Hydraulic systems have the characteristics of strong fault concealment,powerful nonlinear time-varying signals,and a complex vibration transmission mechanism;hence,diagnosis of these systems is a challenge.To provide ...Hydraulic systems have the characteristics of strong fault concealment,powerful nonlinear time-varying signals,and a complex vibration transmission mechanism;hence,diagnosis of these systems is a challenge.To provide accurate diagnosis results automatically,numerous studies have been carried out.Among them,signal-based methods are commonly used,which employ signal processing techniques based on the state signal used for extracting features,and further input the features into the classifier for fault recognition.However,their main deficiencies include the following:(1)The features are manually designed and thus may have a lack of objectivity.(2)For signal processing,feature extraction and pattern recognition are conducted using independent models,which cannot be jointly optimized globally.(3)The machine learning algorithms adopted by these methods have a shallow architecture,which limits their capacity to deeply mine the essential features of a fault.As a breakthrough in artificial intelligence,deep learning holds the potential to overcome such deficiencies.Based on deep learning,deep neural networks(DNNs)can automatically learn the complex nonlinear relations implied in a signal,can be globally optimized,and can obtain the high-level features of multi-dimensional data.In this paper,the main technology used in an intelligent fault diagnosis and the current research status of hydraulic system fault diagnosis are summarized and analyzed.The significant prospect of applying deep learning in the field of intelligent fault diagnosis is presented,and the main ideas,methods,and principles of several typical DNNs are described and summarized.The commonality between a fault diagnosis and other issues regarding typical pattern recognition are analyzed,and research ideas for applying DNNs for hydraulic fault diagnosis are proposed.Meanwhile,the research advantages and development trend of DNNs(both domestically and overseas)as applied to an intelligent fault diagnosis are reviewed.Furthermore,the fault characteristics of a c展开更多
提出了基于径向基函数(Radial Basis Function,RBF)网络和有源自回归(Auto-Regressive with Extra Inputs,ARX)模型的液压系统的故障诊断方法。作为一种性能优越的网络分类器,RBF网络比传统的反向传播(Back Propagation,BP)网络表现出...提出了基于径向基函数(Radial Basis Function,RBF)网络和有源自回归(Auto-Regressive with Extra Inputs,ARX)模型的液压系统的故障诊断方法。作为一种性能优越的网络分类器,RBF网络比传统的反向传播(Back Propagation,BP)网络表现出更好的分类效果,非常适合于故障特征识别。故障诊断方法首先针对目标故障状态建立ARX模型,提取ARX模型的自回归系数作为故障特征向量。然后将故障特征向量作为RBF网络训练样本,建立RBF网络故障分类器,进一步根据RBF网络的输出结果来判断故障的类型。通过建立挖掘机铲斗部分液压系统仿真模型,验证了于基于RBF网络和ARX模型的故障诊断方法的有效性。展开更多
为准确定位轮式推土机液压系统常见故障的发生部位,找出故障原因,结合轮式推土机液压系统的结构和故障特点,运用故障树分析法(Fault Tree Analysis,FTA)对轮式推土机液压系统进行故障模式分析,以"推土铲提升缓慢"这一典型故...为准确定位轮式推土机液压系统常见故障的发生部位,找出故障原因,结合轮式推土机液压系统的结构和故障特点,运用故障树分析法(Fault Tree Analysis,FTA)对轮式推土机液压系统进行故障模式分析,以"推土铲提升缓慢"这一典型故障为例,采用演绎法建立了故障树,分别使用上行法和下行法求出了故障树的最小割集,找出了导致该故障的所有可能原因,验证了方法的有效性.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51705531)Jiangsu Provincial Natural Science Foundation of China(Grant No.BK20150724)
文摘Hydraulic systems have the characteristics of strong fault concealment,powerful nonlinear time-varying signals,and a complex vibration transmission mechanism;hence,diagnosis of these systems is a challenge.To provide accurate diagnosis results automatically,numerous studies have been carried out.Among them,signal-based methods are commonly used,which employ signal processing techniques based on the state signal used for extracting features,and further input the features into the classifier for fault recognition.However,their main deficiencies include the following:(1)The features are manually designed and thus may have a lack of objectivity.(2)For signal processing,feature extraction and pattern recognition are conducted using independent models,which cannot be jointly optimized globally.(3)The machine learning algorithms adopted by these methods have a shallow architecture,which limits their capacity to deeply mine the essential features of a fault.As a breakthrough in artificial intelligence,deep learning holds the potential to overcome such deficiencies.Based on deep learning,deep neural networks(DNNs)can automatically learn the complex nonlinear relations implied in a signal,can be globally optimized,and can obtain the high-level features of multi-dimensional data.In this paper,the main technology used in an intelligent fault diagnosis and the current research status of hydraulic system fault diagnosis are summarized and analyzed.The significant prospect of applying deep learning in the field of intelligent fault diagnosis is presented,and the main ideas,methods,and principles of several typical DNNs are described and summarized.The commonality between a fault diagnosis and other issues regarding typical pattern recognition are analyzed,and research ideas for applying DNNs for hydraulic fault diagnosis are proposed.Meanwhile,the research advantages and development trend of DNNs(both domestically and overseas)as applied to an intelligent fault diagnosis are reviewed.Furthermore,the fault characteristics of a c
文摘提出了基于径向基函数(Radial Basis Function,RBF)网络和有源自回归(Auto-Regressive with Extra Inputs,ARX)模型的液压系统的故障诊断方法。作为一种性能优越的网络分类器,RBF网络比传统的反向传播(Back Propagation,BP)网络表现出更好的分类效果,非常适合于故障特征识别。故障诊断方法首先针对目标故障状态建立ARX模型,提取ARX模型的自回归系数作为故障特征向量。然后将故障特征向量作为RBF网络训练样本,建立RBF网络故障分类器,进一步根据RBF网络的输出结果来判断故障的类型。通过建立挖掘机铲斗部分液压系统仿真模型,验证了于基于RBF网络和ARX模型的故障诊断方法的有效性。
文摘为准确定位轮式推土机液压系统常见故障的发生部位,找出故障原因,结合轮式推土机液压系统的结构和故障特点,运用故障树分析法(Fault Tree Analysis,FTA)对轮式推土机液压系统进行故障模式分析,以"推土铲提升缓慢"这一典型故障为例,采用演绎法建立了故障树,分别使用上行法和下行法求出了故障树的最小割集,找出了导致该故障的所有可能原因,验证了方法的有效性.
