Airborne Oxygen-Making System Drift Oxygen Sensor Characteristics for Fault Diagnosis Strategy

Xiang Yan Luo; Jun Bin Cao; Jun Qing Cao

doi:10.4028/www.scientific.net/AMM.347-350.371

Paper Titles

Multi-Temporal Remote Sensing Dynamic Monitoring and Driving Force Analysis in Yangzhou Urban Area Based on Object-Oriented Technology
p.352

Research on Assist Characteristic of Electric Power Steering System Based on Adaptive Neuro-Fuzzy Control
p.357

The Application of Computer Technology in Project Management
p.362

Fault Diagnosis of Engine Ignition System Based on the Optimized BP Neural Networks by Improved Particle Swarm Algorithm
p.366

Airborne Oxygen-Making System Drift Oxygen Sensor Characteristics for Fault Diagnosis Strategy
p.371

A Two-Screw Squirm Micro-Robot for Gastrointestine: Design and Experiments
p.376

Novel Analysis Method Based on STFT for Muzzle Velocity Measurement of High Firing Rate Artillery
p.383

Fault Diagnosis Technology Research on Radar Equipment Based on Sensor Data Validation and Detection Fusion
p.387

Simulation Study of SVPWM Control Technology for NPC Inverter
p.392

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 347-350Airborne Oxygen-Making System Drift Oxygen Sensor...

Airborne Oxygen-Making System Drift Oxygen Sensor Characteristics for Fault Diagnosis Strategy

Abstract:

This paper focuses on airborne oxygen-making system shortcomings of oxygen sensor characteristic drift in, proposes a method of fault diagnosis. Oxygen sensor with a Wavelet packet analysis of feature extraction, based on wavelet neural network method to determine whether the sensor has failed, and sensor to detect hardware and software design are given.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 347-350)

Pages:

371-375

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.347-350.371

Citation:

Cite this paper

Online since:

August 2013

Authors:

Xiang Yan Luo, Jun Bin Cao, Jun Qing Cao

Keywords:

Airborne Oxygen-Making System, Oxygen Sensor (EGO), Wavelet Packet Decomposition (WPD)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] XU Tao, Wang Qi. Methodology for Sensor Fsult Diagnosis Based on Wavelet Package and Neural Network[J]. Chinese Journal of Sensors and Actuators. 2006(8) 1060-1064.

Google Scholar

[2] Gou T H, Nurre J. Sensor Detection and Recovery by Neural Network[C]/IEEE International Joint Conference on Netural Networks Seattle, WA USA. July 1991. 221-226.

Google Scholar

[3] Chen Chun-yan. Application of Improved Wavelet Netural in Fault Diagnosis of Transformer[J]. Bulletion Of Science And Technology. 2012(10)31-33.

Google Scholar

[4] Fu Huixuan，Zhao Hong，et al. Application design of MATLAB neural[M]. China Machine Press, 2010. (in Chinese). 51-63.

Google Scholar

[5] ANYAKORA S N, LEES F P. Detection of instrument malfunction by process operator[J]. Chem Eng, 1972, 264: 304-309.

Google Scholar

[6] Yang Yum-yu, Du Zhi-min, Jin Xin-qiao. W NN Based Drift Bias Fault Detection Method for Sensors in[J]. Ah'-Conditioning Systems Energy Technology. 2008(29) 28-34.

Google Scholar