Undersampled Blade Tip‐Timing Vibration Reconstruction under Rotating Speed Fluctuation: Uniform and Nonuniform Sensor Configurations

Chen, Zhongsheng; He, Jing; Zhan, Chi

doi:10.1155/2019/8103216

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…Chen et al [11,12] proposed to reconstruct sub-sampled blade vibrations by using the Shannon sampling theorem and wavelet packet transform. Later, Chen et al further presented two reconstruction methods under speed fluctuations [13] and variable speeds [14], respectively. Lin et al [15] presented a novel reconstruction method for non-uniformly subsampled BTT data based on the periodically non-uniform sampling theorem.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Compressed Sensing-Based Blade Vibration Reconstruction From Sub-Sampled Tip-Timing Signals

et al. 2023

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Blade tip-timing (BTT) signals are always seriously sub-sampled, so reconstruction is much necessary for vibration analysis. Blade vibration responses are sparse in order domain and classical compressed sensing (CS) algorithms are difficult to reconstruct vibration orders due to lack of prior sparse information under variable speeds. In order to deal with this issue, this paper proposes an end-to-end deep compressed sensing (DCS) method. Firstly, a Multi-coset BTT measurement model is built under variable speeds and the DCS model is derived in order domain, where a specific convolutional neural network (CNN) is designed. Next, a Simulink model is built to generate training and testing samples. The simulation results show that the convolution layer with the ReLU layer placed after the BN layer can improve the reconstruction performance and the proposed method has better reconstruction accuracy and speed than classical CS algorithms. In the end, experiments are done and the results demonstrate that blade vibration orders can be recovered accurately by the proposed method, which will provide a novel way of BTT signal analysis.INDEX TERMS Blade tip-timing, deep compressed sensing, multi-coset angular sampling, unknown multi-band vibrations, vibration reconstruction

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Compressed Sensing-Based Blade Vibration Reconstruction From Sub-Sampled Tip-Timing Signals

et al. 2023

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“…In terms of other BTT data analysis, Fan et al 15 used double BTT sensors to analyze and process the blade vibration displacement difference to complete the identification of blade synchronous vibration amplitude and engine order. Chen et al 29 proposed a reconstruction method based on nonlinear time transformation (NTT) for the influence of speed fluctuation on vibration reconstruction, which can effectively reduce the influence of speed fluctuation. Zhang et al 30 proposed a multi-phase reference method to solve the problem of speed fluctuation.…”

Section: Introductionmentioning

confidence: 99%

Rotating blade vibration parameter identification based on genetic algorithm

Xiangxiang,

Guo,

Fuhai

2023

Advances in Mechanical Engineering

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In this paper, aiming at the identification of blade vibration parameters such as constant speed synchronization, constant speed asynchronous, and variable speed synchronization, the simulation platform Simulink is used to model the blade vibration system, and the blade tip timing vibration measurement system model is constructed. A method of blade vibration parameter identification based on genetic algorithm is proposed, and numerical simulation and experimental verification are carried out. The results show that the parameter identification of blade vibration by genetic algorithm has high accuracy and strong anti-noise interference ability. The influence of key parameters on the identification of blade vibration parameters is studied. For the constant speed synchronous and constant speed asynchronous vibration of the blade, the angle between the sensors should not be an integral multiple of 2π as far as possible, and the larger DR (Distribution Range) value should be guaranteed. The higher the frequency doubling of blade vibration, the more sensors are needed. For the variable speed synchronous vibration of the blade, the frequency doubling is greater than the influence of the sensor layout on the parameter identification results, but the number of sensors is too small, which will seriously affect the identification accuracy of the frequency doubling. Aiming at the blade vibration test, a blade vibration tester is designed. The blade variable speed synchronous vibration test is carried out by using the strain gauge method and the tip timing method. The measurement results of the strain gauge method are basically consistent with the measurement results based on the genetic algorithm and the tip timing.

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“…In the measurement of bending vibration, only one BTT sensor is arranged along the axial direction of the blade, which is difficult to meet the measurement requirements of the complex blade vibration mode. On the contrary, the blade vibration signal measured based on BTT is undersampled and needs further processing [24,25]. In other words, it is necessary to study the layout of sensors and signal processing methods for torsional vibration measurement.…”

Section: Introductionmentioning

confidence: 99%

A Torsional Vibration Measurement Method for Rotating Blades Based on Blade Tip Timing

Guo

Yang

Guan

et al. 2021

Shock and Vibration

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During the working process of the turbine, some types of faults can cause changes in the vibration characteristics of the blades. The real-time vibration monitoring of the blades is of great significance to the stable operation and state-based maintenance. Torsional vibration is a kind of blade vibration modes and results in failures such as cracks easily. Thus, it is important to measure it due to the harmfulness of torsional vibration. Firstly, the principle of blade tip timing (BTT) is introduced, and the models of the blade are built to analyze the characteristics of torsional vibration. Then, the compressed sensing theory is introduced, and its related parameters are determined according to the measurement requirements. Next, based on the condition that the blade rigidity axis is not bent and bent, respectively, the layout method of sensors is proposed and the numerical simulation of the measurement process is performed. The results of the above two types of numerical simulation verify the proposed measurement method. Finally, by analyzing the influencing factors of measurement uncertainty, the optimization method of sensors’ layout is further proposed. This study can provide important theoretical guidance for the measurement of blade torsional vibration.

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Undersampled Blade Tip‐Timing Vibration Reconstruction under Rotating Speed Fluctuation: Uniform and Nonuniform Sensor Configurations

Cited by 7 publications

References 16 publications

Deep Learning for Compressed Sensing-Based Blade Vibration Reconstruction From Sub-Sampled Tip-Timing Signals

Deep Learning for Compressed Sensing-Based Blade Vibration Reconstruction From Sub-Sampled Tip-Timing Signals

Rotating blade vibration parameter identification based on genetic algorithm

A Torsional Vibration Measurement Method for Rotating Blades Based on Blade Tip Timing

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