Signal extraction and fault identification of steam turbine vibration

Gao, Junru; Meng, Xin; Sun, Yichuang

doi:10.1109/icsess.2014.6933610

Cited by 2 publications

(1 citation statement)

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“…Ridge reconstruction techniques are defined in the literature. 19,[37][38][39] To detect the ridges from wavelet SST, penalized forward-backward greedy algorithm is adapted. Local maxima is identified by minimizing cost function À lnðabsðsstðiÞÞ where sst(i) is the TFR at the instant i.…”

Section: Methodsmentioning

confidence: 99%

Adaptive data-driven nonlinear synchro squeezed transform with single class radial basis function kernel support vector machine applied to wind turbine planetary gearbox fault diagnostics

Maheswari

Umamaheswari²

2019

Proceedings of the Institution of Mechanical Engineers, Part A:

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Planetary stage gears operated at low rotational speed and varying wind speed result variation in load. Variable speed and variable load induce nonstationary operating conditions. Vibration signal measured from Wind power gear transmission systems are embedded with multiple sources of vibration and attenuated considerably as it travels from source of vibration to measuring point. Efficacious multi-component decomposition without mode mixing ensures the accurate fault signature recognition. Synchro squeezing transform is the promising tool that represents the ridges with high resolution in time as well as in frequency axis. An efficient vibration analysis technique, short windowed Fourier synchro squeezing transform with nonlinear radial basis function kernel support vector machine is proposed to detect the mechanical faults in low speed planetary stage of wind turbines. Raw vibration is modeled in time–frequency plane to extract fault pattern signatures effectively with high resolution by adapting an empirical nonlinear tool synchro squeezing transforms. Amplitude modulation and frequency modulation parameters are sculpted from instantaneous amplitude and instantaneous phase, frequency. Hybrid feature space with signal attributes, statistical moments, and randomness measures are extricated from amplitude modulation-frequency modulation components. Single class radial basis function support vector machine is trained with hybrid features. The fault detection accuracy of the proposed method is compared with the standard variants of empirical mode decomposition. The proposed short windowed Fourier synchro squeezing transform-radial basis function kernel support vector machine shows 98.2% accuracy, 98% sensitivity, and 98% specificity.

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Section: Methodsmentioning

confidence: 99%

Adaptive data-driven nonlinear synchro squeezed transform with single class radial basis function kernel support vector machine applied to wind turbine planetary gearbox fault diagnostics

Maheswari

Umamaheswari²

2019

Proceedings of the Institution of Mechanical Engineers, Part A:

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Study on Early Warning Method of Cylinder Block's Off-Center Position and Swing Failure for 50 MW Steam Turbine

Shi

Yao

et al. 2020

西北工业大学学报

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Due to the special structure of the cylinder block, there is an off-center position and swing fault in the process of start-up and operation of the 50 MW extraction unit. Moreover, the lack of effective monitoring and early warning means seriously affects the safety of the unit operation. Therefore, it is very important to forewarn the fault of cylinder off-center position and swing. First of all, through the design of cylinder block offset amplifying mechanism for fault monitoring, the data of eccentric swing required for establishing mathematical model is obtained. Then, neural network is selected for data-driven modeling, two time series prediction models are obtained, and the influence of input and output parameters on the prediction accuracy is studied. Finally, by selecting reasonable early warning value and decision rules, an effective early warning of off-center position and swing fault is realized, and a monitoring device for real-time monitoring and fault early warning is developed. The actual application effect shows that this early warning method has important engineering value to avoid equipment damage caused by the swing fault for 50 MW unit.

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Signal extraction and fault identification of steam turbine vibration

Cited by 2 publications

References 0 publications

Adaptive data-driven nonlinear synchro squeezed transform with single class radial basis function kernel support vector machine applied to wind turbine planetary gearbox fault diagnostics

Adaptive data-driven nonlinear synchro squeezed transform with single class radial basis function kernel support vector machine applied to wind turbine planetary gearbox fault diagnostics

Study on Early Warning Method of Cylinder Block's Off-Center Position and Swing Failure for 50 MW Steam Turbine

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