Electromechanical coupling modeling and motor current signature analysis of bolt loosening of industrial robot joint

Xu, Kai; Wang, Dongxiao; Liu, Xiaoqin

doi:10.1016/j.ymssp.2022.109681

Cited by 19 publications

(4 citation statements)

References 23 publications

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“…This paper takes Qianjiang QJR6-1 six-degree-of-freedom tandem industrial robot joint 2 servo system as the research object, see figure 2. The joint 2 transmission system is simplified to an electromechanical coupling model of the 'motor-reducer-load' system [18], as shown in figure 3.…”

Section: Joint Servo Transmission System Dynamics Modelmentioning

confidence: 99%

Vibration characteristics of industrial robot joint servo transmission system based on electromechanical coupling

Hu,

Wang,

Liu

et al. 2023

Meas. Sci. Technol.

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The complex electromechanical coupling effect of an industrial robot's electromechanical system significantly influences the dynamic characteristics and stability of the joint servo transmission system. This study proposes a kind of electromechanical coupling dynamic model for the industrial robot's joint servo transmission system, incorporating multiple parameters, and investigates the vibration response characteristics of the system. Firstly, an electromechanical coupling analysis of the industrial robot's joint servo transmission system is conducted, simplifying it as a "motor-reducer-load" system and establishing the corresponding electromechanical coupling vibration mathematical model. Secondly, a dynamic model of the robot's joint servo transmission system is developed to analyze its speed characteristics. Finally, the model is used to analyze the electromechanical coupling vibration process of the joint servo transmission system with the introduction of mechanical and electrical parameters and other factors to obtain the vibration characteristics of the industrial robot joint servo transmission system under electromechanical coupling. The experimental results show that this model can better analyze the electromechanical coupling vibration characteristics of the joint servo transmission system, which is significant for improving the performance and fault traceability of the industrial robot joint transmission.

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Section: Joint Servo Transmission System Dynamics Modelmentioning

confidence: 99%

Vibration characteristics of industrial robot joint servo transmission system based on electromechanical coupling

Hu,

Wang,

Liu

et al. 2023

Meas. Sci. Technol.

Self Cite

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“…Sensor data that mainly have provided the best results in this context are motor current, motor torque, or vibrations [7], also used sometimes in combination with other system states. For instance, Xu et al developed a detection method for the bolt loosening of industrial robot joints based on electromechanical modeling and motor current signature analysis (MCSA) [8]. Raouf et al in [9] proposed prognostic health management for the robotic rotate vector reducer using external sensors mounted on the robot and managed using an embedded system to electrical MCSA for mechanical fault detection.…”

Section: Related Workmentioning

confidence: 99%

ROS-Based Condition Monitoring Architecture Enabling Automatic Faults Detection in Industrial Collaborative Robots

2022

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The Condition Monitoring (CM) of industrial collaborative robots (cobots) has the potential to decrease downtimes in highly automated production systems. However, in such complex systems, defining a strategy for effective CM and automatically detecting failures is not straightforward. In this paper, common issues related to the application of CM to collaborative manipulators are first introduced, discussed, and then, a solution based on the Robot Operating System (ROS) is proposed. The content of this document is highly oriented towards applied research and the novelty of this work mainly lies in the proposed CM architecture, while the methodology chosen to assess the manipulator’s health is based on previous research content. The CM architecture developed and the relative strategy used to process data are useful for the definition of algorithms for the automatic detection of failures. The approach is based on data labeling and indexing and aims to extract comparable data units to easily detect possible failure. The end of this paper is provided with a proof of concept (PoC) applied to an industrial collaborative manipulator where the proposed CM strategy has been implemented and tested in a real application scenario. Finally, it is shown how the proposed methodology enables the possibility of defining standard Health Indicators (HIs) to detect joint anomalies using torque information even under a highly dynamic and non-stationary environmental conditions.

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“…This technique works on the basis of defects in the mechanical system that drives an induction motor result in a shift in its stator current spectrum and causes load irregularities in the magnetic field. This shifting of stator current spectrum and irregularities in the magnetic field alter the mutual and self-inductance, resulting in side bands across the line frequency [13][14][15][16]. However, it may not be able to highlight the minimal distributed faults or minute variations from the normal working condition running under same loading condition.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of surface roughness level for bearing coating using VMD of vibration signal

Cambow,

Singh

2024

Phys. Scr.

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Degradation in surface finish of the material is an important concern in engineering applications. However, most of the conventional techniques available to monitor the surface roughness requires dismantling of the machine element. Though in rotary components like bearing, it is practically not feasible. Therefore, in this work, an on-board technique is proposed to compare the level of surface roughness based on vibration signature. To demonstrate, five different industrial standard coatings (Nickel, Copper, Zinc phosphate (ZnP), Silver, and Black oxide) were carried out on raw five ball bearings (NBC6205). Two sets of coated bearing were prepared, where first set was utilized for experimentation and second for the purpose of measuring surface roughness of bearing surface. These coated bearings were tested at five different RPMs ranging from 300 RPM to 1500 RPM and their vibration signals were recorded. The recorded vibration signals must be having characteristics originated from ball rolling on different level of surface roughness and hence distributed in nature. Further, to target distributed characteristics present in the signal, commonly used statistical parameters for vibration signature analysis (RMS, Crest factor, Variance, Skewness, Kurtosis, Shannon entropy and Log energy) were calculated. Then, correlation of the parameters was checked in relation to the different levels of surface roughness, but no relation was found. The signals were then decomposed into six intrinsic mode functions (IMFs) using Variational Mode Decomposition (VMD) method. Again, same statistical parameters were calculated for these decomposed levels, it has been noted that Shannon entropy have shown correlation to surface roughness in at least one decomposed level between 900 to 1500 RPM with minimum value of chain index as 176.65. Moreover, in this RPM range, responsive frequency bands found to be shifted towards higher side i.e. from 567 to 4590 Hz.

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Electromechanical coupling modeling and motor current signature analysis of bolt loosening of industrial robot joint

Cited by 19 publications

References 23 publications

Vibration characteristics of industrial robot joint servo transmission system based on electromechanical coupling

Vibration characteristics of industrial robot joint servo transmission system based on electromechanical coupling

ROS-Based Condition Monitoring Architecture Enabling Automatic Faults Detection in Industrial Collaborative Robots

Comparative analysis of surface roughness level for bearing coating using VMD of vibration signal

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