Lube Intelligent Diagnosis System Combining Bayesian and BP Network Based on IOT Technology

Tie Jun Pan; Lei Na Zheng; Cheng Qing Li

doi:10.4028/www.scientific.net/AMR.490-495.1014

Paper Titles

Mobile Robot Dynamic Path Planning Based on Artificial Potential Field Approach
p.994

Feed-Forward Compensated PD Controller and PD Robust Controller Simulation Comparison on Uncertain Single-Manipulator
p.999

LabVIEW-Based Parallel Optimization of Networked Virtual Test System in Multi-Core Processor Environment
p.1004

Modal Parameter Simulation and Model Updating for some Launch Platform
p.1009

Lube Intelligent Diagnosis System Combining Bayesian and BP Network Based on IOT Technology
p.1014

Application of Simulation Technology in MCU System Development
p.1019

Experimental Study on Physical Properties of Fired Perforated Brick
p.1024

Intelligent Fault Diagnosis Method Based on Vector-Bispectrum and SVDD
p.1029

Implementation and Analysis of the File System Based on Metadata Dynamic Hashing
p.1034

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 490-495Lube Intelligent Diagnosis System Combining...

Lube Intelligent Diagnosis System Combining Bayesian and BP Network Based on IOT Technology

Abstract:

The existing industrial lubrication depend on experience judgment, off-line inspection and regular oil change, whose maintenance requires rich personnel experience and still always have many errors. Line monitoring and quality diagnosis for industrial lube were studied to establish the distributed the online monitoring system based on hierarchical structure, information fusion diagnostic system based on Bayesian network and BP neural network. The filtering system for industrial lube has been developed to achieve unattended, automatic operation purposes, and trialed in the metallurgical industry. The results show monitoring data is stable, reliable, and the problem of high water content of lube in the steel industry is solved. At the same time, lube filtering is transformed from the traditional blind continuous filtering to real-time targeted filtering. In the premise of guaranteeing the lube quality, the system can save electricity more than 30%.

You might also be interested in these eBooks

Mechatronics and Intelligent Materials II

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 490-495)

Pages:

1014-1018

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.490-495.1014

Citation:

Cite this paper

Online since:

March 2012

Authors:

Tie Jun Pan, Lei Na Zheng, Cheng Qing Li

Keywords:

Industrial Lube, Information Fusion, Internet of Things (IOT), Line Monitoring

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] E. Jantunen, A. Adgar, C. Emmanouilidis: NEXT GENERATION MAINTENANCE THROUGH THE ADOPTION OF EMAINTENANCE, Proceedings of the original from Proceedings of the Euro maintenance Conference (2010), p.1.

Google Scholar

[2] T. Pyne, K. Umar Pathak, V. Raj: Reliability improvement of process equipment: A case of rolling mill application, Reliability, Safety and Hazard (ICRESH), (2010), p.335.

DOI: 10.1109/icresh.2010.5779571

Google Scholar

[3] W. Fan: Design about Monitoring and Diagnosis System Structure of Rotating Machinery Based on LabVIEW, Advanced Materials Research, Vol. 383-390 (2010), p.1792.

DOI: 10.4028/www.scientific.net/amr.383-390.1792

Google Scholar

[4] Y. Zhang, J. Mao, Y. Xie: Engine Wear Monitoring with OLVF, Tribology Transactions, Vol. 54 (2011), p.201.

DOI: 10.1080/10402004.2010.534838

Google Scholar