基于短时能量的压气机喘振检测

及时地检测压气机喘振对提高发动机性能、保障飞行安全具有重要意义。在进入喘振状态的过程中,压气机出口总压脉动增大,短时能量亦增大。通过某型压气机节流试验数据,验证了短时能量在喘振检测中的可行性。该算法可检测模态波或突尖波扰动引起的喘振,信号经平方运算后提高了信噪比,算法简单,易于实现。     

非恒定转速下压气机动态模型的建立与仿真

研究了轴流式压气机在非恒定转速条件下的动态建模问题.采用模块化的建模方法在原MG模型的基础上详细地推导了模型的改进部分,并对改进后的模型进行了仿真分析与验证.改进的MG模型将压气机转速(或B参数)作为除压气机压升、平均流量和流量扰动之外的另一个状态变量,使压气机动态模型更加接近实际情况.仿真结果表明,改进模型能较好地描述轴流式压气机的失稳现象(旋转失速与喘振),可以应用于压气机系统稳定性分析、失速/喘振预测以及主动控制等方面的研究.     

轴流压气机失速与喘振的发生与对策分析

轴流压气机不稳定的工作形式通常表现为旋转失速和喘振,这是限制压气机稳定工作范围的两大因素。把压升作为反馈信号,通过补偿节流阀系数值的方式,对旋转失速和喘振进行控制,够增加压气机的喘振裕度,消除失速时伴随的迟回效应。     

基于LXI总线的压气机试验台退喘试验系统研制

压气机喘振是气流沿压气机轴线方向发生的低频率、高振幅的震荡现象,会导致压气机部件强烈机械振动和热端超温,会在短时间内造成部件或整机严重损坏,因此压气机试验台建设时退喘系统建设尤为重要;介绍了一种基于LXI总线的压气机试验台退喘试验系统的实现方法,阐述了该系统的主要特点和优势;试验中,对压力、温度、电压参数进行采集和记录,然后通过判喘准则,进行判断,并快速发出控制信号,实现压气机快速退喘;通过验证,该系统在压气机发生喘振时能够迅速发出信号,实现压气机快速退喘,达到了建设的目的,可有效防止压气机喘振对试验部件造成损坏。     

压气机气动失稳故障的声学诊断技术研究进展

基于声学原理和声学信号进行压气机气动失稳故障诊断具有重要的研究价值，对基于声学信号的压气机失稳监测和预警技术开展了回顾与总结。首先介绍了常见的气动失稳故障，包括失速、喘振和旋转不稳定性的发声机理与信号特征，然后介绍和总结了国内外关于基于声学信号开展压气机气动失稳故障监测、诊断和预警方法的最新研究进展。结果表明：进入气动失稳状态的压气机，在声音特征上通常表现为低频能量增加、周期性破坏和特定模态分量幅值变化。通过对声信号的深入研究分析，建立机理与信号特征的联系，有望设计出更加准确、灵敏的压气机失稳故障检测方法。未来一段时间内，研究如何在包含噪声与混响的信号中提取出用于失稳诊断的信号特征以及发展相应数据集以支撑数据驱动的失稳检测方法将是声学失稳检测技术面临的重大问题。     

辅助动力装置负载压气机防喘控制研究

以带负载压气机的APU为对象,分析了负载压气机工作时喘振机理.根据负载压气机出口气流状态,采用可变极限流量法判别APU负载压气机喘振.根据流量平衡原理,进行负载压气机防喘放气控制,保证APU工作效率的同时,使APU负载压气机远离喘振边界稳定工作.运用硬件在回路仿真系统,通过逼喘仿真,结果表明该方法可以用来保证APU系统压气机的稳定工作.     

轴流压气机旋转失速建模与检测II: 基于北航低速压气机试验台的实验研究

轴流压气机旋转失速和喘振的提前检测对于提高压气机工作效率和稳定性具有重要的意义.本文以北京航空航天大学航空发动机重点实验室的低速轴流压气机实验台为研究对象,基于确定学习理论及动态模式识别方法,开展旋转失速初始扰动近似准确建模和快速检测研究.首先,在压气机机匣壁面周向布置多个动态压力传感器,获取压气机失速前和失速先兆的动态压力信号,基于确定学习理论对旋转失速初始扰动的内部系统动态进行建模;其次,基于以上建模,利用微小振动故障检测方法实现对旋转失速的离线和在线提前检测.实验结果表明,本文所提方法能够在不同转速情况下,提前0.3 s~1 s实现对旋转失速的实时在线检测.     

基于AR模型和神经网络的舰船水压信号检测方法

为了有效地从风浪背景中检测舰船水压场信号，根据舰船水压场信号和波浪噪声信号的差异，以时间序列的AR模型理论为依据，采用基于AR模型和神经网络的舰船水压信号检测方法。该检测算法的核心是将检测问题转化为模式识别问题，首先对接收信号建立AR模型并提取AR模型系数作为特征向量，然后利用人工神经网络对信号进行检测。在此基础上，通过不同浪级情况下海浪水压力场的仿真信号数据，对某型目标舰船的水压力信号进行了检测计算，验证了该方法的有效性，尤其是达到了在高海况、低信噪比条件下，对目标信号检测率比较高、虚警率比较低的效果。     

并行计算在动态模式识别中的实现和应用*

研究一种针对最近提出的动态环境下的机器学习理论——确定学习理论的算法实现,提出一种采用并行计算实现确定学习理论中的动态模式识别的方法。利用并行计算中的OpenMP多核编程环境,采用曙光16核服务器为硬件平台,实现对动态模式识别算法的快速性。同时,以压气机Mansoux模型为应用背景,把确定学习理论的动态模式识别方法应用到压气机旋转失速/喘振的快速检测中,利用多核并行计算实现了从包含多种旋转失速/喘振模式的模式库中快速识别当前模式的方法,为文章中方法提供了一个有效的验证。     

基于AR模型的自适应浮动门限检测方法

混响是水下目标回波信号检测的主要背景干扰;基于AR模型的预白化匹配滤波检测方法,往往由于混响AR模型定阶困难,无法对混响准确建模;这将会导致预白化效果不佳,检测性能下降;针对这一问题,在预白化匹配滤波的基础上,提出了一种基于AR模型预白化匹配滤波的自适应浮动门限检测方法;给出了浮动门限经验公式,并对实测数据进行处理;处理结果表明,所提出的基于AR模型预白化匹配滤波的自适应浮动门限检测方法能使检测门限很好地跟随信号的变化趋势,提高检测概率,保持恒虚警检测.     

时间序列预测模型在压缩机零件测试中的运用

压缩机零件在使用时会随时间的变化发生变形,对于压缩机的性能会有很大的影响,因此在零件进行材料选择和几何尺寸设计时,对相关零件变形的检测和预报是至关重要的。介绍了时间序列分析的基本理论及其预测建模的方法;采用参数识别方法,结合压缩机消音板发生变形的动态数据,建立该压缩机零件变形的时序预测模型,对相关零件变形进行了监测和预报,具有很强的实际运用价值。     

Applications of artificial intelligence for optimization of compressor scheduling

This paper presents a feasibility study of evolutionary scheduling for gas pipeline operations. The problem is complex because of several constraints that must be taken into consideration during the optimization process. The objective of gas pipeline operations is to transfer sufficient gas from gas stations to consumers so as to satisfy customer demand with minimum costs. The scheduling involves selection of a set of compressors to operate during a shift. The scheduling decision has to be made so as to satisfy the dual objectives of minimizing the sum of fuel cost, start-up cost, the cost of gas wasted due to oversupply, and satisfying minimal operative and inoperative time of the compressors. The problem was decomposed into the two subproblems of gas load forecast and selection of compressors. Neural networks were used for forecasting the load; and genetic algorithms were used to search for a near optimal combination of compressors. The study was conducted on a subsystem of the pipeline network located in southeastern Saskatchewan, Canada. The results are compared with the solutions generated by an expert system and a fuzzy linear programming model.     

A smoothness priors time-varying AR coefficient modeling of nonstationary covariance time series

A smoothness priors time varying AR coefficient model approach for the modeling of nonstationary in the covariance time series is shown. Smoothness priors in the form of a difference equation constraint excited by an independent white noise are imposed on each AR coefficient. The unknown white noise variances are hyperparameters of the AR coefficient distribution. The critical computation is of the likelihood of the hyperparameters of the Bayesian model. This computation is facilitated by a state-space representation Kalman filter implementation. The best difference equation order-best AR model order-best hyperparameter model locally in time is selected using the minimum AIC method. Also, an instantaneous spectral density is defined in terms of the instantaneous AR model coefficients and a smoothed estimate of the instantaneous time series variance. An earthquake record is analyzed. The changing spectral analysis of the original data and of simulations from a time varying AR coefficient model of that data are shown.     

A smoothness priors long AR model method for spectral estimation

A new smoothness priors long AR model method approach is taken to the short data span spectral estimation problem. An autoregressive (AR) model that is relatively long compared to the data length is considered. The smoothness priors are in the form of the integrated squared derivatives of the AR model whitening filter. A smoothness tradeoff parameter or Bayesian hyperparameter balances the tradeoff between the infidelity of the AR model to the data and the infidelity of the model to the smoothness constraint. The critical computation of the likelihood of the hyperparameters of the Bayesian model is realized by a constrained least squares computation. Numerical examples are shown. The results of simulation studies using entropy comparison evaluations of the Bayesian and minimum AIC-AR methods of spectral estimation are also shown.     

A new hybrid method for time series forecasting: AR–ANFIS

In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR–ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR–ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts.

     

Shape signifiers for control of a low-order compressor model

Rotating stall and surge, two instability mechanisms limiting the performance of aeroengines compressors, are studied on the third-order Moore-Greitzer model. The skewness of the compressor characteristic, a single parameter shape signifier, is shown to determine the key qualitative properties of feedback control     

舰船水压信号的预测方法研究

提出了一种能从海浪水压信号背景下提取舰船水压信号的预测异常检测(PAD)法。模型预测值与测量值相比较所得的差值被作为检测舰船水压信号是否存在的判据。讨论了作为PAD中预测模型的线性的自回归(AR)模型和非线性的神经网络(NN)模型，并用模拟数据和实测数据对二者进行了比较。仿真结果表明，PAD效果良好，预测模型中，NN模型要优于AR模型。     

Applications of AR*-GRNN model for financial time series forecasting

AR* models contain Autoregressive Moving Average and Generalized Autoregressive Conditional Heteroscedastic class model which are widely used in time series. Recent researches in forecasting with Generalized Regression Neural Network (GRNN) suggest that GRNN can be a promising alternative to the linear and nonlinear time series models. In this paper, a model composed of AR* and GRNN is proposed to take advantage of their feathers in linear and nonlinear modeling. In the AR*-GRNN model, AR* modeling improves the forecasting performance of the combined model by capturing statistical and volatility information from the time series. The relative experiments testify that the combined model provides an effective way to improve forecasting performance which can be achieved by either of the models used separately.     

Large margin mixture of AR models for time series classification

In this paper, we propose the large margin autoregressive (LMAR) model for classification of time series patterns. The parameters of the generative AR models for different classes are estimated using the margin of the boundaries of AR models as the optimization criterion. Models that use a mixture of AR (MAR) models are considered for representing the data that cannot be adequately represented using a single AR model for a class. Based on a mixture model representing each class, we propose the large margin mixture of AR (LMMAR) models. The proposed methods are applied on the simulated time series data, electrocardiogram data, speech data for E-set in English alphabet and electroencephalogram time series data. Performance of the proposed methods is compared with that of support vector machine (SVM) based classifier that uses AR coefficients based features. The proposed methods give a better classification performance compared to the SVM based classifier. Being generative models, the LMAR and LMMAR models provide a generative interpretation that enables utilization of the rejection option in the high risk classification tasks. The proposed methods can also be used for detection of novel time series data.     

表面肌电信号的AR 参数模型分析方法

根据实际肌电信号的随机性特征，对其建立AR(Autoregressive)模型，得到其AR模型的各项参数，分析此系数和对应肌肉活动所确定的肢体动作之间的关系，从而得到基于动作模式的表面肌电信号(EMG)AR模型参数分析方法。