<p>The steam turbine rotor is still the main power generation equipment. Affected by the impact of new energy on the power grid, the steam turbine needs to participate in peak load regulation, which will make turbine rotor components more prone to failure. The rotor is an important equipment of a steam turbine. Unbalance and misalignment are the normality of rotor failure. In the last decade, more and more attention has been paid to the fault detection method based on deep learning, which takes rotating machinery as the object. However, there is a lack of research on actual steam turbine rotors. In this paper, a method of rotor unbalance and misalignment fault detection based on residual network is proposed, which realizes the end-to-end fault detection of rotor. Meanwhile, the method is evaluated with high-precision numerical simulation data, and the multi task detection of rotor unbalance, misalignment and coupling faults is realized. The influence of signal-to-noise ratio and the training size on the detection performance of neural network is discussed. The detection accuracy of unbalanced position is 93.5%, that of misalignment is 99.1%, that of unbalance and misalignment is 89.1% and 99.1%, respectively. The method can realize the direct mapping between the unbalanced and misalignment coupling fault vibration signals and the fault detection results. The method has the ability to automatically extract fault features. It overcomes the shortcoming of traditional methods that rely on signal processing experience, and has the characteristics of high precision and strong robustness.</p>

Research on Coupled Fault Detection of Steam Turbine Rotor Unbalance and Misalignment Based on Numerical Simulation and Convolutional Neural Network

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2021/volume/84898

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