Identification of Asynchronous Blade Vibration Parameters by Linear Regression of Blade Tip Timing Data

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Abbas Rohani Bastami ◽  
Pedram Safarpour ◽  
Arash Mikaeily ◽  
Mohammad Mohammadi
Keyword(s):  
Health Monitoring ◽  
Crack Detection ◽  
Turbine Blades ◽  
Sampling Rate ◽  
Health Indicators ◽  
Least Mean Square ◽  
Blade Vibration ◽  
Monitoring Method ◽  
Blade Tip ◽  
Vibration Parameters

Fracture of blades is usually catastrophic and creates serious damages in the turbomachines. Blades are subjected to high centrifugal force, oscillating stresses, and high temperature which makes their life limited. Therefore, blades should be checked and replaced at specified intervals or utilize a health monitoring method for them. Crack detection by nondestructive tests can only be performed during machine overhaul which is not suitable for monitoring purposes. Blade tip timing (BTT) method as a noncontact monitoring technique is spreading for health monitoring of the turbine blades. One of the main challenges of BTT method is identification of vibration parameters from one per revolution samples which is quite below Nyquist sampling rate. In this study, a new method for derivation of blade asynchronous vibration parameters from BTT data is proposed. The proposed method requires only two BTT sensors and applies least mean square algorithm to identify frequency and amplitude of blade vibration. These parameters can be further used as blade health indicators to predict defect growth in the blades. Robustness of the proposed method against measurement noise which is an important factor has been examined by numerical simulation. An experimental test was conducted on a bladed disk to show efficiency of the proposed method.

New Step to Improve the Accuracy of Blade Synchronous Vibration Parameters Identification Based on Combination of GARIV and LM Algorithm

2017 ◽  
Author(s):  
Weimin Wang ◽  
Sanqun Ren ◽  
Shan Huang ◽  
Qihang Li ◽  
Kang Chen
Keyword(s):  
Experimental Study ◽  
Parameters Identification ◽  
Least Square ◽  
Experimental Result ◽  
Blade Vibration ◽  
Vibration Displacement ◽  
Campbell Diagram ◽  
Identification Method ◽  
Blade Tip ◽  
Vibration Parameters

Generally, turbine blade vibration can be divided into asynchronous vibration and synchronous vibration. Comparing to parameters identification of asynchronous vibration, that of the synchronous vibration is more difficult and needs more sensors. The applicability of the synchronous identification method is more stringent than that of asynchronous identification method. A new method is presented to identify the blade synchronous vibration parameters based on the blade tip-timing (BTT) method and previous achievements in this region. Here, the parameters, such as the frequency of harmonic resonance center, blade vibration amplitude and the initial phase, are obtained by the nonlinear least square fitting algorithm based on relationships between the rotation speed and the blade tip displacement. We call this way as sweep frequency fitting (SFF) method. As the blade is operated at a constant speed that is near the frequency of resonance center, the blade vibration displacement can be obtained by the sensors at different positions, so the blade synchronous vibration Engine Order (EO) can be obtained by the global autoregressive with instrumental variables (GARIV) method. Furthermore the Campbell diagram of blade synchronous vibration can be plotted by the parameters obtained by GARIV method and SFF method. In the experimental study, the parameter identification of blade synchronous vibration is completed and the Campbell diagram of blade vibration is accurately plotted under the excitation of six magnets. Meanwhile, the experimental study and analysis on the harmonic vibration of blade with different numbers of excitation are carried out. The relative deviation of the dynamic frequency of blade between the experimental result and simulation result is less than 1%.

Gas Turbine Blade Damper: A Design Optimization Study to Mitigate High Resonance Blade Vibration

2013 ◽  
Author(s):  
Dilip Kumar ◽  
Sanjay Barad ◽  
T. N. Suresh
Keyword(s):  
Design Optimization ◽  
Gas Turbine ◽  
Turbine Blade ◽  
Turbine Blades ◽  
Full Scale ◽  
Original Design ◽  
Blade Vibration ◽  
Mass System ◽  
Optimization Study ◽  
Blade Tip

This paper describes the design optimization study of an under platform damper to mitigate high vibration problem of a gas turbine rotor blade under resonance condition. An existing theoretical model explicitly, Casba friction damper model was used to evaluate the dynamic characteristics of the turbine blade with under platform damper. Turbine blade is approximated as two degrees of spring-damper-mass system, which is dynamically equivalent to real turbine blades for its first two eigen values. Blade tip response predictions were carried out for different damper mass, stiffness and coefficient of friction under simulated rotational speed of the rotor, to arrive at an optimum mass to control the blade tip response. As a practical application, along with damper mass optimization, shape and mass distribution of the damper is obtained by design trials to ensure good contact between the blade root and damper upper surface. Contact analysis was carried using the ANSYS software. The asymmetric skewed damper geometry posed complications with respect to modelling and optimisation. In realistic application, with the kind of uncertainties in contact pattern, variation in friction coefficient, geometric tolerances, validation/verification plays a major role in assessing the design. As part of verification of this damper design, a full scale gas turbine engine test program was envisaged and completed. Modified optimum damper was implanted as a design change, engine was instrumented for blade vibration measurement. Non-Intrusive Stress Measurement system was used for measuring blade tip amplitudes from all the blades in the rotor. Test blade tip vibration was analysed and compared against the predications. This optimised damper configuration has showed significant reduction in blade amplitudes during full-scale gas turbine testing, in comparison to original design proving the efficacy of new modified damper.

scholarly journals New Sensors and Techniques for the Structural Health Monitoring of Propulsion Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Mark Woike ◽  
Ali Abdul-Aziz ◽  
Nikunj Oza ◽  
Bryan Matthews
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Crack Detection ◽  
Rotating Disk ◽  
Tip Clearance ◽  
Turbine Engines ◽  
Structural Health ◽  
Major Interest ◽  
Blade Tip ◽  
Blade Tip Clearance

The ability to monitor the structural health of the rotating components, especially in the hot sections of turbine engines, is of major interest to aero community in improving engine safety and reliability. The use of instrumentation for these applications remains very challenging. It requires sensors and techniques that are highly accurate, are able to operate in a high temperature environment, and can detect minute changes and hidden flaws before catastrophic events occur. The National Aeronautics and Space Administration (NASA), through the Aviation Safety Program (AVSP), has taken a lead role in the development of new sensor technologies and techniques for the in situ structural health monitoring of gas turbine engines. This paper presents a summary of key results and findings obtained from three different structural health monitoring approaches that have been investigated. This includes evaluating the performance of a novel microwave blade tip clearance sensor; a vibration based crack detection technique using an externally mounted capacitive blade tip clearance sensor; and lastly the results of using data driven anomaly detection algorithms for detecting cracks in a rotating disk.

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.

scholarly journals The Analysis Of Synchronous Blade Vibration Using Linear Sine Fitting

2014 ◽  
Vol 30 (1) ◽  
pp. 5-19 ◽  
Author(s):  
Radosław Przysowa
Keyword(s):  
Axial Compressor ◽  
Process Data ◽  
Blade Vibration ◽  
Record Times ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Engine Order ◽  
Blade Tip ◽  
Vibration Parameters ◽  
Real Test

Abstract In Blade Tip Timing several sensors installed circumferentially in the casing are used to record times of arrival (TOA) and observe deflections of blade tips. This paper aims to demonstrate methodology of model-based processing of aliased data. It focuses on the blade vibration excited by the forces synchronous with engine rotation, which are called integral responses. The driven harmonic oscillator with single degree of freedom (SDOF) is used to analyse blade vibration measured by tip-timing sensors during engine deceleration. When integral engine order EO is known, the linear sine fitting techniques can be used to process data from sensors to estimate amplitude, phase and frequency of blade vibration in each rotation. The oscillator model is implemented in MATLAB and used to generate resonance curves and simulate blade responses observed with tip sensors, installed in the axial compressor. Generated TOA data are fitted to the sine function to estimate vibration parameters. The validated procedure is then employed to analyze real test data.

scholarly journals An Improved Blade Vibration Parameter Identification Method considering Tip Clearance Variation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Liang Zhang ◽  
Qidi Wang ◽  
Xin Li
Keyword(s):  
Parameter Identification ◽  
Eddy Current ◽  
Vibration Amplitude ◽  
Tip Clearance ◽  
Vibration Parameter ◽  
Blade Vibration ◽  
Average Value ◽  
Blade Tip ◽  
Vibration Parameters ◽  
Clearance Change

Blade tip timing (BTT) technology is the most effective means for real-time monitoring of blade vibration. Accurately extracting the time of blade tip reaching the sensors is the key to ensure the accuracy of the BTT system. The tip clearance changes due to various complex forces during high-speed rotation. The traditional BTT signal extraction method does not consider the influence of tip clearance change on timing accuracy and introduces large timing errors. To solve this problem, a quadratic curve fitting timing method was proposed. In addition, based on the measurement principle of the eddy current sensors, the relationship among the output voltage of the eddy current sensor, tip clearance, and the blade cutting magnetic line angle was calibrated. A multisensor vibration parameter identification algorithm based on arbitrary angular distribution was introduced. Finally, the experiments were conducted to prove the effectiveness of the proposed method. The results show that in the range of 0.4 to 1.05 mm tip clearance change, the maximum absolute error of the timing values calculated by the proposed method is 26.0359 us, which is much lower than the calculated error of 203.7459 us when using the traditional timing method. When the tip clearance changed, the constant speed synchronous vibration parameters of No. 0 blade were identified. The average value of the vibration amplitude is 1.0881 mm. Compared with the identification results without changing tip clearance, the average value error of the vibration amplitude is 0.0017 mm. It is proved that within the blade tip clearance variation of 0.4 to 0.9 mm, the timing values obtained by the proposed timing method can accurately identify the vibration parameters of the blade.

scholarly journals Fauna biodiversity as one of Repong Damar forest health indicators

2021 ◽  
Vol 886 (1) ◽  
pp. 012036
Author(s):  
Cici Doria ◽  
Rahmat Safe’i ◽  
Dian Iswandaru ◽  
Hari Kaskoyo
Keyword(s):  
Health Status ◽  
Health Monitoring ◽  
Forest Regeneration ◽  
Forest Health ◽  
Health Indicators ◽  
Health Condition ◽  
Monitoring Method ◽  
Forest Health Monitoring ◽  
Forest Sustainability

Abstract Repong Damar Pekon Pahmungan has a diverse fauna, especially primates. Primates have great benefits for forest sustainability, because the fruit seeds ingested by primates will help spread biodiversity and forest regeneration. The presence of primates can also be an indicator of forest health. The health condition of the repong damar forest is very influential on its sustainability so that one of the health indicators that can be used is biodiversity. Biodiversity of fauna can be identified by using the FHM (Forest Health Monitoring) method to determine the diversity and condition of its health status. Repong Damar has a diversity of primate fauna, namely long-tailed monkeys and gibbons found in cluster plots 3 and 5. Based on this, Repong Damar Pekon Pahmungan has poor forest health status.

Modal Analysis for Crack Detection in Small Wind Turbine Blades

2013 ◽  
Vol 569-570 ◽  
pp. 603-610 ◽  
Author(s):  
Martin Dalgaard Ulriksen ◽  
Jonas Falk Skov ◽  
Kristoffer Ahrens Dickow ◽  
Poul Henning Kirkegaard ◽  
Lars Damkilde
Keyword(s):  
Wind Turbine ◽  
Modal Analysis ◽  
Health Monitoring ◽  
Crack Detection ◽  
Turbine Blades ◽  
Experimental Tests ◽  
Modal Parameters ◽  
Fe Model ◽  
Wind Turbine Blades ◽  
Small Wind Turbine

The aim of the present paper is to evaluate structural health monitoring (SHM) techniques based on modal analysis for crack detection in small wind turbine blades. A finite element (FE) model calibrated to measured modal parameters will be introduced to cracks with different sizes along one edge of the blade. Changes in modal parameters from the FE model are compared with data obtained from experimental tests. These comparisons will be used to validate the FE model and subsequently discuss the usability of SHM techniques based on modal parameters for condition monitoring of wind turbine blades.

scholarly journals Vision Measurement of Tunnel Structures with Robust Modelling and Deep Learning Algorithms

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4945 ◽  
Author(s):  
Xiangyang Xu ◽  
Hao Yang
Keyword(s):  
Deep Learning ◽  
Health Monitoring ◽  
Crack Detection ◽  
Three Dimensional ◽  
Image Data ◽  
Measurement Unit ◽  
Crack Identification ◽  
Monitoring Method ◽  
Vision Measurement ◽  
Robust Modelling

The health monitoring of tunnel structures is vital to the safe operation of railway transportation systems. With the increasing mileage of tunnels, regular inspection and health monitoring are urgently demanded for the tunnel structures, especially for information regarding deformation and damage. However, traditional methods of tunnel inspection are time-consuming, expensive and highly dependent on human subjectivity. In this paper, an automatic tunnel monitoring method is investigated based on image data which is collected through the moving vision measurement unit consisting of camera array. Furthermore, geometric modelling and crack inspection algorithms are proposed where a robust three-dimensional tunnel model is reconstructed utilizing a B-spline method and crack identification is conducted by means of a Mask R-CNN network. The innovation of this investigation is that we combine the robust modelling which could be applied for the deformation analysis and the crack detection where a deep learning method is employed to recognize the tunnel cracks intelligently based on image sensors. In this study, experiments were conducted on a subway tunnel structure several kilometers long, and a robust three-dimensional model is generated and the cracks are identified automatically with the image data. The superiority of this proposal is that the comprehensive information of geometry deformation and crack damage can ensure the reliability and improve the accuracy of health monitoring.

scholarly journals Feature Clustering Analysis Using Reference Model towards Rolling Bearing Performance Degradation Assessment

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiaoxi Ding ◽  
Liming Wang ◽  
Wenbin Huang ◽  
Qingbo He ◽  
Yimin Shao
Keyword(s):  
Health Monitoring ◽  
Clustering Analysis ◽  
Reference Model ◽  
Gaussian Model ◽  
Health Indicators ◽  
Active Role ◽  
Degradation Process ◽  
Performance Degradation ◽  
Monitoring Method ◽  
Feature Clustering

The health monitoring and management have been accepted in modern industrial machinery for an intelligent industrial production. To timely and reliably assess the bearing performance degradation, a novel health monitoring method called feature clustering analysis (FCA) has been proposed in this study. Along with the working time going, this new monitored chart picked by FCA aims to describe the feature clustering distribution transition by a series of reference models. First, the data provided by the reference state (healthy data) and the one from the monitor state (monitor data) are fused together to construct a reference model, which is to explore the active role of healthy status and activate the difference between healthy status and unhealthy status. Manifold learning is later implemented to mine the discriminated features for good class-separable clustering measure. In this manner, heterogeneous information hidden in this reference model will appear once degradation happened. Finally, a clustering quantification factor, named as feature clustering indicator (FCI), is calculated to assess distribution evolution and migration of the monitor status as compared to the consistent healthy status. Furthermore, a single Gaussian model (SGM) based on these FCIs is used to provide a smooth estimate of the healthy condition level. The corresponding negative log likelihood probability (NLLP) and the fault occurrence alarm are developed for an accurate and reliable FCC. And it can well depict a comprehensibility of the real bearing performance degradation process for its whole life. Meanwhile, as compared to other health profiles based on the classical health indicators, the proposed FCC has provided a much more accurate degradation level and rather monotonic profile. The experimental results show the potential in machine health performance degradation assessment.

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