将安全域的思想引入滚动轴承的状态监测中,综合利用局部均值分解(Local Mean Decomposition,LMD)、主成分分析(Principal Component Analysis,PCA)和最小二乘支持向量机(Least Square Support Vector Machine,LSSVM),进行了滚动轴承运...将安全域的思想引入滚动轴承的状态监测中,综合利用局部均值分解(Local Mean Decomposition,LMD)、主成分分析(Principal Component Analysis,PCA)和最小二乘支持向量机(Least Square Support Vector Machine,LSSVM),进行了滚动轴承运行状态的安全域估计以及正常和各种故障状态的辨识。首先,按一定的时间间隔将采集正常及各种故障状态的振动数据进行分段,每段数据进行LMD后获得各乘积函数分量;其次,基于各段数据的乘积函数分量,利用PCA提取出每段数据的T2和SPE统计量控制限值作为滚动轴承的状态特征量;最后,利用二分类的LSSVM进行滚动轴承运行状态的安全域估计,利用多分类LSSVM进行滚动轴承的正常以及滚动体、内圈、外圈故障四种状态的辨识。试验结果显示安全域估计和多种状态辨识的准确率均较高,验证了本文方法的有效性。展开更多
The calculation of force for a roll system has significant effects on cold roiled thin strip gauge adjustment of a 20-high Sendzimir mill. According to the rolling parameters and rolling plans of a ZR 22B-42 Sendzimir...The calculation of force for a roll system has significant effects on cold roiled thin strip gauge adjustment of a 20-high Sendzimir mill. According to the rolling parameters and rolling plans of a ZR 22B-42 Sendzimir mill in a silicon steel factory, the contact force and the resultant force of each roll in the roll system were calculated both in the static state and in the rolling state through C＋＋ programs. It was found that the contact force between the see ond intermediate driven roll and the back up rolls B and C was much lower than that between the other rolls in static state. The results also demonstrated that the resultant force are 59.5%-62%, 37.7%-40.3%, 87.1%-88.7% and 53.9%-56.7% of the rolling force at the second intermediate driven roll, the second intermediate idler roll, the back-up rolls B and C and the back-up rolls A and D, respectively. It was also revealed that the minimum contact force generated between the first intermediate roll and the second intermediate idler roll is 206.7 kN on the first roll ing pass, and that on the second rolling pass, the minimum contact force generated between the second intermediate driven roll and the back-up roll C is 470.7 kN.展开更多
准确预测滚动轴承剩余使用寿命(remaining useful life,RUL),对于保证工程设备安全稳定可靠运行具有极其重要的作用.现有深度学习预测方法往往直接建立振动监测数据与剩余寿命之间的映射关系,通常忽略滚动轴承性能退化的不同状态差异性...准确预测滚动轴承剩余使用寿命(remaining useful life,RUL),对于保证工程设备安全稳定可靠运行具有极其重要的作用.现有深度学习预测方法往往直接建立振动监测数据与剩余寿命之间的映射关系,通常忽略滚动轴承性能退化的不同状态差异性,且并未考虑深度学习模型所提取各类特征的差异性,给剩余寿命预测结果带来了极大的偏差.鉴于此,提出一种新型滚动轴承退化状态划分方法和RUL预测方法.提取轴承振动信号的特征,利用Mann-Kendall检验法进行趋势判断,确定出退化期的起始点;通过归一化奇异值相关系数走势确定出慢速退化期的终点;构建基于融合注意力机制的双向长短时记忆网络(bidirectional long short-term memory with attention,Bi-LSTM-Att)的滚动轴承RUL预测模型,利用所截取的慢速退化期数据与对应RUL标签训练预测模型实现RUL预测.通过轴承公开数据集验证所提方法对轴承RUL预测的准确性和有效性.展开更多
Dynamical Joining of the solid-state metal is the key technology to realize endless hot rolling. The heating and laser welding method both require long joining time. Based on super deformation method, a 7-bar and 2-sl...Dynamical Joining of the solid-state metal is the key technology to realize endless hot rolling. The heating and laser welding method both require long joining time. Based on super deformation method, a 7-bar and 2-slider mechanism was developed in Japan, and the joining time is less than 0.5 s, however the length of each bar are not reported and this mechanism is complex. A relatively simple 6-bar and 1-slider mechanism is put forward, which can realize the shearing and extrusion motion of the top and bottom blades with a speed approximately equal to the speed of the metal plates. In order to study the kinematics property of the double blades, based on complex vector method, the multi-rigid-body model is built, and the displacement and speed functions of the double blades, the joining time and joining thickness are deduced, the kinematics analysis shows that the initial parameters can＇t satisfy the joining process. Hence, optimization of this mechanism is employed using genetic algorithm（GA） and the optimization parameters of this mechanism are obtained, the kinematics analysis show that the joining time is less than 0.1 s, the joining thickness is more than 80% of the thickness of the solid-state metal, and the horizontal speeds of the blades are improved. A new mechanism is provided for the joining of the solid-state metal and a foundation is laid for the design of the device.展开更多
文摘将安全域的思想引入滚动轴承的状态监测中,综合利用局部均值分解(Local Mean Decomposition,LMD)、主成分分析(Principal Component Analysis,PCA)和最小二乘支持向量机(Least Square Support Vector Machine,LSSVM),进行了滚动轴承运行状态的安全域估计以及正常和各种故障状态的辨识。首先,按一定的时间间隔将采集正常及各种故障状态的振动数据进行分段,每段数据进行LMD后获得各乘积函数分量;其次,基于各段数据的乘积函数分量,利用PCA提取出每段数据的T2和SPE统计量控制限值作为滚动轴承的状态特征量;最后,利用二分类的LSSVM进行滚动轴承运行状态的安全域估计,利用多分类LSSVM进行滚动轴承的正常以及滚动体、内圈、外圈故障四种状态的辨识。试验结果显示安全域估计和多种状态辨识的准确率均较高,验证了本文方法的有效性。
基金Sponsored by Open Fund of Key Laboratory for Metallurgical Equipment and Control of Education Ministry of China(2013B03)
文摘The calculation of force for a roll system has significant effects on cold roiled thin strip gauge adjustment of a 20-high Sendzimir mill. According to the rolling parameters and rolling plans of a ZR 22B-42 Sendzimir mill in a silicon steel factory, the contact force and the resultant force of each roll in the roll system were calculated both in the static state and in the rolling state through C＋＋ programs. It was found that the contact force between the see ond intermediate driven roll and the back up rolls B and C was much lower than that between the other rolls in static state. The results also demonstrated that the resultant force are 59.5%-62%, 37.7%-40.3%, 87.1%-88.7% and 53.9%-56.7% of the rolling force at the second intermediate driven roll, the second intermediate idler roll, the back-up rolls B and C and the back-up rolls A and D, respectively. It was also revealed that the minimum contact force generated between the first intermediate roll and the second intermediate idler roll is 206.7 kN on the first roll ing pass, and that on the second rolling pass, the minimum contact force generated between the second intermediate driven roll and the back-up roll C is 470.7 kN.
文摘准确预测滚动轴承剩余使用寿命(remaining useful life,RUL),对于保证工程设备安全稳定可靠运行具有极其重要的作用.现有深度学习预测方法往往直接建立振动监测数据与剩余寿命之间的映射关系,通常忽略滚动轴承性能退化的不同状态差异性,且并未考虑深度学习模型所提取各类特征的差异性,给剩余寿命预测结果带来了极大的偏差.鉴于此,提出一种新型滚动轴承退化状态划分方法和RUL预测方法.提取轴承振动信号的特征,利用Mann-Kendall检验法进行趋势判断,确定出退化期的起始点;通过归一化奇异值相关系数走势确定出慢速退化期的终点;构建基于融合注意力机制的双向长短时记忆网络(bidirectional long short-term memory with attention,Bi-LSTM-Att)的滚动轴承RUL预测模型,利用所截取的慢速退化期数据与对应RUL标签训练预测模型实现RUL预测.通过轴承公开数据集验证所提方法对轴承RUL预测的准确性和有效性.
基金Supported by National Natural Science Foundation of China(Grant No.51475139)
文摘Dynamical Joining of the solid-state metal is the key technology to realize endless hot rolling. The heating and laser welding method both require long joining time. Based on super deformation method, a 7-bar and 2-slider mechanism was developed in Japan, and the joining time is less than 0.5 s, however the length of each bar are not reported and this mechanism is complex. A relatively simple 6-bar and 1-slider mechanism is put forward, which can realize the shearing and extrusion motion of the top and bottom blades with a speed approximately equal to the speed of the metal plates. In order to study the kinematics property of the double blades, based on complex vector method, the multi-rigid-body model is built, and the displacement and speed functions of the double blades, the joining time and joining thickness are deduced, the kinematics analysis shows that the initial parameters can＇t satisfy the joining process. Hence, optimization of this mechanism is employed using genetic algorithm（GA） and the optimization parameters of this mechanism are obtained, the kinematics analysis show that the joining time is less than 0.1 s, the joining thickness is more than 80% of the thickness of the solid-state metal, and the horizontal speeds of the blades are improved. A new mechanism is provided for the joining of the solid-state metal and a foundation is laid for the design of the device.
