GA), and support vector machine (SVM), which can be applied to address the issues of fault detection and diagnosis, will also be reviewed. Then, the principle of embedded systems and their application in condition monitoring is reviewed in the third section. The last section of this chapter will discuss the work done in robotics health monitoring and finally the research gap is addressed in the summary section.

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  1. School of Mechanical and Systems Engineering, Newcastle University, Newcastle upon Tyne, UK

    Alaa Abdulhady Jaber

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  1. Alaa Abdulhady Jaber
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Correspondence to Alaa Abdulhady Jaber .

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Jaber, A.A. (2017). Literature Review. In: Design of an Intelligent Embedded System for Condition Monitoring of an Industrial Robot. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-44932-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-44932-6_2

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