Sparse Reconstruction Method of Non-Uniform Sampling and its Application in Blade Tip Timing System

2020 ◽  
Author(s):  
Jie Tian ◽  
Xiaopu Zhang ◽  
Yong Chen ◽  
Peter Russhard ◽  
Hua Ouyang
Keyword(s):  
Compressive Sensing ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Blade Vibration ◽  
Uniform Sampling ◽  
Blade Vibrations ◽  
Timing Data ◽  
Blade Tip ◽  
Simultaneous Identification ◽  
Multi Mode

Abstract Based on the blade vibration theory of turbomachinery and the basic principle of blade timing systems, a sparse reconstruction model is derived for the tip timing signal under an arbitrary sensor circumferential placement distribution. The proposed approach uses the sparsity of the tip timing signal in the frequency domain. The application of compressive sensing in reconstructing the blade tip timing signal and monitoring multi-mode blade vibrations is explored. To improve the reconstruction effect, a number of numerical experiments are conducted to examine the effects of various factors on synchronous and non-synchronous signals. This enables the specific steps involved in the compressive sensing reconstruction of tip timing signals to be determined. The proposed method is then applied to the tip timing data of a 27-blade rotor. The results show that the method accurately identifies the multi-mode blade vibrations at different rotation speeds. The proposed method has the advantages of low dependence on prior information, insensitivity to environmental noise, and simultaneous identification of synchronous and non-synchronous signals. The experimental results validate the effectiveness of the proposed approach in engineering applications.

Sparse reconstruction of blade tip-timing signals for multi-mode blade vibration monitoring

2016 ◽  
Vol 81 ◽  
pp. 250-258 ◽  
Author(s):  
Jun Lin ◽  
Zheng Hu ◽  
Zhong-Sheng Chen ◽  
Yong-Min Yang ◽  
Hai-Long Xu
Keyword(s):  
Vibration Monitoring ◽  
Sparse Reconstruction ◽  
Blade Vibration ◽  
Blade Tip ◽  
Multi Mode

scholarly journals A Non-Uniformly Under-Sampled Blade Tip-Timing Signal Reconstruction Method for Blade Vibration Monitoring

Sensors ◽  
2015 ◽  
Vol 15 (2) ◽  
pp. 2419-2437 ◽  
Author(s):  
Zheng Hu ◽  
Jun Lin ◽  
Zhong-Sheng Chen ◽  
Yong-Min Yang ◽  
Xue-Jun Li
Keyword(s):  
Signal Reconstruction ◽  
Vibration Monitoring ◽  
Reconstruction Method ◽  
Blade Vibration ◽  
Blade Tip

Fast Sparse Reconstruction of Sound Field Via Bayesian Compressive Sensing

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Ding-Yu Hu ◽  
Xin-Yue Liu ◽  
Yue Xiao ◽  
Yu Fang
Keyword(s):  
Compressive Sensing ◽  
Sound Field ◽  
Sparse Reconstruction ◽  
Limited Attention ◽  
Reconstruction Method ◽  
Sound Sources ◽  
Computation Efficiency ◽  
Reconstruction Methods ◽  
Spatially Extended ◽  
Value Decomposition

To overcome the contradiction between the resolution and the measurement cost, various algorithms for reconstructing the sound field with sparse measurement have been developed. However, limited attention is paid to the computation efficiency. In this study, a fast sparse reconstruction method is proposed based on the Bayesian compressive sensing. First, the reconstruction problem is modeled by a sparse decomposition of the sound field via singular value decomposition. Then, the Bayesian compressive sensing is adapted to reconstruct the sound field with sparse measurement of sound pressure. Numerical results demonstrate that the proposed method is applicable to either the spatially sparse distributed sound sources or the spatially extended sound sources. And comparisons with other two sparse reconstruction methods show that the proposed one has the advantages in terms of reconstruction accuracy and computational efficiency. In addition, as it is developed in the framework of multitask compressive sensing, the method can use multiple snapshots to perform reconstruction, which greatly enhances the robustness to noise. The validity and the advantage of the proposed method are further proved by experimental results.

scholarly journals Standard Sine Fitting Algorithms Applied To Blade Tip Timing Data

2014 ◽  
Vol 30 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Krzysztof Kaźmierczak ◽  
Radosław Przysowa
Keyword(s):  
A Priori ◽  
Measurement Data ◽  
Time Of Arrival ◽  
Blade Vibration ◽  
Indirect Methods ◽  
Non Linear ◽  
Timing Data ◽  
Blade Tip ◽  
Vibration Parameters ◽  
Signal Processing Methods

Abstract Blade Tip Timing (BTT) is a non-intrusive method to measure blade vibration in turbomachinery. Time of Arrival (TOA) is recorded when a blade is passing a stationary sensor. The measurement data, in form of undersampled (aliased) tip-deflection signal, are difficult to analyze with standard signal processing methods like digital filters or Fourier Transform. Several indirect methods are applied to process TOA sequences, such as reconstruction of aliased spectrum and Least-Squares Fitting to harmonic oscillator model. We used standard sine fitting algorithms provided by IEEE-STD-1057 to estimate blade vibration parameters. Blade-tip displacement was simulated in time domain using SDOF model, sampled by stationary sensors and then processed by the sinefit.m toolkit. We evaluated several configurations of different sensor placement, noise level and number of data. Results of the linear sine fitting, performed with the frequency known a priori, were compared with the non-linear ones. Some of non-linear iterations were not convergent. The algorithms and testing results are aimed to be used in analysis of asynchronous blade vibration.

Efficient Generation of Engine Representative Tip Timing Data Based on a Reduced Order Model for Bladed Rotors

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Felix Figaschewsky ◽  
Benjamin Hanschke ◽  
Arnold Kühhorn
Keyword(s):  
Real Data ◽  
Data Generation ◽  
Simulation Environment ◽  
Blade Vibration ◽  
Compressor Rotor ◽  
Positioning Errors ◽  
System A ◽  
Sample Data ◽  
Timing Data ◽  
Blade Tip

In modern compressors, the assessment of blade vibration levels as well as health monitoring of the components are fundamental tasks. Traditionally, this assessment is done by the application of strain gauges (SG) to some blades of the assembly. In contrast to SGs, blade tip timing (BTT) offers a contactless monitoring of all blades of a rotor and there is no need of a telemetry system. A major issue in the interpretation of BTT data is the heavily undersampled nature of the signal. Usually, newly developed BTT algorithms are tested with sample data created by simplified structural models neglecting many of the uncertainties and disturbing influences of real applications. This work focuses on the creation of simulated BTT datasets as close as possible to real case measurements. For this purpose, a subset of nominal system modes (SNM) representation of a compressor rotor is utilized. This model is able to include a large number of features present in real measurements, such as mistuning, static blade deflections due to centrifugal loads, aerodynamic damping, and multiple mode resonances. Additionally, manufacturing deviations of the blade geometry, probe positioning errors (PPEs) in the BTT system, and noise in the time of arrivals (TOAs) are captured by the BTT simulation environment. The main advantage of the created data is the possibility to steadily increase the signal complexity. Starting with a “perfect” signal the simulation environment is able to add different uncertainties one after the other. This allows the assessment of the influence of different features occurring in real measurements on the performance and accuracy of the analysis algorithms. Finally, a comparison of simulated BTT data and real data acquired from a rig test is shown to validate the presented approach of BTT data generation.

scholarly journals Wavelet Analysis of Experimental Blade Vibrations During Interaction With an Abradable Coating

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Romain Mandard ◽  
Jean-François Witz ◽  
Yannick Desplanques ◽  
Jacky Fabis ◽  
Jean Meriaux
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
High Speed ◽  
Continuous Wavelet ◽  
High Speed Imaging ◽  
Blade Vibration ◽  
Time Frequency ◽  
Abradable Coating ◽  
Blade Vibrations ◽  
Blade Tip

Minimizing the clearance between turbofan blades and the surrounding casing is a key factor to achieving compressor efficiency. The deposition of an abradable coating on casings is one of the technologies used to reduce this blade-casing clearance and ensure blade integrity in the event of blade-casing contact. Aircraft in-service conditions may lead to interactions between the blade tip and the coated casing, during which wear of the abradable coating, blade dynamics, and interacting force are critical yet little-understood issues. In order to study blade/abradable-coating interactions of a few tens of milliseconds, experiments were conducted on a dedicated test rig. The experimental data were analyzed with the aim of determining the friction-induced vibrational modes of the blade. This involved a time-frequency analysis of the experimental blade strain using continuous wavelet transform (CWT) combined with a modal analysis of the blade. The latter was carried out with two kinds of kinematic boundary conditions at the blade tip: free and modified, by imposing contact with the abradable coating. The interaction data show that the blade vibration modes identified during interactions correspond to the free boundary condition due to the transitional nature of the phenomena and the very short duration of contacts. The properties of the continuous wavelet transform were then used to identify the occurrence of blade-coating contact. Two kinds of blade/abradable-coating interactions were identified: bouncing of the blade over short time periods associated with loss of abradable material and isolated contacts capable of amplifying the blade vibrations without causing significant wear of the abradable coating. The results obtained were corroborated by high-speed imaging of the interactions.

Efficient Generation of Engine Representative Tip Timing Data Based on a Reduced Order Model for Bladed Rotors

2018 ◽  
Author(s):  
Felix Figaschewsky ◽  
Benjamin Hanschke ◽  
Arnold Kühhorn
Keyword(s):  
Real Data ◽  
Strain Gauges ◽  
Data Generation ◽  
Simulation Environment ◽  
Blade Vibration ◽  
Compressor Rotor ◽  
Positioning Errors ◽  
System A ◽  
Timing Data ◽  
Blade Tip

In modern compressors the assessment of blade vibration levels as well as health monitoring of the components are fundamental tasks. Traditionally, this assessment is done by the application of strain gauges to some blades of the assembly. In contrast to strain gauges, blade tip timing (BTT) offers a contactless monitoring of all blades of a rotor and there is no need of a telemetry system. A major issue in the interpretation of BTT data is the heavily undersampled nature of the signal. Usually, newly developed BTT algorithms are tested with sample data created by simplified structural models neglecting many of the uncertainties and disturbing influences of real applications. This work focuses on the creation of simulated BTT datasets as close as possible to real case measurements. For this purpose a subset of nominal system modes (SNM) representation of a compressor rotor is utilized. This model is able to include a large number of features present in real measurements, such as mistuning, static blade deflections due to centrifugal loads, aerodynamic damping and multiple mode resonances. Additionally, manufacturing deviations of the blade geometry, probe positioning errors in the BTT system and noise in the time of arrivals (TOAs) are captured by the BTT simulation environment. The main advantage of the created data is the possibility to steadily increase the signal complexity. Starting with a “perfect” signal the simulation environment is able to add different uncertainties one after the other. This allows the assessment of the influence of different features occurring in real measurements on the performance and accuracy of the analysis algorithms. Finally, a comparison of simulated BTT data and real data acquired from a rig test is shown to validate the presented approach of BTT data generation.

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