Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases

Multi-faults detection is a challenge for rolling bearings due to the mode mixture and coupling of multiple fault features, as well as its easy burying in the complex, non-stationary structural vibrations and strong background noises. In this paper, a method based on the flexible analytical wavelet transform (FAWT) possessing fractional scaling and translation factors is proposed to identify multiple faults occurred in different components of rolling bearings. During the route of the proposed method, the proper FAWT bases are constructed via genetic optimization algorithm (GA) based on maximizing the spectral correlated kurtosis (SCK) which is firstly presented and proved to be efficient and effective in indicating interested fault mode. Via using the customized FAWT bases for each interested fault mode, the original vibration measurements are decomposed into fine frequency subbands, and the sensitive subband which enhances the signal-to-noise ratio (SNR) is selected to exhibit the fault signature on its envelope spectrum. The proposed method is tested via simulated signals, and applied to analyze the experimental vibration measurements from the running roller bearings subjected to outrace, inner-race and roller defects. The analysis results validate the effectiveness of the proposed method in identifying multi-faults occurred in different components of rolling bearings.     

A progressive approach to predict shot peening process parameters for forming integral panel of Al7050-T7451

In this paper, a progressive approach to predict the multiple shot peening process parameters for complex integral panel is proposed. Firstly, the invariable parameters in the forming process including shot size, mass flow, peening distance and peening angle are determined according to the empirical and machine type. Then, the optimal value of air pressure for the whole shot peening is selected by the experimental data. Finally, the feeding speed for every shot peening path is predicted by regression equation. The integral panel part with thickness from 2 mm to 5 mm and curvature radius from 3200 mm to 16000 mm is taken as a research object, and four experiments are conducted. In order to design specimens for acquiring the forming data, one experiment is conducted to compare the curvature radius of the plate and stringer-structural specimens, which were peened along the middle of the two stringers. The most striking finding of this experiment is that the outer shape error range is below 3.9%, so the plate specimens can be used in predicting feeding speed of the integral panel. The second experiment is performed and results show that when the coverage reaches the limit of 80%, the minimum feeding speed is 50 mm/s. By this feeding speed, the forming curvature radius of the specimens with different thickness from the third experiment is measured and compared with the research object, and the optimal air pressure is 0.15 MPa. Then, the plate specimens with thickness from 2 mm to 5 mm are peened in the fourth experiment, and the measured curvature radius data are used to calculate the feeding speed of different shot peening path by regressive analysis method. The algorithm is validated by forming a test part and the average deviation is 0.496 mm. It is shown that the approach can realize the forming of the integral panel precisely.     

电液位置伺服系统的动态试验研究

本文研究了快速响应的电液位置伺服系统动态试验,着重讨论了在实践中如何选择伪随机二位式最大长度序列(PRBS)参数的问陋,并提出快速响应系统PRBS参数选择的原则,使系统辨识精度提高约15％。     

Ionospheric behavior of the F2 peak parameters foF2 and hmF2 at Hainan and comparisons with IRI model predictions

Monthly median values of foF2, hmF2 and M(3000)F2 parameters, with hourly time interval resolution for the diurnal variation, obtained with DPS-4 digisonde observations at Hainan (19.4°N, 109.0°E) are used to study the low latitude ionospheric variation behavior. The observational results are compared with the International Reference Ionospheric Model (IRI) predictions. The time period coverage of the data used for the present study is from March 2002 to February 2005. Our present study showed that: (1) In general, IRI predictions using CCIR and URSI coefficients follow well the diurnal and seasonal variation patterns of the experimental values of foF2. However, CCIR foF2 and URSI foF2 IRI predictions systematically underestimate the observed results during most time period of the day, with the percentage difference ΔfoF2 (%) values changing between about −5% and −25%, whereas for a few hours around pre-sunrise, the IRI predictions generally overestimate the observational ones with ΔfoF2 (%) sometimes reaching as large as ∼30%. The agreement between the IRI results and the observational ones is better for the year 2002 than for the other years. The best agreement between the IRI results and the observational ones is obtained in summer when using URSI coefficients, with the seasonal average values of ΔfoF2 (%) being within the limits of ±10%. (2) In general, the IRI predicted hmF2 values using CCIR M(3000)F2 option shows a poor agreement with the observational results. However, when using the measured M(3000)F2 as input, the diurnal variation pattern of hmF2 given by IRI2001 has a much better agreement with the observational one with the detailed fine structures including the pre-sunrise and post-sunset peaks reproduced reasonably well. The agreement between the IRI predicted hmF2 values using CCIR M(30,000)F2 option and the observational ones is worst for the afternoon to post-midnight hours for the high solar activity year 2002. During daytime hours the agreement between the hmF2 values obtained with CCIR M(30,000)F2 option and the observational ones is best for summer season. The discrepancy between the observational hmF2 and that obtained with CCIR M(30,000)F2 option stem from the CCIR M(3000)F2 model, which does not produce the small scale structures observed in the measured M(3000)F2.     

基于因子模糊化BP神经网络的磨损颗粒识别

在引入了一套磨粒形态学描述子来提取磨损颗粒的显微形态特征的基础上 ,采用人工神经网络技术 ,编制了用于磨损颗粒自动识别的 BP网络计算机模拟程序。在网络训练的过程中应用了本文引入的因子模糊化训练方法 ,使训练速度大大加快 ,以异或问题为例 ,速度可提高 5～ 1 0倍。应用此网络对磨粒测试库进行识别实验 ,识别正确率在 90 %以上 ,并且识别速度很快 ,大大优于传统的磨粒识别方法。     

尺寸参数对气动液阀启动特性的影响

为气动液阀的启动过程建立了数学模型，分析了尺寸参数对该阀启动特性的影响。计算结果表明：在保证必要的工作寿命的前提下，适当地增大控制腔气孔的直径或增大靠近控制腔的活塞端面直径有利于提高该阀的响应能力。本文所得的结论有利于此类阀门的设计。     

自适应控制方法的直接命中目标本体的平行接近制导律

本文在最少量测条件下采用视线角速度ｑ趋于零的指标函数和低阶等效辨识模型与快速自适应控制方法实现了战术导弹的平行接近制导律。这种最优制导律可以实现直接命中目标本体，实现一弹多用，可以用于反导弹的控制。能使制导装置简化并使其尺寸和重量减小，算法简单易于工程使用。经过大量数学和半实物仿真试验表明，上述成果可应用于实际工程当中。     

空间智能桁架自适应模糊振动控制

研究了一种基于自适应模糊控制器的空间智能桁架振动控制方法。在考虑剩余模态影响的条件下建立了空间智能桁架的独立模态空间振动控制方程,并对自适应模糊控制器作改进,证明了控制系统的稳定性。仿真结果表明:该自适应模糊控制器可有效抑制桁架振动,控制效果明显优于传统模糊控制,同时能抑制控制溢出和观测溢出。     

线性回归模型岭迹选择主元方法及应用研究

针对线性回归模型的多重复共线性问题，在理论上，引入方差膨胀因子，从量化的角度探测线性回归模型的多重复共线性；并采用岭迹分析法对此类模型的结构辨识方法展开研究，归纳了岭迹选择主元的若干原则，并通过实际计算证实了该方法的有效性。     

固体火箭发动机试验模态分析技术研究

将固体火箭发动机划分为144个自由度，采用锤击法，单点激励多点响应（SIMO）对其在4种工况下进行了频率响应的测量，应用了多种方法进行模态参数识别和数据拟合，并对其结果进行比较，获得了发动机在各工况1kHz以内的各阶固有频率、振型和阻尼，并对试验模态分析技术在固体火箭发动机试验中的应用进行了研究。