基于知识图谱的潜油电泵井故障诊断

潜油电泵井系统是油田开采重要工具,具有排量大、扬程高与作业环境灵活多变等优点.为了降低潜油电泵井系统故障危害,需要对其发生故障部件进行快速精确定位并维修.本文提出一种基于知识图谱的潜油电泵井故障诊断方法.采用改进BiLSTM-CRF实体识别算法与BERT关系抽取算法提取故障数据中的专家知识,构建潜油电泵井故障诊断领域知识图谱;利用构建知识图谱搭建以故障征兆为初始节点的贝叶斯推理网络,利用历史故障数据与条件概率解耦的计算方式推理出故障原因.本文通过故障诊断真实案例进行方法验证.     

Multi-fault classification based on support vector machine trained by chaos particle swarm optimization

A novel method of training support vector machine (SVM) by using chaos particle swarm optimization (CPSO) is proposed. A multi-fault classification model based on the SVM trained by CPSO is established and applied to the fault diagnosis of rotating machines. The results show that the method of training SVM using CPSO is feasible, the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine, the precision and reliability of the fault classification results can meet the requirement of practical application.     

水电数字仿真在线融合故障诊断专家系统

为了解决丰满水电数字仿真系统的在线故障诊断问题，基于信息与方法融合的思想，提出了分布式集成神经网络建模方法、模糊神经网络专家系统(FNNES)在线故障诊断方法。将模糊神经网络(FNN)嵌入专家系统(ES)中，FNN负责知识获取和逻辑推理，ES负责系统信息的输入和输出、符号推理，并对FNN的结论进行解释。系统的运行验证了方法的有效性和实际应用价值。为现场诊断系统的开发提供了有益的方法和经验。     

基于IMF能量矩和Bayes-LSSVM的滚动轴承诊断

针对传统方法对滚动轴承故障特征提取效果尚有局限和最小二乘支持向量机分类器的参数不易确定,从而降低了故障诊断的准确性的问题,提出基于本征模函数能量矩和贝叶斯框架下的最小二乘支持向量机实现滚动轴承的故障诊断。在该方法中,通过经验模态分解将原始信号分解为多个本征模函数,之后将本征模函数作时间轴的积分,得到本征模函数能量矩特征故障向量。采用贝叶斯推理方法进行三级分层推断,解决最小二乘支持向量机分类器的参数具有任意性和不确定性的问题,实现参数优化。对滚动轴承的仿真结果表明,该方法能对故障进行有效、准确的诊断,诊断正确率达到98.75%。     

模糊神经网络变幅液压系统故障诊断

针对变幅液压系统复杂性、不确定性、模糊性的特点,提出基于故障树的模糊神经网络作为变幅液压系统故障诊断的方法。该方法利用故障树知识提取变幅液压系统故障诊断的输入变量和输出变量,引入模糊逻辑的概念,采用模糊隶属函数来描述这些故障的程度,利用Levenberg-Marquardt优化算法对神经网络进行训练,系统推理速度快,容错能力强,并通过实例分析验证了变幅液压系统模糊神经网络故障诊断的有效性。     

Deep continual transfer learning with dynamic weight aggregation for fault diagnosis of industrial streaming data under varying working conditions

Catastrophic forgetting of learned knowledges and distribution discrepancy of different data are two key problems within fault diagnosis fields of rotating machinery. However, existing intelligent fault diagnosis methods generally tackle either the catastrophic forgetting problem or the domain adaptation problem. In complex industrial environments, both the catastrophic forgetting problem and the domain adaptation problem will occur simultaneously, which is termed as continual transfer problem. Therefore, it is necessary to investigate a more practical and challenging task where the number of fault categories are constantly increasing with industrial streaming data under varying operation conditions. To address the continual transfer problem, a novel framework named deep continual transfer learning network with dynamic weight aggregation (DCTLN-DWA) is proposed in this study. The DWA module is used to retain the diagnostic knowledge learned from previous phases and learn new knowledge from the new samples. The adversarial training strategy is applied to eliminate the data distribution discrepancy between source and target domains. The effectiveness of the proposed framework is investigated on an automobile transmission dataset. The experimental results demonstrate that the proposed framework can effectively handle the industrial streaming data under different working conditions and can be utilized as a promising tool for solving actual industrial problem.     

基于补偿神经网络的航空电子故障智能诊断系统及应用

针对航空电子系统中故障诊断的问题，提出一种将神经网络中的BP算法与模糊逻辑系统相结合、自动产生并自动修正模糊规则的自适应的模糊逻辑推理机。通过函数逼近仿真分析和航空电子系统故障诊断的实际应用，证明此方法简单有效，故障诊断的精度高，取得了较好的效果，具有一定的应用前景。     

旋转机械振动故障诊断专家系统

针对旋转机械故障诊断智能化程度不高的现状,采用可视化语言LabVIEW作为故障诊断专家系统前端开发平台,采用MySQL作为系统后台数据库,开发了一个基于产生式规则表示知识的旋转机械故障诊断专家系统,介绍了该系统的总体结构、知识库、推理机的设计;最后给出该专家系统的实例验证,在INV1612转子实验平台上,添加动平衡配重钉一枚,提高转子转速至1 800r/min,验证了系统对转子质量不平衡故障诊断的可行性,去掉配重钉,提高转子转速至6 100r/min,验证了系统对油膜涡动故障诊断的有效性;实验结果表明,文中开发的故障诊断专家系统具有一定的实用价值。     

基于本体不确定性推理的故障诊断

针对航空维修行业内不同角色企业故障诊断知识、系统、资源的分散、封闭、异构等原因导致的故障知识不易共享、重用的问题,采用本体来构建故障知识库;并设计其本体知识模型结构,以表达故障现象与故障结论间存在的不确定性关系;进一步编写相应的推理规则,设计基于本体的不确定性推理系统。以飞机发动机故障诊断为例进行了验证。     

Application of an intelligent classification method to mechanical fault diagnosis

A new method for intelligent fault diagnosis of rotating machinery based on wavelet packet transform (WPT), empirical mode decomposition (EMD), dimensionless parameters, a distance evaluation technique and radial basis function (RBF) network is proposed in this paper. In this method, WPT and EMD are, respectively, used to preprocess vibration signals to mine fault characteristic information more accurately. Then, dimensionless parameters in time domain are extracted from each of the original vibration signals and preprocessed signals to form a combined feature set. Moreover, the distance evaluation technique is utilised to calculate evaluation factors of the combined feature set. Finally, according to the evaluation factors, the corresponding sensitive features are selected and input into the RBF network to automatically identify different machine operation conditions. An experiment of rolling element bearings is carried out to test the performance of the proposed method. The experimental result demonstrates that the method combining WPT, EMD, the distance evaluation technique and the RBF network may accurately extract fault information and select sensitive features, and therefore it may correctly diagnose the different fault categories occurring in the bearings. Furthermore, this method is applied to slight rub fault diagnosis of a heavy oil catalytic cracking unit, the actual result shows the method may be applied to fault diagnosis of rotating machinery effectively.