Title :
Signal extraction and fault identification of steam turbine vibration
Author :
Junru Gao ; Xin Meng ; Yajun Sun
Author_Institution :
Hebei Univ. of Eng. Handan, Handan, China
Abstract :
This paper, the vibration signal of steam turbine which are detected by fault diagnosis are influenced by environmental noise and detecting instrument itself, leading to vibration signal waveform distortion which contains a large number of non-stationary composition, and cannot effectively react turbine fault characteristics, and the coupling among different fault characteristics of unilateral fault features make it difficult to identify fault accurately. Aiming at solving this problem, this paper combine the axis of spectrum analysis with path analysis of vibration signal processing and recognition method, two kinds of detection method in the fault diagnosis process validation to ensure the accuracy of test results.
Keywords :
distortion; fault diagnosis; mechanical engineering computing; signal detection; spectral analysis; steam turbines; vibrations; environmental noise; fault diagnosis; fault identification; nonstationary composition; path analysis; signal detection method; signal extraction; spectrum analysis; steam turbine vibration signal; turbine fault characteristics; unilateral fault features; vibration signal processing; vibration signal recognition; vibration signal waveform distortion; Fault diagnosis; Feature extraction; Rotors; Turbines; Vibrations; Wavelet analysis; Wavelet packets; Axis trajectory; Fault diagnosis; Spectrum; Steam turbine unit; vibration signal;
Conference_Titel :
Software Engineering and Service Science (ICSESS), 2014 5th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-3278-8
DOI :
10.1109/ICSESS.2014.6933610