Novel Rolling(新颖的轧制)研究综述
Novel Rolling 新颖的轧制 - In this study, we proposed a novel rolling bearing fault diagnosis strategy based on multi-channel convolution neural network(MCNN) combining multi-scale clipping fusion(MSCF) data augmentation technique. [1] This article focuses on the development and implementation a novel rolling horizon robust online scheduling framework that utilizes stochastic optimization within a model-based feedback scheme to tackle the uncertainties in electricity prices, electric power demands, water inflows and plant model parameters. [2] In order to solve the difficulty of compound fault diagnosis of rolling bearings, a novel rolling bearings fault diagnosis method based on improved tunable Q-factor wavelet transform (TQWT) is proposed in this paper. [3] In order to diagnose rolling bearing fault accurately, a novel rolling bearing fault diagnosis method based on adaptive feature selection and clustering is proposed. [4] We develop a novel rolling horizon algorithm to solve this challenging problem in real-time, which explicitly considers the limited OC capacities and use of a back-up delivery capacity (company-owned or third party provided) to ensure the service quality. [5] RESULTS This study showed that, irrespective of the approach used to quantify the physical demands (traditional [average measures per minute] and novel rolling average time epoch [most demanding scenarios]), during the HALF condition players covered less and performed a lower number of high-intensity accelerations and decelerations than in HTRAN (Bayesian factor > 10 and standardized effect size > 0. [6] A novel rolling experiment was developed, in which the grid method was employed to capture the tiny metal flow along the width. [7] To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). [8] Our method first removes the high‐frequency signals from the curvature tensor field of an input freeform surface by a novel rolling guidance tensor filter, which results in a more regular and smooth curvature tensor field, then deforms the input surface to match the smoothed field as much as possible. [9] R-TPI employs a novel rolling enrollment scheme, which allows concurrent patient enrollment that is faster than cohort-based enrollment. [10] A novel rolling process characterized by multi-pass bi-axial reduction was proposed. [11] In the present study, a novel rolling method is proposed, which is called Multi-Rotational Flat Rolling (MRFR). [12] The results may be beneficial for understanding the fundamental behaviors of novel rolling conducting rotary joint. [13] To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. [14]在这项研究中，我们提出了一种基于多通道卷积神经网络（MCNN）结合多尺度裁剪融合（MSCF）数据增强技术的新型滚动轴承故障诊断策略。 [1] 本文着重于开发和实施一种新颖的滚动范围稳健在线调度框架，该框架利用基于模型的反馈方案中的随机优化来解决电价、电力需求、进水量和工厂模型参数的不确定性。 [2] 针对滚动轴承复合故障诊断困难，提出一种基于改进的可调Q因子小波变换（TQWT）的滚动轴承故障诊断新方法。 [3] 为了准确诊断滚动轴承故障，提出了一种基于自适应特征选择和聚类的滚动轴承故障诊断方法。 [4] 我们开发了一种新颖的滚动水平算法来实时解决这一具有挑战性的问题，该算法明确考虑了有限的 OC 容量和使用备用交付能力（公司拥有或第三方提供）来确保服务质量。 [5] 结果 这项研究表明，无论用于量化身体需求的方法（传统的 [每分钟平均测量值] 和新颖的滚动平均时间时期 [最苛刻的场景]），在 HALF 条件下，球员覆盖较少并且执行较少数量的高-强度加速和减速比 HTRAN（贝叶斯因子 > 10 和标准化效应大小 > 0。 [6] 开发了一种新颖的轧制实验，其中采用网格方法捕获沿宽度的微小金属流。 [7] 针对这一问题，本文提出了一种基于变分模态分解（VMD）、置换熵（PE）和支持向量机（SVM）的滚动轴承振动信号故障特征提取和故障模式识别方法。 [8] 我们的方法首先通过一种新颖的滚动引导张量滤波器从输入自由曲面的曲率张量场中去除高频信号，从而产生更加规则和平滑的曲率张量场，然后使输入表面变形以匹配平滑场：尽可能。 [9] R-TPI 采用了一种新颖的滚动登记方案，允许同时进行患者登记，比基于队列的登记更快。 [10] 提出了一种以多道次双轴压下为特征的新型轧制工艺。 [11] 在本研究中，提出了一种新的轧制方法，称为多旋转平面轧制（MRFR）。 [12] 该结果可能有助于理解新型滚动导电旋转接头的基本行为。 [13] 为提高滚动轴承故障识别精度，有效分析故障严重程度，提出一种基于快速样本熵、小波包能量熵和多类相关向量机的滚动轴承故障诊断及严重程度分析方法。 . [14]
novel rolling bearing 新型滚动轴承
In this study, we proposed a novel rolling bearing fault diagnosis strategy based on multi-channel convolution neural network(MCNN) combining multi-scale clipping fusion(MSCF) data augmentation technique. [1] In order to solve the difficulty of compound fault diagnosis of rolling bearings, a novel rolling bearings fault diagnosis method based on improved tunable Q-factor wavelet transform (TQWT) is proposed in this paper. [2] In order to diagnose rolling bearing fault accurately, a novel rolling bearing fault diagnosis method based on adaptive feature selection and clustering is proposed. [3] To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). [4] To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. [5]在这项研究中，我们提出了一种基于多通道卷积神经网络（MCNN）结合多尺度裁剪融合（MSCF）数据增强技术的新型滚动轴承故障诊断策略。 [1] 针对滚动轴承复合故障诊断困难，提出一种基于改进的可调Q因子小波变换（TQWT）的滚动轴承故障诊断新方法。 [2] 为了准确诊断滚动轴承故障，提出了一种基于自适应特征选择和聚类的滚动轴承故障诊断方法。 [3] 针对这一问题，本文提出了一种基于变分模态分解（VMD）、置换熵（PE）和支持向量机（SVM）的滚动轴承振动信号故障特征提取和故障模式识别方法。 [4] 为提高滚动轴承故障识别精度，有效分析故障严重程度，提出一种基于快速样本熵、小波包能量熵和多类相关向量机的滚动轴承故障诊断及严重程度分析方法。 . [5]
novel rolling horizon 新颖的滚动地平线
This article focuses on the development and implementation a novel rolling horizon robust online scheduling framework that utilizes stochastic optimization within a model-based feedback scheme to tackle the uncertainties in electricity prices, electric power demands, water inflows and plant model parameters. [1] We develop a novel rolling horizon algorithm to solve this challenging problem in real-time, which explicitly considers the limited OC capacities and use of a back-up delivery capacity (company-owned or third party provided) to ensure the service quality. [2]本文着重于开发和实施一种新颖的滚动范围稳健在线调度框架，该框架利用基于模型的反馈方案中的随机优化来解决电价、电力需求、进水量和工厂模型参数的不确定性。 [1] 我们开发了一种新颖的滚动水平算法来实时解决这一具有挑战性的问题，该算法明确考虑了有限的 OC 容量和使用备用交付能力（公司拥有或第三方提供）来确保服务质量。 [2]