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.

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.

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.

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.

Using Tiptiming and Strain Gauge Data for the Estimation of Consumed Life in a Compressor Blisk Subjected to Stall-Induced Loading

2014 ◽  
Author(s):  
Vsevolod Kharyton ◽  
Ronnie Bladh
Keyword(s):  
Complex Stress ◽  
Strain Gauges ◽  
Time Signal ◽  
Data Sets ◽  
Transonic Compressor ◽  
Compressor Rotor ◽  
Industry Standard ◽  
Timing Data ◽  
Time Histories ◽  
Common Application

Using tip timing technology to record blade vibratory behavior has grown to become an industry standard over the past decade. Typically, the technology gets used during engine prototype testing to verify safe operation of the blades and thus the engine through synchronous and non-synchronous excitation events. Another common application is blade health monitoring, where the technique is used to detect deviations in natural frequencies and/or amplitudes compared to the virgin state. In both cases, acquired response data are used to establish that blade stresses remain below the high cycle fatigue limit. More rarely is tip timing data used as basis for remaining life estimation. As an example of how tip timing technology can be used beyond traditional resonance clearance for new blade designs, this paper presents an assessment of the fatigue damage incurred to a transonic compressor rotor subjected to stall-induced dynamic loading. The compressor rotor in question is equipped with tip timing, as well as strain gauges for a limited set of airfoils. The dynamic loads at stall are non-synchronous and highly erratic in nature, leading to quasi-static response of multiple modes. To facilitate a conceptually straightforward time domain finite life fatigue analysis, different strategies are employed to reconstruct the stress-time signal from tip timing data. This in turn allows for quantification of accumulated damage cycles, which is here done through simplified and traditional rainflow counting techniques. Additionally, a non-standard way of processing of tip timing data was employed to overcome one of tip timing method drawbacks — frequency aliasing. As an approach the nonuniform Fourier transform was applied to the same data sets. The results obtained are thoroughly evaluated and compared with strain gauges results highlighting the benefits and limitations of the respective approaches for highly complex stress-time histories such as stall events.

A Discussion on the Advancement of Blade Tip Timing Data Processing

2017 ◽  
Author(s):  
Vsevolod Kharyton ◽  
Grigorios Dimitriadis ◽  
Colin Defise
Keyword(s):  
Digital Signal ◽  
Minimum Variance ◽  
Rotating Stall ◽  
Blade Vibration ◽  
Variable Threshold ◽  
Engine Order ◽  
Timing Data ◽  
Large Scope ◽  
Blade Tip ◽  
Frequency Aliasing

The Blade Tip Timing method (BTT) is a well-known approach permitting individual blade vibration behavior characterization. The technique is becoming increasingly popular among turbomachinery vibration specialists. Its advantages include its non-intrusive nature and its capability of being used for long-term monitoring, both in on-line and offline analysis. However, the main drawback of BTT is frequency aliasing. Frequency aliasing effects in tip timing can be reduced by means of the application of different methods from digital signal analysis that can exploit the non-uniform nature of the data sampled by BTT. This non-uniformity is due to the fact that an optimization of the circumferential distribution of BTT probes is usually required in order to improve the data quality for targeted modes of blade vibration and/or orders of excitation. The BTT data analysis methods considered in this study are the non-uniform Fourier transform, the minimum variance spectrum estimator approach, a multi-channel technique using in-between samples interpolation, the Lombe-Scargle periodogram and an iterative variable threshold procedure. These methods will be applied to measured data representing quite a large scope of events occurring during gas-turbine compressor operation, e.g. synchronous engine order resonance crossing, rotating stall, suspected limit-cycle oscillations. Finally, the frequency estimates obtained from all these methods will be summarized.

Multiple Frequency Analysis Methods for Blade Tip-Timing Data Analysis

Volume 2 ◽  
2004 ◽  
Author(s):  
Jon Gallego Garrido ◽  
Grigorios Dimitriadis
Keyword(s):  
Measurement Errors ◽  
Simulated Data ◽  
Test Cases ◽  
Blade Vibration ◽  
Use Of Data ◽  
Analysis Methods ◽  
Timing Data ◽  
Suitable Amount ◽  
Blade Tip ◽  
Auto Regressive

Blade Tip-Timing (BTT) is a method for the measurement of blade vibration in rotating bladed assemblies such as those found in turbomachinery. The system aims to replace strain gauge technology. However all current BTT analysis methods fail to recover the correct frequencies when two blade modes are excited simultaneously by a synchronous vibration. In this paper, five new methods that can recover simultaneous frequencies from BTT data are presented. The methods are based on the auto-regressive approach. The approached make use of data either from a single blade and single revolution or from multiple revolutions. Furthermore, some of the methods are designed to allow for the presence of measurement errors. The techniques are validated on three test cases in which simulated data was used. It is shown that most of the methods produce accurate estimates for the vibration frequency, even in the presence of significant noise levels, provided that a suitable amount of the response waveform is measured. The most consistent estimates are obtained from the methods that make use of data from multiple revolutions.

Tip-Timing Data Analysis for Mistuned Bladed Discs Assemblies

2008 ◽  
Author(s):  
Cyrille Ste´phan ◽  
Marc Berthillier ◽  
Joseph Lardie`s ◽  
Arnaud Talon
Keyword(s):  
Spectral Analysis ◽  
Optical Sensors ◽  
Fundamental Problem ◽  
Spectral Estimation ◽  
Minimum Variance ◽  
Vibration Measurement ◽  
Blade Vibration ◽  
Autocorrelation Matrix ◽  
Timing Data ◽  
Blade Tip

In turbomachine industry, bladed assembly vibration measurements are very important for blades behaviour estimation. These measurements are generally obtained from strain gauges. However, one of the most promising methods for the analysis of blade vibrations in rotating bladed assemblies is the Blade Tip Timing or Optical Blade Vibration Measurement method. A set of optical sensors is mounted on an engine casing, in front of a disc, and measures the times of arrival of each blade. These timings are then used to compute the vibrations of the blades. However the fundamental problem for spectral analysis of blade tip timing data is that the signals are undersampled and aliased. We propose here a new method for spectral estimation of blades responses from tip timing data that overcome these difficulties. The method proposed in this communication is based on the use of a minimum variance filter associated with an iterative updating of the autocorrelation matrix. That allows to process correctly a signal even if the number of known signal samples is less than equivalent Nyquist criterion. This approach is suitable for spectral analysis of undersampled and aliased signals. Perfomances of the spectral estimator have been evaluated for one simulated and one experimental test cases. The method seems very promising for the monitoring of mistuned bladed discs.

scholarly journals Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference

2021 ◽  
Vol 11 (8) ◽  
pp. 3484
Author(s):  
Martin Tabakov ◽  
Adrian Chlopowiec ◽  
Adam Chlopowiec ◽  
Adam Dlubak
Keyword(s):  
Fuzzy Inference ◽  
Real Data ◽  
Fuzzy Rules ◽  
Fuzzy Information ◽  
Benchmark Data ◽  
System A ◽  
Footprint Of Uncertainty ◽  
Interval Type ◽  
Derived Rules

In this research, we introduce a classification procedure based on rule induction and fuzzy reasoning. The classifier generalizes attribute information to handle uncertainty, which often occurs in real data. To induce fuzzy rules, we define the corresponding fuzzy information system. A transformation of the derived rules into interval type-2 fuzzy rules is provided as well. The fuzzification applied is optimized with respect to the footprint of uncertainty of the corresponding type-2 fuzzy sets. The classification process is related to a Mamdani type fuzzy inference. The method proposed was evaluated by the F-score measure on benchmark data.

Application de la fonction bêta et des polynômes de Jacobi à l'analyse des valeurs extrêmes

1994 ◽  
Vol 21 (6) ◽  
pp. 1074-1080 ◽  
Author(s):  
J. Llamas ◽  
C. Diaz Delgado ◽  
M.-L. Lavertu
Keyword(s):  
Frequency Estimation ◽  
Probabilistic Method ◽  
Beta Function ◽  
Real Data ◽  
Flood Frequency ◽  
Data Series ◽  
Data Generation ◽  
Practical Applications ◽  
Flood Probability ◽  
Probable Maximum Flood

In this paper, an improved probabilistic method for flood analysis using the probable maximum flood, the beta function, and orthogonal Jacobi’s polynomials is proposed. The shape of the beta function depends on the sample's characteristics and the bounds of the phenomenon. On the other hand, a serial of Jacobi’s polynomials has been used improving the beta function and increasing its convergence degree toward the real flood probability density function. This mathematical model has been tested using a sample of 1000 generated beta random data. Finally, some practical applications with real data series, from important Quebec's rivers, have been performed; the model solutions for these rivers showed the accuracy of this new method in flood frequency estimation. Key words: probable maximum flood, beta function, orthogonal polynomials, distribution function, flood frequency estimation, data generation, convergency.

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