Energy consumption modeling of industrial robot based on simulated power data and parameter identification

Liu, Aiming; Liu, Huan; Yao, Bitao; Xu, Wenjun; Yang, Ming

doi:10.1177/1687814018773852

Cited by 48 publications

(39 citation statements)

References 20 publications

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“…For the purpose of simplification, we assume that RAs can save energy when they take a longer time to accomplish tasks. Note that this relationship can be true in specific regions of operation for both industrial robots [61] and manufacturing tools [62], but does not necessarily hold for all parameters and tasks. Assuming that the speed of operation and the energy cost are inversely proportional for Robot 2, the energy cost for moving to B2 is evaluated to the following: and…”

Section: Decision Making Modelmentioning

confidence: 99%

Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

Kovalenko¹,

Balta²,

Tilbury³

et al. 2021

Preprint

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Due to the advancements in manufacturing system technology and the ever-increasing demand for personalized products, there is a growing desire to improve the flexibility of manufacturing systems. Multi-agent control is one strategy that has been proposed to address this challenge. The multi-agent control strategy relies on the decision making and cooperation of a number of intelligent software agents to control and coordinate various components on the shop floor. One of the most important agents for this control strategy is the product agent, which is the decision maker for a single part in the manufacturing system. To improve the flexibility and adaptability of the product agent and its control strategy, this work proposes a direct and active cooperation framework for the product agent. The directly and actively cooperating product agent can identify and actively negotiate scheduling constraints with other agents in the system. A new modeling formalism, based on priced timed automata, and an optimization-based decision making strategy are proposed as part of the framework. Two simulation case studies showcase how direct and active cooperation can be used to improve the flexibility and performance of manufacturing systems.

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Section: Decision Making Modelmentioning

confidence: 99%

Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

Kovalenko¹,

Balta²,

Tilbury³

et al. 2021

Preprint

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show abstract

“…Commonly, there are a variety of components in robotic systems that consume energy, for instance, controllers, fans for the cooling, motors, and the joint's friction of the robot. In [7], the motor characteristics were analyzed by modeling the energy consumption of the ABB IRB 1200 robot based on a method of parameters identification from the power data rather than joint torque.…”

Section: An Industrial Robot Is Playing the Most Important Role And Itsmentioning

confidence: 99%

“…The power losses are represented by the second term of the E elec equation (RI 2 ). However, one study [7] took the efficiency μ i of the motor in consideration when modeling the energy consumption of such a system, which is given by…”

Section: B System Modeling and Faultsmentioning

confidence: 99%

“…Pa includes the power consumption for an air compressor of the pneumatic mechanism, cooling fans, and the computer PC as a control and monitoring station. Auxiliary components consumption is usually constant [7], [26]. The control system power comprises the losses due to ac-dc converters, dc bus, inverters, etc., and the power supply of driving the robot motors.…”

Section: B System Modeling and Faultsmentioning

confidence: 99%

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

Sabry

Nordin

Sabry

et al. 2020

IEEE Trans. Ind. Electron.

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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.

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“…Energy effectiveness of a production processes is a part of a machines building and exploitation discipline, whose significance was already noticed and is constantly being developed [3,4,9,10,11]. Authors of this paper focused an attention in their work on all factors, having an influence on the final electrical energy, consumed by the manipulation and transportation machines and particularly consumed by industrial robots.…”

Section: Introductionmentioning

confidence: 99%

Power losses and their properties for low range of a robot electric motor working conditions as the part of energy effectiveness research

Świder¹,

Zbilski²

2018

Eksploatacja i Niezawodność – Maintenance and Reliability

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Energy consumption modeling of industrial robot based on simulated power data and parameter identification

Cited by 48 publications

References 20 publications

Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

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

Power losses and their properties for low range of a robot electric motor working conditions as the part of energy effectiveness research

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