相关向量机基函数和超参的协同优化

针对传统相关向量机在训练误差、权值矩阵的稀疏性以及对数边缘似然函数零逼近之间存在冲突,提出利用受试者工作特征曲线对相关向量机参数和核函数进行协同优化。依据模型分类准确率确定合适的核函数;引入模型在5%误判率下的分类准确率,对超参边际似然函数进行改进;为保证权值矩阵稀疏最大化,通过边际似然函数阈值选取最佳相关向量组合,运用交叉验证算法以及各交叉模型的ROC曲线,对相关向量机超参进行最优估计。此外,利用车辆横摆角速度对优化模型进行测试,结果表明:所提算法训练耗时略长,但测试时间明显短于传统估计算法,且模型的分类能力得到大幅提升。     

基于BA-WPHM的滚动轴承两阶段剩余寿命预测方法

为了提高滚动轴承剩余寿命预测的准确性,根据滚动轴承运行过程的两阶段性特点,提出了一种基于蝙蝠算法(BA)和威布尔比例风险模型(WPHM)的滚动轴承两阶段剩余寿命预测方法。首先,构建基于WPHM的剩余寿命预测模型;其次,提出了两阶段极大似然估计法,建立新的似然函数,并利用BA算法进行求解,以提高参数估计的准确性;最后,建立BA-WPHM模型对滚动轴承进行剩余寿命预测。案例分析表明,相比于Newton-Raphson算法、自组织分层猴群算法(SHMA)和独特的自适应粒子群算法(UAPSO),提出的方法参数估计的准确性更高,剩余寿命的预测精度优于支持向量回归(SVR)方法,验证了所提方法的有效性,为滚动轴承维修决策的可行性提供了依据。     

稀疏贝叶斯模型与相关向量机学习研究

虽然支持向量机在模式识别的相关领域得到了广泛应用,但它自身固有许多不足之处.相关向量机是在稀疏贝叶斯框架下提出的稀疏模型,模型没有规则化系数,核函数不要求满足Mercer条件.相关向量机不仅具备良好的泛化能力,而且还能够得到具有统计意义的预测结果.首先介绍了稀疏贝叶斯回归和分类模型,通过参数推断过程,将相关向量机学习转化为最大化边缘似然函数估计,并分析了3种估计方法,给出了快速序列稀疏贝叶斯学习算法流程.     

基于相关向量机的非线性动态系统辨识

基于具有核函数不用满足Mercer条件、相关向鼍自动确定及核函数少特点的稀疏贝叶斯的相关向量机核学习方法,提出了平滑先验条件约束的相关向量机的学习方法,采用稀疏贝叶斯模型的最大边缘似然算法加快了求解相关向量机的向量,并采取交叉验证法确定其核参数提高了相关向量机辨识的泛化性.该方法避免了支持向量机的非线性系统辨识的模型结构难于确定的问题,与支持向量机辨识方法相比较,辨识的模型结构更简洁.仿真表明,该方法应用于非线性动态系统的辨识,具有良好的效果.     

双时间尺度下的设备随机退化建模与剩余寿命预测方法

基于退化建模的剩余寿命预测（Remaining useful life,RUL）是当前可靠性领域研究的热点.现有的退化模型都是针对单个时间尺度下的退化设备,缺少对设备性能变化与多个时间尺度相关的退化建模与剩余寿命预测方法.鉴于此,本文基于Wiener过程提出了一种双时间尺度随机退化建模与剩余寿命预测方法,用随机比例系数描述不同时间尺度之间的不确定关系,推导出丫首达时间意义下设备的双时间尺度剩余寿命分布,讨论了其与基于单时间尺度退化模型得到的剩余寿命分布之间的关系,并给出了基于历史退化数据的未知参数极大似然估计方法.最后,将所提方法应用到惯性平台关键器件陀螺仪的退化建模与剩余寿命预测中,验证了方法的有效性.     

基于启发式算法的混沌支持向量机流量预测

针对现有混沌支持向量机回归模型存在流量预测效率低下的问题,利用差分进化(DE)算法、遗传算法和粒子群优化算法确定模型的径向基核函数系数、惩罚系数、不敏感系数等参数,在此基础上建立改进的混沌支持向量机回归模型进行流量预测。实例表明,相比其他启发式算法,DE算法能以较高的效率搜索到混沌支持向量机回归模型的最优参数,并且该模型具有较高的预测精度。     

退化数据驱动的设备剩余寿命在线预测

为在线预测单台服役设备的可用剩余寿命,提出一种融合先验退化数据和设备自身现场退化数据的剩余寿命预测方法。建立符合非线性Wiener过程描述的设备退化模型,利用先验数据采用极大似然法估计模型中的未知参数,使用贝叶斯方法融合新增的现场退化数据实时更新模型参数,进一步实现对设备实时剩余寿命评估。数值仿真和实例计算的结果表明,与固定参数法相比,该方法能够根据现场退化数据不断更新设备剩余寿命分布,进而更好地体现设备的个体差异,显著降低剩余寿命分布的不确定性。     

基于改进灰狼算法优化多核支持向量回归机及其应用

为更好发现数据中的复杂规律,避免核函数选择的盲目性和局部最优等非线性优化问题,本文提出一种基于改进灰狼算法优化多核支持向量回归机算法.首先,基于全局核函数和局部核函数构建多核支持向量机采油速度预测模型;其次,利用基于云模型和二次插值算法改进灰狼优化算法对核函数权值和参数的选取进行优化;最后,应用灰色关联分析理论确定采油速度影响因素集,并作为多核支持向量回归机预测模型的输入.与6种采油速度预测方法进行对比,所提方法具有较好的全局寻优能力和较高的预测率的优点.     

融合专家经验和寿命试验数据的产品可靠性评估

专家经验是可靠性工程中常见的一类可靠性数据,通过将其与产品的寿命试验数据融合,可以扩充可靠性信息,为产品可靠性的评估提供新的思路.对此,利用Bayes理论,考虑不同类型和不同形式的专家经验,通过验前矩拟合的方法将其转化为产品寿命分布参数的验前分布.进一步,根据寿命试验数据确定似然函数,推断分布参数的验后分布,可求得数据融合后产品的可靠度和剩余寿命等可靠性评估结果.通过蓄电池算例分析,表明所提出方法的应用及其有效性.     

改进的LS-SVM理论在刮板输送机链条寿命预测中的应用

在分析以往理论计算和加速寿命试验方法在链条预测中的不足的基础上,提出了一种基于LabVIEW和改进的LS-SVM理论的刮板机链寿命预测系统,并给出了原理及实现方法;针对传统最小二乘支持向量机存储空间和计算量大的缺点,利用阈值改进最小二乘支持向量机算法;结果表明,算法对回归精度影响不大,能有效地减少存储空间,对刮板机链寿命有良好的预测能力,适合以模块形式嵌入到刮板输送机检测系统中。     

基于改进CNN-LSTM的飞控系统剩余寿命预测

数据驱动的剩余寿命（remaining useful life,RUL）预测是复杂系统健康管理的重点研究内容,然而数据集的缺乏制约了不同系统上RUL预测的研究。针对这一问题,以飞控系统为例,提出一种仿真模型和数据混合驱动的RUL预测方法。该方法通过模型仿真提供充足的故障数据,并结合改进CNN-LSTM网络实现高质量的故障信息提取。首先对系统及其故障模式建立仿真模型,利用蒙特卡罗方法生成随机故障时间序列并依次注入故障,根据仿真响应和失效阈值确定序列的寿命标签,即可生成包含多组随机序列的系统失效数据集;其次利用长短时记忆网络（long short-term memory,LSTM）提取系统状态参数时间序列的故障信息,结合一维卷积神经网络（1D-CNN）提取不同状态参数之间的关联特征,从而形成时序-空间相结合的剩余寿命预测网络。充分的实验结果证明了所提方法对不同系统均能帮助达到动态和准确的剩余寿命预测。     

A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance

Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of concern.In this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level.The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends.Then,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,respectively.To enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters.By designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization algorithm.In addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method.Verification is done using an aero-engine data set from NASA.The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.     

A multi-head neural network with unsymmetrical constraints for remaining useful life prediction

This paper proposes a multi-head neural network (MHNN) model with unsymmetrical constraints for remaining useful life (RUL) prediction of industrial equipment. Generally, the existing deep learning methods proposed for RUL prediction utilize symmetrical constraint loss functions such as the mean squared error function to calculate training errors. However, if the predicted RUL is much larger than the actual value in some safety–critical applications, severe damage may occur. To address this issue, an unsymmetrical constraint function is proposed as the loss function in this work that penalizes the late predictions (i.e., the predicted RUL is larger than the actual RUL) more strongly. In addition, an adjustable parameter is added to this function to adjust the model’s attention to the late predictions. In MHNN model, the bidirectional gated recurrent units (BGRU) and self-attention mechanism are employed to extract temporal features from the condition monitoring data. In addition, the structure of the multi-head neural network is adopted in the proposed model, helping to capture more degradation information by means of multiple identical and parallel networks. The proposed method is validated against a commonly used turbofan engine dataset. Compared with other latest methods on the same dataset, the proposed method is proven to be superior. Taking the FD004 dataset as an example, the score obtained by MHNN is 24.09% lower than that obtained by the best existing method.     

State estimation for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays

In this paper, the state estimation problem is investigated for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays. The norm-bounded uncertainties enter into the system in a randomly way, and such randomly occurring uncertainties (ROUs) obey certain Bernoulli distributed white noise sequence with known conditional probability. By constructing a new Lyapunov–Krasovskii functional, sufficient conditions are proposed to guarantee the convergence of the estimation error for all discrete time-varying delays, ROUs and distributed sensor delays. Subsequently, the explicit form of the estimator parameter is derived by solving two linear matrix inequalities (LMIs) which can be easily tested by using standard numerical software. Finally, a simulation example is given to illustrate the feasibility and effectiveness of the proposed estimation scheme.     

无需相对速度信息的多Euler-Lagrange 系统自适应神经网络包含控制

对含有模型非线性不确定性和外部扰动的多Euler-Lagrange 系统的分布式协调包含控制问题进行研究. 考虑通讯拓扑为有向图, 所有领航者均为动态, 且各智能体间相对速度信息不可测情况. 首先, 选取相对速度作为辅助变量, 引入低通滤波器进行估计; 然后, 采用神经网络方法逼近并补偿非线性不确定性, 提出一种分布式自适应包含控制律, 并应用Lyapunov 稳定性理论证明闭环系统的包含误差一致最终有界; 最后, 通过仿真算例验证了所提出的控制律的有效性.

     

基于线性矩阵不等式的参数不确定性关联模糊大系统的分散鲁棒镇定

讨论了参数不确定性关联模糊大系统的分散鲁棒镇定问题,所考虑的参数不确定性满足范数有界条件.基于李雅普诺夫稳定性理论及大系统分散控制理论,采用分散化PDC(parallel distributed compensation)控制器,给出了保证该关联模糊大系统闭环渐近稳定的LMI形式的充分条件,通过MATLAB软件中的LMI工具箱可求解出这些LMI中的控制器参数.仿真例子说明了所提方法的有效性.     

A novel deep learning scheme for multi-condition remaining useful life prediction of rolling element bearings

The remaining useful life (RUL) prediction of a rolling element bearing is important for more reasonable maintenance of machinery and equipment. Generally, the information of a failure can hardly be acquired in advance while running and the degradation process varies in terms of different faults. Thus, fault identification is indispensable for a multi-condition RUL prediction, where, however, the fault identification and RUL prediction are separated in most studies. A new hybrid scheme is proposed in this paper for the multi-condition RUL prediction of rolling element bearings. The proposed scheme contains both classification and regression, where the 2D-DCNN based classifier and predictors are built concerning typical fault conditions of a bearing. For the online prediction, the raw signals are spanned in the time-frequency domain and then transferred into images as the input of the scheme. The classifier is used to monitor the vibration of rolling bearings for online fault recognition and excite the corresponding predictor for RUL prediction once a fault is detected. The output from the predictor is amended by the proposed adaptive delay correction method as the final prediction results. A demonstration is performed based on the XJTU-SY datasets and the results are compared with those from the state-of-the-art methods, which proves the superiority of the proposed scheme in improving the accuracy and linearity of RUL prediction. The time cost of the proposed online prediction scheme is also investigated and the results indicate high time effectiveness.     

Robust distributed model predictive control of linear systems with structured time-varying uncertainties

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min–max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.     

Distributed guaranteed cost control for aperiodic sampled-data networked interconnected systems: a switched system approach

This paper investigates the distributed guaranteed cost control problem for a class of networked interconnected control systems (NICSs) under aperiodic sampling. The NICSs with missing data and time-varying delay are modelled as an aperiodic sampled-data switched system with uncertainties. Then, sufficient conditions ensuring the exponential stability and guaranteed cost quadratic performance are presented by using the average dwell time approach. The distributed state feedback controller is designed by solving a set of LMIs. Finally, a simulation example is given to verify the effectiveness of the proposed method.