Blade Rubbing Research Articles

A Study on Machine Learning-Based Feature Classification for the Early Diagnosis of Blade Rubbing.

This research focuses on the development of a machine learning-based approach for the early diagnosis of blade rubbing in rotary machinery. In this paper, machine learning-based diagnostic methods are used for blade rubbing early diagnosis, and the faults are simulated using experimental models. The experimental conditions were simulated as follows: Excessive rotor vibration is generated by an unbalance mass, and blade rubbing occurs through excessive rotor vibration. Additionally, the severity of blade rubbing was also simulated while increasing the unbalance mass. And then, machine learning-based diagnostic methods were applied and the trends according to the severity of blade rubbing were compared. This paper provides a signal processing method through feature analysis to diagnose blade rubbing conditions in machine learning. It was confirmed that the results of the unbalance and blade rubbing represent different trends, and it is possible to distinguish unbalance from blade rubbing before blade rubbing occurs. The diagnosis using machine learning methods will be applicable to rotating machinery faults like blade rubbing; furthermore, the early diagnosis of blade rubbing will be possible.

On the operational similarities of bladed rotor vibrations with casing contacts

AbstractRotor-to-stator rubbing in rotating machinery, resulting from tight clearances, introduces complex dynamics that can potentially lead to high vibrations and machine failure. Historically, the rubbing models were addressed using cylinder-to-cylinder contacts; however, recent attention has shifted towards examining blade-tip contact in turbines, which affects the systems dynamics and efficiency. This study investigates the impact of the variations in blade number on bladed rotor systems, emphasizing on the types of motion that occur as function of the operational speed in the sub-critical range. A simplified bladed rotor model has been developed, using a Jeffcott rotor with blades represented as damped elastic pendulums. The equations of motion are derived and numerical simulations are performed to explore the system’s behaviour with varying blade numbers (3, 5, 7, and 10) in order to analyse displacements, contact forces and bifurcation diagrams as function of the rotating speed. Results reveal distinct regions: periodic motion (I and III) and chaotic motion (II and IV) appear alternatively in the bifurcation diagram, with the chaotic regions occurring at specific fractions of the natural frequency and the number of blades. The study concludes that chaotic motions are associated with larger displacements and higher contact forces, and the vibrational behaviour becomes less hazardous as the number of blades increases. In addition, the appearance of periodic and chaotic motions occur in the same regions by scaling the rotating speed with the number of blades and natural frequency of the system. From an operational perspective, this dynamic investigation offers valuable insights into the severity of blade rubbing in industrial systems. It can guide the implementation of mitigation solutions to prevent worst-case failure scenarios and help to perform adjustments to either operational or design parameters.

Thermal–Elastic–Magnetic Coupling-Induced Rubbing Behaviors of a Bladed Thin-Walled Rotor with Distributed Magnetic Actuators

A bladed thin-walled rotor with magnetic bearings in gas turbines has minimal wear to improve the service life. Especially, the rotor system can actively suppress vibrations. Yet, thermal–elastic–magnetic coupling-induced rubbing features of a bladed thin-walled rotor with magnetic bearings are not clear, and blade rubbing behaviors induced by high temperatures always occur in this kind of rotor. This paper establishes a new bladed thin-walled rotor model with distributed electromagnetic actuators to reduce thermoelastic vibrations and develops a solution approach for obtaining the thermal–elastic–magnetic coupling-induced rubbing characteristics of the rotor. The solution approach is verified, and the effectiveness of the distributed electromagnetic actuator model is demonstrated. The magnetic supports require two differential-control actuators at each position to generate the electromagnetic force, due to irregular concave–convex deformations of the rotor. Thereafter blade rub behaviors for the thin-walled rotor system are revealed. Uniform and smaller thermal deformations of the rotor system with the present actuator model avoid tip rub due to preventing thermal energy concentration. With the proper bearing capacity of a single actuator, an adequate number of actuators are required to ensure stability. The proposed theoretical prototype of the bladed thin-walled rotor with distributed electromagnetic actuators prevents blade rubbing caused by high temperatures. The provided solution approach can evaluate the vibration characteristics of a rotating thin-walled rotor with magnetic supports in the high-temperature environment.

Development of features for blade rubbing defect classification in machine learning

Development of features for blade rubbing defect classification in machine learning

Blade Fault Localization with the Use of Vibration Signals Through Artificial Neural Network: A Data-Driven Approach

Turbines are significant for extracting energy for petrochemical plants, power generation, and aerospace industries. However, it has been reported that turbine-blade failures are the most common causes of machinery breakdown. Therefore, numerous analyses have been performed to formulate techniques for detecting and classifying the fault of the turbine blade. Nevertheless, the blade fault localization method, performed to locate the faulty parts, is equally important for plant operation and maintenance. Therefore, this study will propose a blade fault localization method centered on time-frequency feature extraction and a machine learning approach. The purpose is to locate the faulty parts of the turbine blade. In addition, experimental research is carried out to simulate various blade faults. It includes blade rubbing, blade parts loss, and twisted blade. An artificial neural network model was developed to localize blade fault through the extracted features with newly proposed and selected features. The classification results indicated that the proposed feature set and feature selection method could be used for blade fault localization. It can be seen from the classification rate for blade faultiness localization.

A genetic algorithm based aerothermal optimization of tip carving for an axial turbine blade

In turbomachines, a properly dimensioned gap between the rotating blade tip and the stationary casing is required in order to avoid mechanical failures due to blade rubbing. Maintaining a tip gap allows the relative motion of the blade, however a leakage flow almost always exists due to the strong pressure differentials existing near the tip airfoil boundaries. Tip leakage flow which is a 3-dimensional and highly complex flow system is responsible from a considerable amount of total pressure loss in a turbine stage. Besides, tip leakage flows induce adverse thermal effects near the blade tip, eventually causing an increase in cooling demand. Various passive control methods exist to weaken the adverse effects of tip leakage flows, in an effort to increase turbine stage efficiency. In this paper, a novel tip carving approach is applied to mitigate the undesired aerothermal effects of the tip leakage flow. A numerical investigation is carried out to obtain the optimum shape of the carved blade tip with an objective function to minimize both heat transfer and leakage loss. A genetic algorithm is used for the optimization, integrated with a meta model which predicts the objective functions quickly. Various meta-models such as Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Support Vector Machine (SVM) are tested for this purpose. An initial database consisting of 55 blade tip geometries is created for meta-model training using “Sobol design of experiments” methodology. This database is then successively enlarged using a coarse-to-fine approach in order to improve the prediction capabilities of the meta-models. Once a sufficient level of prediction error and a proper consistency is achieved, the optimization process is terminated. Current results indicate that carved blade tip designs are likely to achieve a considerable improvement in aero-thermal performance of axial turbine stages. Multi-objective optimization of the blade tip surface of the carved type is a promising approach in gas turbines since it paves the way for undiscovered blade tip designs for further performance improvements.

Numerical investigations on axial and radial blade rubs in turbo-machinery

In the recent years, the clearance between the rotor blades and stator/casing had been getting smaller and smaller prior improving the aerodynamic efficiency of the turbomachines as demand in the engineering field. Due to the clearance reduction between the blade tip and the rotor casing and between rotor blades and stator blades, axial and radial blade rubbing could be occurred, especially at high speed resulting into complex nonlinear vibrations. The primary aim of this study is to address the blade axial rubbing phenomenon using numerical analysis of rotor system. A comparison between rubbing caused impacts of axial and radial blade rubbing and rubbing forces are also aims of this study. Tow rotor models (rotor-stator and rotor casing models) has been designed and sketched using SOILDSWORKS software. ANSYS software has been used for the simulation and the numerical analysis. The rubbing conditions were simulated at speed range of 1000rpm, 1500rpm and 2000rpm. Analysis results for axial blade rubbing showed the appearance of blade passing frequency and its multiple frequencies (lx, 2x 3x etc.) and these frequencies will more excited with increasing the rotational speed. Also, it has been observed that when the rotating speed increased, the rubbing force and the harmonics frequencies in x, y and z-direction become higher and severe. The comparison study showed that axial blade rub is more dangerous and would generate a higher vibration impacts and higher blade rubbing force than radial blade rub.

Aerodynamical and Structural Analysis of Operationally Used Turbine Blades

This paper presents an integrated methodology for the analysis of operationally-used turbine blades, incorporating aerodynamic and multiple structural simulations. In jet engines, blade rubbing and erosion lead to deviations of the blade geometry. The presented functional simulations are conducted in order to predict the influence of wear on the performance of turbine blades based on these geometric variations.A numerical simulation of the investigated turbine blades using CFD show the change of aerodynamic performance and the flow field due to wear. Additionally, the deviations of the blade geometry lead to a different pressure and temperature distribution on the blade surface, which is used as input for the structural simulations.The change in geometry, surface pressure and temperature lead to a change in vibration behavior of the blade. Particularly the eigenfrequencies and excitation are affected. This is incorporated into the analysis by performing a structural vibration simulation of a complete bladed disk, using component mode synthesis and wave base substructuring. The mistuning effects are analyzed statistically using the Monte Carlo method.The change in vibration amplitudes influences crack opening and closing for a single blade under thermo-mechanical load. These processes, including thermal expansion, are investigated using the extended finite element method. Two real turbine blades are used to compare the characteristics of a new and a used blade.

터빈 블레이드 진단을 위한 회전기계 마찰 진동에 관한 연구

Rubbing and misalignment are the most usual faults that occurs in rotating machinery and with them severe effect on power plant availability. Especially blade rubbing is hard to detect on FFT spectrum using the vibration signal. In this paper, the possibility of feature analysis of vibration signal is confirmed under blade rubbing and misalignment condition. And the lab-scale rotor test device provides the blade rubbing and shaft misalignment modes. Feature selection based on GA (genetic algorithm) is processed by the extracted feature of the time domain. Then, classification of the features is analyzed by using SVM (support vector machine) which is one of the machine learning algorithm. The results of features selection based on GA compared with those based on PCA (principal component analysis). According to the results, the possibility of feature analysis is confirmed. Therefore, blade rubbing and shaft misalignment can be diagnosed by feature of vibration signal.

Time Frequency Analysis for Blade Rub Detection in Multi Stage Rotor System

Blade fault is one of the most causes of failure in turbo machinery. This paper discussed the time frequency analysis for blade rubbing detection from casing vibration signal. Feasibility of Short Time Fourier Transform (STFT), Wigner-Ville distribution (WVD) and Choi-Williams distribution (CWD) were examined for blade rub detection in a multi stage blade system through an experimental data. Analysis results of the experimental data showed that these time frequency analysis methods have some inevitable deficiencies in segregating the blade passing frequency (BPF) components of the three rotor stage signals. However, CWD demonstrated a better time-frequency resolution in analyzing the multi stage rotor system signal.

Condition Monitoring of Blade in Turbomachinery: A Review

Blade faults and blade failures are ranked among the most frequent causes of failures in turbomachinery. This paper provides a review on the condition monitoring techniques and the most suitable signal analysis methods to detect and diagnose the health condition of blades in turbomachinery. In this paper, blade faults are categorised into five types in accordance with their nature and characteristics, namely, blade rubbing, blade fatigue failure, blade deformations (twisting, creeping, corrosion, and erosion), blade fouling, and loose blade. Reviews on characteristics and the specific diagnostic methods to detect each type of blade faults are also presented. This paper also aims to provide a reference in selecting the most suitable approaches to monitor the health condition of blades in turbomachinery.

Vibration Analysis of Multi Stages Rotor for Blade Faults Diagnosis

Blade fault is one of the most common faults in turbomachinery. In this article, a rotor system consists of multiple rows of blade was developed. The effectiveness of conventional FFT spectrum and wavelet analysis in the diagnosis of multi stage blade rubbing faults is examined at different stages, variety of blade fault conditions, and different blades rubbing severity. Blade fault caused impacts and the use of wavelets as analysis tool to detect the blade faults was studied. Results showed that, vibration spectrum can clearly depict the location and the stage of blade rubbing, while it is difficult to be identified in wavelet analysis. The limitations of wavelet analysis for multi stage blade fault diagnosis were identified. Some probable solutions to improve wavelet time-frequency representation in blade fault diagnosis were also presented.

Blade Faults Classification and Detection Methods: Review

Blade faults are ranked among the most frequent causes for gas turbine failures. This paper provides a review on the types of blade faults as well as its pertinent detection methods. In this paper, blade faults are categorized into five major groups according to their nature and characteristics namely, blade rubbing, blade fatigue failures, blade deformation, blade fouling, and blade root related problems such as cracked root and loose blade. This paper aims to provide an overview on the characteristics of each type of blade fault as well as its best detection methods available to date.

Vibration Analysis of Rub in Rotating Machinery

Rubbing is one of the most common faults that occurs in rotating machinery. This paper studies the vibration responses of a rotor system under the influence of blade induced rubbing. An experimental study was undertaken to simulate various conditions of rubbing in rotor system caused by rotating blades and its corresponding vibration responses are measured and analyzed. Experimental results showed that the effect and vibration responses caused by blade rubbing is enormous and therefore can be easily detected based on vibration spectrum analysis. Besides this, the severity of rubbing caused by different conditions of blades could also be estimated based on the magnitudes and patterns of vibration spectrum.

Detection of Early Faults in Rotating Machinery Based on Wavelet Analysis

This paper explores the application of wavelet analysis for the detection of early changes in rotor dynamics caused by common machinery faults, namely, rotor unbalance and minor blade rubbing conditions. In this paper, the time synchronised wavelet analysis method was formulated and its effectiveness to detect machinery faults at the early stage was evaluated based on signal simulation and experimental study. The proposed method provides a more standardised approach to visualise the current state of rotor dynamics of a rotating machinery by taking into account the effects of time shift, wavelet edge distortion, and system noise suppression. The experimental results showed that this method is able to reveal subtle changes of the vibration signal characteristics in both the frequency content distribution and the amplitude distortion caused by minor rotor unbalance and blade rubbing conditions. Besides, this method also appeared to be an effective tool to diagnose and to discriminate the different types of machinery faults based on the unique pattern of the wavelet contours. This study shows that the proposed wavelet analysis method is promising to reveal machinery faults at early stage as compared to vibration spectrum analysis.

The transmission of acoustic emission across large-scale turbine rotors

Seal and blade-tip rubbing in turbo machinery can often result in costly rotor damage. Acoustic emission analysis at the bearings has previously been cited as a viable non-destructive monitoring technique by which the onset of such rotor–stator contacts can be detected in large-scale turbines [Aerospace Conf., IEEE Proc. 6 (2000) 79; Electr. Engng Jpn 110 (2000)]. These studies suggested that the stress waves or acoustic emissions produced during the frictional rub process can propagate across the turbine surface and across the bearing interfaces to be detectable by the sensor attached to the bearing housing. However, results from the actual large-scale turbine units have been scarce. This paper presents a fundamental/pre-requisite study on the transmissibility of acoustic emission (AE) signatures across very large-scale turbine rotors. It is concluded that the actual AE attenuation incurred does not inherently exclude the detection of seal and blade rubbing in turbines. However, its success is highly dependent on advanced signal processing.