Search

Blade tip timing (BTT) has become the most promising online monitoring method for turbine blade health. Most existing BTT data analysis methods have a narrow application scope for different types of BTT data and slow calculation speed, which adversely affects the practical industrial application of BTT. In this study, we propose a parametric Bayesian model for rotating blade frequency tracking with a single probe. Compared with existing methods, this method has a wider application range and fast computation speed; moreover, it minimizes the number of probes used. Numerical simulation and experimental results show that the method is highly feasible and robust. In addition, the advantages of this method in signal amplitude estimation are demonstrated. The results are significant for online blade health monitoring of turbines. • Parametric Bayesian model for rotating blade frequency tracking with single probe. • Our method has a wider application range and fast computation speed. • The advantages of our method in signal amplitude estimation are demonstrated. • Results are significant for online blade health monitoring of turbines. [ABSTRACT FROM AUTHOR]

Rotating blades are functional and weak components with a high failure risk, which affects the working ability and operation safety of turbomachinery. Therefore, it is significant to monitor the blades. Blade tip timing (BTT) is a potential measurement technique for rotating blades' health monitoring owing to its noncontact and efficiency. However, severe undersampling and excessive probes hinder the application of BTT. Thus, identifying the parameters for blade condition monitoring from severely undersampled BTT signals is a hotspot in current research. In this work, we reviewed and summarized a novel bi-probe-based BTT method proposed in our previous work for frequency identification in the non-resonant region. On this basis, we considered the amplitude identification and mathematically derived the maximum frequency that can be identified (supremum frequency) and the maximum error that can be permissible (error tolerance) by the proposed method, which provided theoretical guidance for selecting appropriate layouts and delay schemes. Finally, we revealed the dialectical relationship between the risk of successful reconstruction and the upper bounding of error termed risk versus error mechanism, which facilitated further error reduction. • A bi-probe BTT method for frequency identification was reviewed and summarized. • The maximum identifiable frequency and allowable error of the method were derived. • This paper filled the theoretical blank of the active aliasing-based method. • This paper provided guidance for selecting appropriate layouts and delay schemes. [ABSTRACT FROM AUTHOR]

• The sparse reconstruction model with the generalized minimax-concave (GMC) penalty maintains convexity. • The GMC penalty is used to reconstruct the undersampled blade tip timing signal. • A regularization parameter selection strategy is provided according to noise level. • The GMC penalty can identify the blade-tip displacement and blade natural frequency with high accuracy. Rotor blade health monitoring based on the non-contact blade tip timing (BTT) technique has already been proved to be an alternative method to the classical contact strain measurement method. However, the signal sampled by the BTT system is usually undersampled due to the limited BTT sensors. Sparse regularization in the framework of ℓ 1 -norm has been introduced to identify the blade vibration parameter from the undersampled BTT data. However, the standard sparse regularization based on ℓ 1 -norm penalty generally generates an underestimated solution. Compared with ℓ 1 -norm penalty, generalized minimax-concave (GMC) penalty as a non-convex penalty has the promising property of amplitude improvement. In this paper, a non-convex optimization model based on GMC penalty is developed for reconstructing the undersampled BTT signal to obtain the accurate blade-tip displacement and blade natural frequency. The optimization model based on GMC penalty is presented to find the global optimal solution for the sparse representation of the BTT signal even if GMC penalty turns out to be a non-convex regularizer. Additionally, the strategy of regularization parameter selection is provided through the blade tip timing simulator. The relationship between the noise level and the regularization parameter is established to provide the strategy of regularization parameter selection in experiment. Finally, the blade spin testing is carried out for measuring the blade vibration by BTT and strain gauge systems. Amplitudes and frequencies of reconstructed BTT signals are compared with the measurements of the strain gauge, which are transferred from the strain at the blade root to the displacement at the blade tip by using the conversion coefficient obtained from the finite element model. Both simulation and experiment demonstrate that compared with the ℓ 1 -norm penalty, GMC penalty can reconstruct the blade-tip displacement and blade natural frequency with high accuracy. [ABSTRACT FROM AUTHOR]