基于 MPDE-EEMD 及自适应共振解调的轴承故障特征提取方法

针对滚动轴承振动信号具有非线性、非平稳性和非高斯性,并且故障特征往往淹没于系统噪声之中而难于识别的问题, 提出了以多种群差分进化(multiple population differential evolution, MPDE) 算法来改进集合经验模式分解( ensemble empirical mode decomposition, EEMD) 的 MPDE-EEMD 消噪方法,并与自适应共振解调技术( adaptive resonance demodulation technique, ARDT)相结合实现故障特征提取。 首先,为了解决 EEMD 中加入参数依靠人工选择且难以准确获取的问题,建立极值点分布 特性评价函数,利用 MPDE 来寻优获取最佳白噪声幅值,实现 EEMD 自适应分解。 然后,采用峭度与相关性相结合的准则对分 解后的 IMF 分量进行自动筛选,将满足条件的有效信号进行重构,实现对原始振动信号的降噪处理。 最后,采用 ARDT 自动确 定对消噪信号进行带通滤波的带宽和中心频率,再通过包络解调提取出滤波信号的特征频率。 将轴承仿真故障信号与实际故 障信号用于算法的验证,结果表明 MPDE-EEMD+ARDT 能有效提取出轴承故障特征。     

叠合盆地凸起区多期复杂断裂特征及形成机制——以准噶尔盆地车排子凸起为例

以高精度三维地震资料为基础,结合构造演化恢复和区域构造背景,系统研究了准噶尔盆地车排子凸起断裂分布和形成机制。结果表明,车排子凸起发育深部和浅部2套断裂体系,深部断裂体系和浅部断裂体系分别发育3种断裂样式。车排子凸起共经历5个构造演化阶段,其中,晚二叠世挤压-逆冲控制了深部断裂体系的初始形成,晚侏罗世逆冲-走滑控制了深部断裂体系的最终定型,新近纪叠加掀斜、局部伸展控制了浅部断裂体系的形成。不同断裂体系在油气成藏过程中均起到了重要作用。其中:深部断裂体系的红车断裂是重要的油源断裂,低序级断层控制形成了石炭系火山岩优质储集体;浅部断裂体系使得油气向浅部层系运移调整,同时形成了断块圈闭、断鼻圈闭和断层-岩性圈闭等丰富的圈闭类型。     

SFEP文本因果关系提取及其与SFN转化研究

为将系统故障演化过程(system fault evolution process，SFEP)的文本描述转化为空间故障网络(space fault network，SFN)结构，用于故障分析，本文提出SFEP文本因果关系提取方法，及其与SFN基本结构的转化方法。首先给出SFEP中事件的几种典型因果关系。随后提出因果关系与SFN基本结构的转化流程。本文方法围绕着关键字和因果关系组模式展开，通过模型的不断学习补充和丰富关键字和组模式。最终使方法具备将SFEP文本转化为SFN结构的能力。以飞机起落架故障发生过程文本为例进行了应用，实验结果表明该方法可用于SFEP文本中的因果关系分析，并得到了理想的SFN。完善的关键字和组模式有利于使用计算机智能处理SFEP的SFN。     

A Bayesian approach to diagnosing covariance matrix shifts

In addition to the quick detection of abnormal changes in a multivariate process, it is also critical to provide an accurate fault identification of responsible components following an out-of-control signal. In line with the work of Tan and Shi for diagnosing shifts in the mean vector, this paper develops a Bayesian approach for diagnosing shifts in the covariance matrix. The simulation comparisons favor the proposed approach. A real example is also presented to demonstrate the implementation of the proposed method.     

Fault detection and diagnostic method of diesel engine by combining rule-based algorithm and BNs/BPNNs

The stable operation of diesel engine is critical to the normal production of the industry, and the prevention, monitoring, and identification of faults are of great significance. At present, the fault research on diesel engines still has some defects, such as only few types of faults diagnosis are identified, the accuracy of fault diagnosis is still low, and fault identification is located at a fixed speed. A novel fault detection and diagnostic method of diesel engine by combining rule-based algorithm and Bayesian networks (BNs) or Back Propagation neural networks (BPNNs) is proposed. The signals are processed by wavelet threshold denoising and ensemble empirical mode decomposition. The signal-sensitive feature values are extracted from the decomposed intrinsic mode function. Seven faults are roughly identified using rule-based algorithm and finely identified using BNs or BPNNs. Results show the proposed fault diagnosis method has a good diagnostic performance for a wide range of rotation speeds when the training data for BNs and BPNNs are from fixed speeds. In addition, the influences of the layers of decomposed signals, sensor noise and external excitation interference on the fault diagnostic performance are also researched.     

基于人工神经网络的柴油机失火故障诊断

车载诊断系统在诊断失火故障时,采用基于曲轴段角加速度和阈值规则相结合的方法,该方法在内燃机高速轻载运行时诊断单缸完全失火工况存在一定的局限性。通过对比分析失火和正常工况下曲轴瞬时转速的幅频和相频特征,提取不同谐次的幅值和相位信息,结合人工神经网络作为故障模式识别工具,得到了一种改善方法。通过台架实验,对此改善方法进行了单缸完全失火、两缸完全失火和单缸一定程度失火的故障诊断测试。结果表明,在实验条件下该方法可以有效识别不同的失火模式,并可在单缸失火模式下实现失火程度判别。同时,该方法通过少量工况数据训练神经网络,即可实现一定转速范围内的失火诊断,可行性强,可用于发动机失火故障在线诊断。     

基于KJADE的列车轴承轨边声学诊断方法研究

为在线诊断运行列车的轴承状态,提出一种基于核特征矩阵联合近似对角化(kernel joint approximate diagonalization of eigen-matrices,简称KJADE)的列车轴承轨边声学故障诊断方法。首先,从校正后的轨边信号中提取原始特征,将其通过非线性映射函数映射到高维特征空间;其次,对特征空间的核矩阵进行四阶累积量的特征分解,获得新融合特征,并采用支持向量机分类器对融合特征进行辨识;最后,对轴承外圈、内圈、滚子故障和正常4种状态下的列车轨边声学信号进行分析。结果表明,该方法可以有效实现对列车轴承轨边声音信号的非线性特征提取,提高了故障的识别率。     

Sparse decomposition method based on time–frequency spectrum segmentation for fault signals in rotating machinery

The impulse signal in large rotating machinery with damage fault is sparse, weak, coupled, and even nonperiodic in intermittent operation. To extract this complex signal is a key topic in machinery fault diagnosis. Sparse decomposition (SD) has excellent adaptability in describing arbitrary complex signals based on over-complete dictionary. However, the pursuit speed of best atom is a serious drawback. To alleviate this, a method of sparse decomposition based on time–frequency spectrum segmentation (SD-TFSS) is introduced. Generalized S transform (GST) provides the capability to show the distribution of vibration signals, but the resolution is susceptible to noise, multiresolution generalized S-transform (MGST) is developed to generate multiresolution time–frequency spectrums. Then, spectrums fusion with an appropriate threshold is adopted to acquire multiresolution binary spectrums and produce an optimal binary spectrum. From this optimal binary spectrum, all the connectivity areas are extracted and marked by spectrum segmentation. Thus, an optimal library can be constructed by selecting the optimal atoms of every connectivity area, and the signal can be expressed with this library. We conduct simulations and experiments demonstrating that the proposed method performs well with lower pursuit complexity, higher decomposition efficiency, and better approximation precision.     

煤矿井下探测中影响不含水断层视电阻率变化的单一主控因素分析

选取晋城矿区典型3号无烟煤试样，在实验室进行单轴加载条件下的电阻率测试实验，并开展矿井瞬变电磁法探测不含水断层现场试验，研究了不含水断层的视电阻率变化特征。结果表明:煤样加载全过程，经过压密阶段、弹性变形阶段、塑性变形阶段、峰值后破裂阶段、残余变形阶段5个阶段；煤样应力应变曲线表现为由低到高再到低的特征，煤样电阻率曲线表现为由高到低再到高的特征；加载过程中，压密阶段、弹性变形阶段应力越大电阻率越小，应力为影响电阻率变化的主控因素；塑性变形阶段，电阻率先减小后增大，应力和裂隙共同影响电阻率变化，由应力为主要因素逐渐转变到裂隙为主要因素；峰值后破裂阶段和残余变形阶段电阻率急速增大，裂隙为影响电阻率变化的主控因素；通过瞬变电磁法探测不含水断层试验，发现不含水断层导致视电阻率明显升高，表明裂隙为影响断层视电阻率单一变化的主要因素。     

煤巷过断层破碎带施工工艺与支护技术实践

W1313回风顺槽在里程332 m处揭露正断层,对此采取了调整巷道顶板锚网索支护参数,采用圆木大板棚复合支护等措施,并加强了顶板、通风、掘锚机破岩、爆破作业和作业人员的安全管理,确保工作面过断层破碎带时的安全施工。