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Appl. Sci. 2017, 7(10), 1025; doi:10.3390/app7101025

Fault Diagnosis of a Reconfigurable Crawling–Rolling Robot Based on Support Vector Machines

1
Temasek Lab, Singapore University of Technology and Design, Singapore 487372, Singapore
2
Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, USA
3
Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
4
Department of Robotics and Mechatronics, Tokyo Denki University, Tokyo 120-8551, Japan
*
Authors to whom correspondence should be addressed.
Received: 1 August 2017 / Revised: 11 September 2017 / Accepted: 25 September 2017 / Published: 6 October 2017
(This article belongs to the Section Mechanical Engineering)
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Abstract

As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM)-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU) sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC) and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation. View Full-Text
Keywords: fault diagnosis; machine learning; Support Vector Machines; statistical features; reconfigurable robotics fault diagnosis; machine learning; Support Vector Machines; statistical features; reconfigurable robotics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Elangovan, K.; Krishnasamy Tamilselvam, Y.; Mohan, R.E.; Iwase, M.; Takuma, N.; Wood, K.L. Fault Diagnosis of a Reconfigurable Crawling–Rolling Robot Based on Support Vector Machines. Appl. Sci. 2017, 7, 1025.

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