基于故障树的贝叶斯网络建造方法与故障诊断应用

文章首先指出应用贝叶斯网络模型进行设备故障诊断具有的优势,提出了由常用的故障树模型建造贝叶斯网络的方法。然后详细比较了故障树与贝叶斯网络在诊断推理和模型表达方面的特点,并以实例进行说明。     

随机共振系统的电路模拟及在微弱信号增强传输中的应用

采用电子线路仿真双稳随机共振系统,建立微弱信号增强传输单元.实验结果表明,该装置适合于低信噪比条件下微弱正弦信号的检测,并且可以有效增强周期脉冲信号及二进制数字脉冲信号的传输,为微弱故障特征信号的提取以及数字信号的远程传输中提供了一种实用装置.     

基于PXI技术的涡轮泵实时状态监控演示系统设计

针对某型火箭发动机地面试车时间短、工作转速高、且不可重复的特点,根据开放性、有效性和可靠性的系统设计原则,采用PXI技术和虚拟仪器技术,设计了一套涡轮泵实时状态监控演示系统.该系统为评定涡轮泵健康状态、诊断发动机故障奠定了基础.     

基于物理模型和修正灰色模型的行星轮系疲劳裂纹故障预测方法

结合物理模型与灰色理论,提出行星轮系齿根疲劳裂纹故障预测的新思路.针对直升机主传动系统中的2k-H行星轮系,建立太阳轮齿根疲劳裂纹损伤的物理基模型,通过仿真获得不同损伤严重度的振动仿真信号.选择并计算仿真信号的故障特征矢量,并以此作为损伤特征的标准模式,对待检信号特征矢量与标准模式进行灰色关联度分析,根据关联度对裂纹进行定量检测.结合物理模型仿真信号对灰色预测模型进行修正,使之具有更好的疲劳裂纹故障预测能力.对试验中的疲劳裂纹进行定量检测和故障预测.试验数据验证了本方法对行星轮系太阳轮疲劳裂纹的定量检测和故障预测能力.     

Novelty detection methods for online health monitoring and post data analysis of turbopumps

As novelty detection works when only normal data are available, it is of considerable promise for health monitoring in cases lacking fault samples and prior knowledge. We present two novelty detection methods for health monitoring of turbopumps in large-scale liquid-propellant rocket engines. The first method is the adaptive Gaussian threshold model. This method is designed to monitor the vibration of the turbopumps online because it has minimal computational complexity and is easy for implementation in real time. The second method is the one-class support vector machine (OCSVM) which is developed for post analysis of historical vibration signals. Via post analysis the method not only confirms the online monitoring results but also provides diagnostic results so that faults from sensors are separated from those actually from the turbopumps. Both of these two methods are validated to be efficient for health monitoring of the turbopumps.     

FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox's faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.     

神经网络学习算法的过拟合问题及解决方法

针对反向传播学习算法及其改进算法中出现的过拟合问题，探讨了三种解决方法；调整法、提前停止法和陷层节点自生成法，并用实例对三种方法进行了验证和比较。其中，调整法和提前停止法针对一个较大的网络可以解决过拟合问题，而隐层节点自生成法的提出既能避免过拟合问题，又能获得最少神经元网络结构。这三种方法有效地解决了在神经网络学习过程中的近拟合问题，提高了网络的适应性，它们不仅适合于函数逼近，而且可以推广到其他网络结构等应用领域。     

涡轮泵实时故障检测的多特征参量自适应阈值综合决策算法

为了实时监控液体火箭发动机涡轮泵的状态,提高安全性,降低故障带来的破坏程度,提出了一种多特征参量自适应阈值综合决策算法(MATA)。研究了该算法的特征参量选取、阈值区间的确定、阈值的自适应计算(包括特征参量均值与标准方差的自适应计算)、故障综合决策逻辑、故障数据对阈值贡献的剔除等方法,利用某型火箭发动机地面试车涡轮泵振动测量数据和某型转子试验平台实时测量数据对该算法进行离线和实时在线故障检测试验验证,结果表明MATA没有发生误检测情况,具有实时故障检测的能力。因此,MATA适合于液体火箭发动机涡轮泵的实时故障检测。     

Extended stochastic resonance (SR) and its applications in weak mechanical signal processing

To catch symptoms of machine failure as early as possible, one of the most important strategies is to apply more progressive techniques during signal processing. This paper presents a method based on stochastic resonance (SR) to detect weak fault signal. First, a discrete model of a bistable system that can demonstrate SR is researched, and the stability condition for controlling the selection of model parameters of the discrete model and guarantee the solving convergence are established. Then, the frequency range of the weak signals that the SR model can detect is extended through a type of normalized scale transformation. Finally, the method is applied to extract the weak characteristic component from heavy noise to indicate the little crack fault in a bearing outer circle.     

Jeffcott转子碰摩故障试验研究

针对工种实际中遇到的机组转子摩故障开展了试验研究,通过大量的试验,观测到了工作转子速低于一阶临界转速时,转子定子局部碰摩引起的倍频振动,高于一阶临界转速时,局部碰摩引起的分频振 及某些转速内出现的异频伪共振现象,试验民其于碰摩力模型和仿真结果定性一致,试验还表明,异频伪共振是高速工作的转子发生碰摩时的主要振动成分,这一结论对诊断和抑制高速转子的异常超标振动有积极意义。