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Fault Detection and Diagnosis of Industrial Robot Based on Power Consumption Modeling

Data acquisition; Electric power utilization; Estimation; Fits and tolerances; Industrial robots; Manipulators; Robot applications; Signal encoding; Consumption patterns; Diagnosis techniques; Fault detection and diagnosis; Industrial robotic systems; Neural network classifier; Performance verificat...

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Main Authors: Sabry A.H., Nordin F.H., Abidin Ab Kadir M.Z.
Other Authors: 56602511900
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-252822023-05-29T16:07:50Z Fault Detection and Diagnosis of Industrial Robot Based on Power Consumption Modeling Sabry A.H. Nordin F.H. Sabry A.H. Abidin Ab Kadir M.Z. 56602511900 25930510500 57217008030 25947297000 Data acquisition; Electric power utilization; Estimation; Fits and tolerances; Industrial robots; Manipulators; Robot applications; Signal encoding; Consumption patterns; Diagnosis techniques; Fault detection and diagnosis; Industrial robotic systems; Neural network classifier; Performance verification; Power consumption model; Wireless data acquisitions; Fault detection Fault detection via power consumption monitoring of industrial robots is a substantial problem considered in this article, in which the healthy measurements of power consumption and encoders data for a prespecified task are employed as a reference for comparison to diagnose the potential failures or excessive degradation in the robot joints. Since most electrical and mechanical faults directly affect the consumed energy, the proposed solution analyzes the comparison outcomes between the healthy reference data with that monitored in a real time for each individual task. To integrate the power measurements with a base station, a ZigBee-based wireless data acquisition circuit has been developed to process the joints data. This article suggests a measurement-based mathematical model called Bode equations vector fitting as a robust fitting method to estimate such power consumption patterns. The achieved estimates allow a clear distinction for the potential failures in the robot joints that affect the power rate patterns even when involving sharp fluctuations. A table-based neural network classifier is presented to indicate the faulty joint or encoder according to the time intervals that divided for the executed task. The experimental results demonstrate the performance verification and feasibility of the proposed approach in ABB-IRB-1200 robot manipulator. Note to Practitioners-Industrial machines are seeking to achieve energy optimization to verify the sustainability demand goal. Currently, many industrial robotic systems are not effectively monitored and modeled mathematically toward detecting the potential faults. In this context, a faults diagnosis method with an accurate mathematical model based on reference power patterns is proposed for monitoring the performance of that system. The proposed energy-based diagnosis technique can be readily integrated with the existent industrial robots supply and can be monitored remotely. Furthermore, no significant changes in the machine's hardware, but a reference pattern of a power consumption per each individual task per each robot, are required. � 1982-2012 IEEE. Final 2023-05-29T08:07:50Z 2023-05-29T08:07:50Z 2020 Article 10.1109/TIE.2019.2931511 2-s2.0-85085695938 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085695938&doi=10.1109%2fTIE.2019.2931511&partnerID=40&md5=6443e247083b4a8ececf4926b385754b https://irepository.uniten.edu.my/handle/123456789/25282 67 9 8868116 7929 7940 All Open Access, Hybrid Gold Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Data acquisition; Electric power utilization; Estimation; Fits and tolerances; Industrial robots; Manipulators; Robot applications; Signal encoding; Consumption patterns; Diagnosis techniques; Fault detection and diagnosis; Industrial robotic systems; Neural network classifier; Performance verification; Power consumption model; Wireless data acquisitions; Fault detection
author2 56602511900
author_facet 56602511900
Sabry A.H.
Nordin F.H.
Sabry A.H.
Abidin Ab Kadir M.Z.
format Article
author Sabry A.H.
Nordin F.H.
Sabry A.H.
Abidin Ab Kadir M.Z.
spellingShingle Sabry A.H.
Nordin F.H.
Sabry A.H.
Abidin Ab Kadir M.Z.
Fault Detection and Diagnosis of Industrial Robot Based on Power Consumption Modeling
author_sort Sabry A.H.
title Fault Detection and Diagnosis of Industrial Robot Based on Power Consumption Modeling
title_short Fault Detection and Diagnosis of Industrial Robot Based on Power Consumption Modeling
title_full Fault Detection and Diagnosis of Industrial Robot Based on Power Consumption Modeling
title_fullStr Fault Detection and Diagnosis of Industrial Robot Based on Power Consumption Modeling
title_full_unstemmed Fault Detection and Diagnosis of Industrial Robot Based on Power Consumption Modeling
title_sort fault detection and diagnosis of industrial robot based on power consumption modeling
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806425926201769984
score 13.222552

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