Fault Diagnosis Analysis in Large-Scale Computing Environments

2010 ◽  
Author(s):  
Yan Xue ◽  
Xuefang Zhu
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
Computing Environments ◽  
Large Scale Computing

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

Energy-Aware Accounting and Billing in Large-Scale Computing Facilities

IEEE Micro ◽  
2011 ◽  
Vol 31 (3) ◽  
pp. 60-71 ◽  
Author(s):  
Victor Jimenez ◽  
Francisco Cazorla ◽  
Roberto Gioiosa ◽  
Eren Kursun ◽  
Canturk Isci ◽  
...  
Keyword(s):  
Large Scale ◽  
Energy Aware ◽  
Large Scale Computing

Investigating the Use of Large-Scale Immersive Computing Environments in Collaborative Design

2014 ◽  
Author(s):  
Meisha Rosenberg ◽  
Judy M. Vance
Keyword(s):  
Design Process ◽  
Collaborative Design ◽  
Large Scale ◽  
Design Teams ◽  
Team Members ◽  
3D Cad ◽  
Cad Models ◽  
Computing Environments ◽  
Design Ideas ◽  
Human Motions

Successful collaborative design requires in-depth communication between experts from different disciplines. Many design decisions are made based on a shared mental model and understanding of key features and functions before the first prototype is built. Large-Scale Immersive Computing Environments (LSICEs) provide the opportunity for teams of experts to view and interact with 3D CAD models using natural human motions to explore potential design configurations. This paper presents the results of a class exercise where student design teams used an LSICE to examine their design ideas and make decisions during the design process. The goal of this research is to gain an understanding of (1) whether the decisions made by the students are improved by full-scale visualizations of their designs in LSICEs, (2) how the use of LSICEs affect the communication of students with collaborators and clients, and (3) how the interaction methods provided in LSICEs affect the design process. The results of this research indicate that the use of LSICEs improves communication among design team members.

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