基于Hilbert变换的包络分析及其在滚动轴承故障诊断中的应用

讨论了Hilbert变换的基本原理,以及基于Hilbert变换的包络解调方法在轴承故障诊断中的应用。实践表明:对于具有调制现象的滚动轴承故障诊断,基于Hilbert变换的包络解调方法,具有明显的诊断意义,是一种可靠的诊断方法。     

基于瞬时转速信号的柴油机角域转换方法

针对往复机械故障分析诊断中通过平均转速等间隔划分角度的方法诊断效果不精确的问题,提出了一种利用瞬时转速对柴油机时域信号进行角域转换的方法。分析了转速波动较大工况下平均转速等角度划分方法中影响相位偏差产生的主要因素,根据转速波动量的大小以及预期的角域精度范围设定阈值参数,再通过该阈值实现角域转换方法的自动选择。研究结果表明,在转速波动工况下采用瞬时转速对时域信号进行角域转换能够有效避免相位偏差,为基于角域的柴油机故障诊断奠定基础。     

基于案例推理的旋转机械故障诊断系统研究

针对大型旋转机械结构复杂,故障现象难以用结构化知识表示,故障诊断规则难以提炼的特点,将基于案例推理(CBR)方法应用于旋转机械故障诊断。从旋转机械故障诊断的需求出发,在分析旋转机械故障诊断知识特点的基础上,对故障诊断系统的总体结构、故障案例库的构建、案例相似度匹配、案例调整和学习等CBR方法的关键技术进行了研究。重点设计了故障案例表示方法,采用基于三标度的层次分析法(AHP)确定案例征兆权值。提出了改进的最近邻法计算案例相似度,可以从征兆名称、征兆值、权值三方面对案例进行精确匹配。提出了基于案例审核的学习机制,可以充分发挥不同人员的作用。开发了基于CBR的旋转机械故障诊断系统原型,并给出了应用实例。     

基于CART决策树的柴油机故障诊断方法研究

采用一种自适应局部有效值（RMS）计算方法提取柴油机缸盖振动信号时域特征,结合分类回归树（CART）算法建立故障分类模型并进行柴油机的状态识别。通过实验获取柴油机失火和撞缸两种故障工况及正常工况下的振动数据,计算出原始信号的局部RMS后,根据自适应阈值确定点火冲击区域和非点火上止点冲击区域提取局部特征,最后将特征输入CART算法中构建分类模型来验证所提取特征的有效性。结果表明：柴油机在3种状态下的识别率均达到100%,基于CART算法和局部特征提取的方法能够有效诊断柴油机故障。     

故障自愈工程研究

介绍了现代医学自愈到治愈的发展历程，比较了装备与人的异同点．在系统学理论和现代医学中自恢复理论的基础上，提出了过程装备自愈调控原理，包括免疫、防御、代偿、自修复和适应等，可以用来指导研究装备自愈原理及工程应用；重点研究了以故障预防和消除为目标的装备系统自愈调控原理，为研制出具有自愈功能的新一代装备提供了科学依据．同时建立了3套针对不同工况的FSR模型，来模拟FSR系统的工作情况．     

内燃机工况对气缸敲击振动的影响探究

为了更深入地了解内燃机工况与敲缸振动的关系,通过建立数学模型和仿真计算验证了内燃机气缸敲击振动峰峰值与发动机转速的非线性关系及与发动机负载的线性关系。内燃机扭矩平衡方程包含了发动机转速、负载和动力扭矩等信息,通过动力扭矩与活塞所受侧推力之间的几何关系将活塞径向加速度引入扭矩平衡方程,以活塞径向加速度在燃烧膨胀冲程上止点处的冲击信号峰峰值来表征敲缸振动的剧烈程度,整理后的数学模型显示加速度振动与内燃机转速为非线性关系、与负载为线性关系,经AVL-EXCITE仿真计算验证了数学模型的正确性。研究结果可以帮助在线监测系统和故障诊断人员根据敲缸振动对转速和负载的敏感性判断是否发生敲缸,为敲缸故障预警提供参考依据。     

连续排液式自动平衡头的自适应模糊PID控制算法研究

针对持续注液平衡头系统控制对象的大惯性、非线性、纯滞后等特性,设计了一种改进的模糊PID控制器所设计的控制系统比常规PID控制系统具有较强鲁棒性,系统具有良好的适应能力,其有很好的快速性和很小的超调。     

Research on fault diagnosis method of piston rod based on harmonic wavelet and manifold learning

As the core part of reciprocating compressor,piston rod is easy to cause a serious accident when abrasion and breakage fault occur to it. Therefore,it is very important to monitor its running state. At present,a small number of reciprocating compressors have been installed on-line monitoring and diagnosis system,most of which can only monitor a single vertical subsidence of piston rod and it can't fully represent the running state of piston rod. Therefore,a method of monitoring the vertical and horizontal displacement of piston rod axis orbit is simultaneously used. In view of the characteristics that the piston rod axis orbit is disordered and difficult to extract features,purification of the axis orbit is carried out based on harmonic wavelet and then features are extracted such as vibration energy,natural frequency and the axis orbit envelope area. After that,a nonlinear local tangent space manifold learning algorithm is used to reduce the dimension of the features and obtain sensitive features. By analyzing the practical cases,the effectiveness of the method for fault monitoring and diagnosis of reciprocating compressor piston rod assembly has been verified. Finally,as BP neural network has the characteristics of solving complex nonlinear problems,the validity of the fault diagnosis method of reciprocating compressor piston rod based on harmonic wavelet and manifold learning is proved by actual case data analysis based on BP neural network.     

Research on mechanical wear life feature fusion prediction method based on temporal pattern attention mechanism

In order to solve the problem of low prediction accuracy when only vibration or oil signal is used to predict the remaining life of gear wear, a gear wear life feature fusion prediction method based on temporal pattern attention mechanism is proposed. Firstly, deep residual shrinkage network(DRSN) is used to extract the features of the original vibration time series signals with low signal-tonoise ratio, and the vibration features associated with gear wear evolution are obtained. Secondly,the ex...