Research of Feature Extraction and Fault Diagnosis for Sensor Signal

被引：0

作者：

Shan Yu-Gang ^{[1
,2
,3
,5
]}

Hu Wei-Guo ^{[3
]}

Wang Hong ^{[2
]}

Yuan Jie ^{[4
]}

机构：

[1] Chinese Acad Sci, Shenyang Inst Automat, Shenyang 110016, Peoples R China

[2] Microcyber Inc, Shenyang 110179, Peoples R China

[3] Airforce 93303, Shenyang 110043, Peoples R China

[4] Xinjiang Univ, Sch Elect Engn, Urumqi 830047, Peoples R China

[5] Chinese Acad Sci, Grad Sch, Beijing 100039, Peoples R China

来源：

PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE | 2012年

关键词：

Kernel fisher; DAGSVM; Sensor fault; Modulus maxima;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The paper presents an integrated approach of feature extraction and fault diagnosis of sensor. Taking the sensor signal energies and modulus maxima of wavelet packet decomposition in three bands as initial features, valid features are extracted according to kernel fisher transform of initial feature vector, enhancing the signal characteristics. In accordance with inter-class separability a set of binary SVM classifiers are combined to construct optimal Decision Directed Acyclic Graph. The approach is applied to the FDT/DTM device management system, which is used for pressure sensor fault diagnosis in the water cycle control system of NCS4000, a numerical experiment shows that the algorithm is effective.

引用

页码：5412 / 5417

页数：6

共 6 条

[1] FDT-JIG Working Group, 2004, FDT INT SPEC VERS 1
[2] Li Wenjun, 2004, ADV MODELLING ANAL, V41, P71
[3] Mallat S, 1992, WAVELET TRANSFORM MA
[4] Platt JC, 2000, ADV NEUR IN, V12, P547
[5] Bounds on error expectation for support vector machines
Vapnik, V
Chapelle, O
[J]. NEURAL COMPUTATION, 2000, 12 (09) : 2013 - 2036
[6] A wavelet-based approach to abrupt fault detection and diagnosis of sensors
Zhang, JQ
Yan, Y
[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2001, 50 (05) : 1389 - 1396

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