Guang-Chen Bai

Probabilistic Design of HPT Blade-Tip Radial Running Clearance with Distributed Collaborative Response Surface Method

AbstractTo develop the high-performance high-reliability of aeroengine, the probabilistic design of high-pressure turbine (HPT) blade-tip radial running clearance (BTRRC) with three objects (disk, blade, and casing) and two disciplines (heat and mechanical loads) was completed based on distributed collaborative response surface method (DCRSM) from a probabilistic perspective considering dynamic loads and nonlinear material properties. The mathematical model of DCRSM was established based on the quadratic response surface function. The basic idea of BTRRC probabilistic design based on DCRSM was introduced. The BTRRC probabilistic analysis results, consisting of the probabilistic distribution characteristics of input-output variables, the failure probability, and reliability of BTRRC under different static blade-tip clearances δ and the major factors affecting the BTRRC, reveal that the static blade-tip clearance δ=1.865×10−3  m is an optimally acceptable option for BTRRC design. The comparison of methods s...

Quantitative Diagnosis of Rotor Vibration Fault Using Process Power Spectrum Entropy and Support Vector Machine Method

To improve the diagnosis capacity of rotor vibration fault in stochastic process, an effective fault diagnosis method (named Process Power Spectrum Entropy (PPSE) and Support Vector Machine (SVM) (PPSE-SVM, for short) method) was proposed. The fault diagnosis model of PPSE-SVM was established by fusing PPSE method and SVM theory. Based on the simulation experiment of rotor vibration fault, process data for four typical vibration faults (rotor imbalance, shaft misalignment, rotor-stator rubbing, and pedestal looseness) were collected under multipoint (multiple channels) and multispeed. By using PPSE method, the PPSE values of these data were extracted as fault feature vectors to establish the SVM model of rotor vibration fault diagnosis. From rotor vibration fault diagnosis, the results demonstrate that the proposed method possesses high precision, good learning ability, good generalization ability, and strong fault-tolerant ability (robustness) in four aspects of distinguishing fault types, fault severity, fault location, and noise immunity of rotor stochastic vibration. This paper presents a novel method (PPSE-SVM) for rotor vibration fault diagnosis and real-time vibration monitoring. The presented effort is promising to improve the fault diagnosis precision of rotating machinery like gas turbine.

Nonlinear dynamic probabilistic design of turbine disk-radial deformation using extremum response surface method-based support vector machine of regression

In order to improve the computational efficiency of nonlinear dynamic probabilistic design for aeroengine typical components, a probabilistic design method–extremum response surface method-based support vector machine of regression was proposed. By taking support vector machine of regression as an extremum response surface function, the mathematical model of surface method-based support vector machine of regression was established. The probabilistic design of turbine disk-radial deformation was accomplished based on the surface method-based support vector machine of regression fully considering the influences of the nonlinearity of material property and the dynamic of heat load and mechanical load. The analysis results show that the probabilistic distribution and inverse probabilistic features of input–output parameters and the major factors (rotor speed and gas temperature) are gained legitimately, which provide the useful reference for disk design and blade-tip clearance control more effective of high-pressure turbine). Through the comparison of methods, surface method-based support vector machine of regression is demonstrated to hold high efficiency and high precision in nonlinear dynamic probabilistic design of aeroengine typical components. Moreover, the proposed surface method-based support vector machine of regression is promising to provide a useful insight for disk dynamic optimal design and blade-tip clearance control of aeroengine high-pressure turbine.

Dynamic probabilistic design for blade deformation with SVM-ERSM

Purpose – This paper aims to reasonably quantify the radial deformation of turbine blade from a probabilistic design perspective. A probabilistic design for turbine blade radial deformation considering non-linear dynamic influences can quantify risk and thus control blade tip clearance to further develop the high performance and high reliability of aeroengine. Moreover, the need for a cost-effective design has resulted in the development of probabilistic design method with high computational efficiency and accuracy to quantify the effects of these uncertainties. Design/methodology/approach – An extremum response surface method-based support vector machine (SVM-ERSM) was proposed based on SVM of regression to improve the computational efficiency and precision of blade radial deformation dynamic probabilistic design regarding non-linear material properties and dynamically thermal and mechanical loads. Findings – Through the example calculation and comparison of methods, the results show that the blade radia...

Optimum Control for Nonlinear Dynamic Radial Deformation of Turbine Casing with Time-Varying LSSVM

With the development of the high performance and high reliability of aeroengine, the blade-tip radial running clearance (BTRRC) of high pressure turbine seriously influences the reliability and performance of aeroengine, wherein the radial deformation control of turbine casing has to be concerned in BTRRC design. To improve BTRRC design, the optimum control-based probabilistic optimization of turbine casing radial deformation was implemented using time-varying least square support vector machine (T-LSSVM) by considering nonlinear material properties and dynamic thermal load. First the T-LSSVM method was proposed and its mathematical model was established. And then the nonlinear dynamic optimal control model of casing radial deformation was constructed with T-LSSVM. Thirdly, through the numerical experiments, the T-LSSVM method is demonstrated to be a promising approach in reducing additional design samples and improving computational efficiency with acceptable computational precision. Through the optimum control-based probabilistic optimization for nonlinear dynamic radial turbine casing deformation, the optimum radial deformation is 7.865 × 10−4 m with acceptable reliability degree 0.995 6, which is reduced by 7.86 × 10−5 m relative to that before optimization. These results validate the effectiveness and feasibility of the proposed T-LSSVM method, which provides a useful insight into casing radial deformation, BTRRC control, and the development of gas turbine with high performance and high reliability.

Study on the theory, method and model for mechanical dynamic assembly reliability optimization

To improve the performance and reliability of gas turbine like an aeroengine, the multi-object multi-discipline (MOMD) reliability optimization design of high press turbine (HPT) blade-tip radial running clearance (BTRRC) was first accomplished based on the mechanical dynamic assembly reliability (MDAR) theory and distributed collaborative response surface method (DCRSM). Four optimization models of MDAR were developed based on the features of assembly machinery and the thought of DCRSM, which are, respectively, called as the direct reliability optimization model (denoted by M1), the multilayer reliability optimization models (denoted by M2), the direct reliability optimization model-based probabilistic analysis (denoted by M3), and the multilayer reliability optimization model-based probabilistic analysis (denoted by M4). Through the MDAR optimization design of BTRRC by the four standard optimization models, some conclusions are drawn as follows: (1) the DCRSM is proved to be effective and feasible for MOMD MDAR optimization design with high computational efficiency and precision; (2) all the reliability optimization results of BTRRC and assembly objects satisfy the requirements of optimization design, and the optimized BTTRC variations are reduced by about 10% and obey the normal distribution, which are quite promising in improving the design and control of HPT BTRRC; (3) in computational efficiency, the computing time of M1 and M3 is far less than those of M2 and M4, meanwhile M3 and M4 are superior to M1 and M2; (4) in computational accuracy, M1 and M2 are better than M3 and M4, as well as M2 and M4 are higher than M1 and M3 theoretically. The presented study does not only fulfill the HPT BTRRC dynamic assembly design from a probabilistic optimization perspective and improve the performance and reliability of gas turbine engine, but also provides a promising approach and four valuable optimization models for MDAR optimization design. Besides, the present efforts are of great significance in enriching the theory and method of mechanical reliability design.

Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults:

To accurately reveal rolling bearing operating status, multi-feature entropy distance method was proposed for the process character analysis and diagnosis of rolling bearing faults by the integration of four information entropies in time domain, frequency domain and time–frequency domain and two kinds of signals including vibration signals and acoustic emission signals. The multi-feature entropy distance method was investigated and the basic thought of rolling bearing fault diagnosis with multi-feature entropy distance method was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner ball faults, inner–outer faults and normal) are gained under different rotational speeds. In the view of the multi-feature entropy distance method, the process diagnosis of rolling bearing faults was implemented. The analytical results show that multi-feature entropy distance fully reflects the process feature of rolling bearing faults with the change of rotating speed; the multi-feature entropy distance with vibration and acoustic emission signals better reports signal features than single type of signal (vibration or acoustic emission signal) in rolling bearing fault diagnosis; the proposed multi-feature entropy distance method holds high diagnostic precision and strong robustness (anti-noise capacity). This study provides a novel and useful methodology for the process feature extraction and fault diagnosis of rolling element bearings and other rotating machinery.

Dynamic neural network method-based improved PSO and BR algorithms for transient probabilistic analysis of flexible mechanism

Abstract To improve the computing efficiency and precision of transient probabilistic analysis of flexible mechanism, dynamic neural network method (DNNM)-based improved particle swarm optimization (PSO)/Bayesian regularization (BR) (called as PSO/BR-DNNM) is proposed based on the developed DNNM with the integration of extremum response surface method (ERSM) and artificial neural network (ANN). The mathematical model of DNNM is established based on ANN on the foundation of investigating ERSM. Aiming at the high nonlinearity and strong coupling characteristics of limit state function of flexible mechanism, accurate weights and thresholds of PSO/BR-DNNM function are discussed by searching initial weights and thresholds based on the improved PSO and training final weights and thresholds by the BR-based training performance function. The probabilistic analysis of two-link flexible robot manipulator (TFRM) was investigated with the proposed method. Reliability degree, distribution characteristics and major factors (section sizes of link-2) of TFRM are obtained, which provides a useful reference for a more effective TFRM design. Through the comparison of three methods (Monte Carlo method, DNNM, PSO/BR-DNNM), it is demonstrated that PSO/BR-DNNM reshapes the probability of flexible mechanism probabilistic analysis and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of flexible mechanism and thereby also enriches the theory and method of mechanical reliability design.

A dynamic probabilistic design method for blade-tip radial running clearance of aeroengine high-pressure turbine

Around the engineering background of the probabilistic design of high-pressure turbine (HPT) blade-tip radial running clearance (BTRRC) which conduces to the high-performance and high-reliability of aeroengine, a distributed collaborative extremum response surface method (DCERSM) was proposed for the dynamic probabilistic analysis of turbomachinery. On the basis of investigating extremum response surface method (ERSM), the mathematical model of DCERSM was established. The DCERSM was applied to the dynamic probabilistic analysis of BTRRC. The results show that the blade-tip radial static clearance δ = 1.82 mm is advisable synthetically considering the reliability and efficiency of gas turbine. As revealed by the comparison of three methods (DCERSM, ERSM, and Monte Carlo method), the DCERSM reshapes the possibility of the probabilistic analysis for turbomachinery and improves the computational efficiency while preserving computational accuracy. The DCERSM offers a useful insight for BTRRC dynamic probabilistic analysis and optimization. The present study enrichs mechanical reliability analysis and design theory.

Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling

To improve the computational efficiency and accuracy of reliability-based fatigue life prediction for complex structure, a time-varying particle swarm optimization- (PSO-) based general regression neural network (GRNN) surrogate model (called as TV/PSO-GRNN) is developed. By integrating the proposed space-filling Latin hypercube sampling technique and PSO-GRNN regression function, the mathematical model of TV/PSO-GRNN is studied. The reliability-based fatigue life prediction framework is illustrated in respect of the TV/PSO-GRNN surrogate model. Moreover, the reliability-based fatigue life prediction of an aircraft turbine blisk under multiphysics interaction is performed to validate the TV/PSO-GRNN model. We obtain the distributional characteristics, reliability degree, and sensitivity degree of fatigue failure cycle, which are useful for the turbine blisk design. By comparing the direct simulation (FE/FV model), RSM, GRNN, PSO-GRNN, and TV/PSO-GRNN, we observe that the TV/PSO-GRNN surrogate model is promising to perform the reliability-based fatigue life prediction of the turbine blisk and enhance the computational efficiency while ensuring an acceptable computational accuracy. The efforts of this study offer a useful insight for the reliability-based design optimization of complex structure.