基于灰色关联理论的案例推理在故障智能诊断系统中的应用

从灰色关联理论出发，结合欧几里德距离，提出了一种新的相似度计算方法，并以装载机故障智能诊断系统为例，将该计算方法应用于案例的检索中；同时从案例的表示、案例的检索、案例库的更新等方面，讨论了基于灰色关联理论的案例推理在整个系统中的实现方法以及案例推理与其他诊断方式的结合策略。该计算方法大大提高了相似度的分辨率，并很好地解决了多指标因素相互美联情况下复合相似度的计算问题。     

基于神经网络和案例推理的智能诊断系统综述

系统介绍机械故障诊断技术的重要性和国内外发展现状、趋势的基础上,指出了人工神经网络理论应用于机械故障诊断技术具有极大的应用价值和发展潜力,和案例推理都是人工智能领域的代表技术.同时也指出,由于两种技术本身的缺陷和现代故障形式的不断复杂化,单独应用这些人工智能技术都不可避免的存在局限性.因此,对两者的结合进行了探讨.     

案例推理在轧机故障诊断系统的应用研究

轧机系统是个大型系统,其结构与功能较复杂,而且要求高精度与高可靠性,所以故障诊断较困难。从轧机故障特点、多种诊断方法对比,选出采用CBR进行轧机故障诊断,并对故障诊断系统的建立方法和系统设计等方面进行了探讨。     

基于混合推理的给水泵故障智能诊断系统

阐述模糊推理与规则推理相结合的混合诊断推理方法,提出给水泵振动故障诊断的数学模型.确定诊断流程,给出一个现场诊断实例,其诊断结果与现场检查结果基本一致,验证了这种方法用于给水泵振动故障诊断的可行性.     

基于案例推理的工程机械故障诊断智能化研究

智能化技术是实施故障诊断智能化的关键,作为人工智能领域新分支的CBR技术是一种较好的智能化技术,可用于故障诊断领域实现智能化故障诊断。分析了CBR故障诊断的机理,以工程机械智能化故障诊断系统为对象,运用CBR方法对系统故障诊断过程中案例搜集与整理、案例知识表示、案例检索与匹配、案例学习机制以及案例维护等关键技术进行了研究。结果表明,CBR方法是工程机械故障诊断智能化领域中一种行之有效的方法。     

一种新型的轧机齿轮箱

采用差动机构进行率分流，设计出一种新型结构齿轮箱，找出一种减轻重量降低成本的新途径。     

基于DSP的便携式故障智能诊断系统

便携式故障智能诊断系统运用DSP对机械设备运行中的噪声和振动信号实时分析,并将分析诊断结果通过LCD显示,还可利用DSP的USB接口将信号数据及其分析结果传输到计算机中.该系统具有快速诊断、低功耗等特点,满足了现场维修的实际需要.     

案例推理在多准则评价智能决策支持系统中的应用研究

案例推理是动态决策环境下求解下不良结构的常用方法,本文将基于案例的推理方法用于多准则综合评价中,具体讨了方案库和评价结果库的建立,并将其用关系数据库的格式进行存储,提出了基于灰色关联理论和模糊集理论相结合的相似度计算方法,从而可以准确地检索到相近案例,对人定方案给出定性或者定量的评价。     

宝钢1420轧机主传动齿轮箱的研制

本文介绍了１４２０轧机主传动齿轮箱的主要参数,结构特点,制造难点与要点和研制体会。     

Intelligent fault diagnostic system based on RBR for the gearbox of rolling mills

This paper presents an intelligent system that is necessary for diagnostic accuracy and efficiency in the iron and steel industry. A rule-based reseaning (RBR) intelligent diagnostic system has been developed based on many successful diagnostic applications. It can solve the difficulty in knowledge acquisition and has more precision. Its application results prove that the usability of the system is good and it will increasingly attain perfection.     

基于OPC技术的轧机主传动系统远程监测与诊断

提出了基于OPC技术的轧机主传动系统远程监测的结构体系,讨论了基于OPC技术的轧机主传动系统远程监测的实现方法,开发了基于OPC技术的轧机主传动远程监测与故障诊断系统.     

基于实例推理的柴油机故障诊断与维修

根据柴油机维修领域的专家知识和实践经验，运用实例推理，对柴油机的故障采取故障征兆和原因相结合的综合分析方法进行推理机制的设置，进而建立能确定维修方案的推理系统，并结合诊断实例加以说明。     

Intelligent fault diagnosis of rolling bearing and gear system under fluctuating load conditions using image processing technique

Health monitoring of a rotating machine is mainly done by investigation of the vibration patterns generated by the machine. Leveraging the fact that faults occurring in different parts of a machine generate unique fault signatures, a fault diagnosis methodology is proposed that can identify nine different healthy and faulty categories under varying load and noisy conditions. Neural network is employed for classification of faults in various categories. The robustness of features such as semivariance, kurtosis and Shannon entropy make them strong candidates to train the artificial neural network. The matching of vibration textural patterns with wave atom basis functions ensures removal of noise. As a result, the enhanced features used to train the neural network have led to high accuracy in classification. The algorithm is tested at various load conditions for both bearing and gear fault experimental data sets acquired by machinery fault simulator in laboratory. Simulation results show high degree of accuracy for both bearing and gear fault diagnosis under no load to heavy load noisy conditions.

     

Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension

The development of non-linear dynamic theory brought a new method for recognising and predicting the complex non-linear dynamic behaviour. Fractal dimension can quantitatively describe the non-linear behaviour of vibration signal. In the present paper, the capacity dimension, information dimension and correlation dimension are applied to classify various fault types and evaluate various fault conditions of rolling element bearing, and the classification performance of each fractal dimension and their combinations are evaluated by using SVMs. Experiments on 10 fault data sets showed that the classification performance of the single fractal dimension is quite poor on most data sets, and for a given data set, each fractal dimension exhibited different classification ability, this indicates that various fractal dimensions contain various fault information. Experiments on different combinations of the fractal dimensions demonstrated that the combination of all these three fractal dimensions gets the highest score, but the classification performance is still poor on some data sets. In order to improve the classification performance of the SVM further, 11 time-domain statistical features are introduced to train the SVM together with three fractal dimensions, and the classification performance of the SVM is improved significantly. At the same time, experimental results showed that the classification performance of the SVM trained with 11 time-domain statistical features in tandem with three fractal dimensions outperforms that of the SVM trained only with 11 time-domain statistical features or with three fractal dimensions.     

齿轮故障诊断技术现状与展望

介绍了齿轮故障理论及诊断技术的现状;对齿轮故障机理研究、齿轮故障简易诊断技术、精密诊断技术、诊断技术最新发展进行了分类阐述,并对齿轮故障诊断技术的未来发展方向提出了看法。     

Intelligent fault diagnosis of rolling bearing based on kernel neighborhood rough sets and statistical features

Intelligent fault diagnosis benefits from efficient feature selection. Neighborhood rough sets are effective in feature selection. However, determining the neighborhood value accurately remains a challenge. The wrapper feature selection algorithm is designed by combining the kernel method and neighborhood rough sets to self-adaptively select sensitive features. The combination effectively solves the shortcomings in selecting the neighborhood value in the previous application process. The statistical features of time and frequency domains are used to describe the characteristic of the rolling bearing to make the intelligent fault diagnosis approach work. Three classification algorithms, namely, classification and regression tree (CART), commercial version 4.5 (C4.5), and radial basis function support vector machines (RBFSVM), are used to test UCI datasets and 10 fault datasets of rolling bearing. The results indicate that the diagnostic approach presented could effectively select the sensitive fault features and simultaneously identify the type and degree of the fault.     

LabVIEW和MATLAB混合编程在齿轮箱故障诊断系统中的应用

为了实现轧机传动部件的早期故障诊断,利用LabVIEW便捷的图形界面和MATLAB强大的数值分析功能,开发了一套齿轮箱故障诊断系统.通过LabVIEW调用MATLAB中的小波工具箱,并结合包络解调等方法实现故障信息的准确提取.经过模拟故障数据和高线精轧机故障的诊断实践,表明该系统运行可靠,并且能更早地识别故障隐患.