An experiment of state estimation for predictive maintenance using Kalman filter on a DC motor

Preventive maintenance (PM) is an effective approach to promoting reliability. Time-based and condition-based maintenance are two major approaches for PM. No matter which approach is adopted for PM, whether a failure can be early detected or even predicted is the key point. This paper presents the experimental results of a failure prediction method for preventive maintenance by state estimation using the Kalman filter on a DC motor. The rotating speed of the motor was uninterruptedly measured and recorded every 5 min from 1 April until 20 June 2001. The measured data are used to execute Kalman prediction and to verify the prediction accuracy. The resultant prediction errors are acceptable. Futhermore, the shorter the increment time for every step used in Kalman prediction, the higher prediction accuracy it achieves. Failure can be prevented in time so as to promote reliability by state estimation for predictive maintenance using the Kalman filter.     

A practical polynomial chaos Kalman filter implementation using nonlinear error projection on a reduced polynomial chaos expansion

The polynomial chaos Kalman filter (PCKF) has been gaining popularity as a computationally efficient and robust alternative to sampling methods in sequential data assimilation settings. The PCKF's sampling free scheme and attractive structure to represent non‐Gaussian uncertainties makes it a promising approach for data filtering techniques in nonlinear and non‐Gaussian frameworks. However, the accuracy of PCKF is dependent on the dimension and order of the polynomial chaos expansion used to represent all sources of uncertainty in the system. Thus, when independent sources of errors, like process noise and time independent sensors' errors are incorporated in the system, the curse of dimensionality hinders the efficiency and the applicability of PCKF. This study sheds light on this issue and presents a practical framework to maintain an acceptable accuracy of PCKF without scarifying the computational efficiency of the filter. The robustness and efficiency of the presented implementation is demonstrated on 3 typical numerical examples to illustrate its ability to achieve considerable accuracy at a low computational tax.     

Battery cell modeling and online estimation of the state of charge of a lithium-ion battery

Battery models are vital to the development of electric vehicles. Different models have been proposed over the years to describe the battery dynamics in various degree of detail. More detail comes at the cost of more computation. This paper proposes using a combination of the Kinetic Battery Model (KiBaM) and the dual capacity network model to capture both the nonlinear state of charge variation and linear transient response. After capture, we then derived a recursive formula for the online implementation of the proposed model. In the first part of this study, MATLAB® was used to build a battery model of a battery cell. The battery model simulates how the magnitude of the discharge current and state of charge influence the parameters of the model, with the results showing that it can predict the voltage response within a voltage error of 4% under dynamic loading. In the second part of the study, a model-based Kalman filter was adopted for estimating the state of charge. This algorithm was compatible with the recursive formula and could be used in conjunction with the online batter model.     

Towards simplified and optimized a posteriori error estimation using PGD reduced models

The paper deals with the use of model order reduction within a posteriori error estimation procedures in the context of the finite element method. More specifically, it focuses on the constitutive relation error concept, which has been widely used over the last 40 years for FEM verification of computational mechanics models. A technical key‐point when using constitutive relation error is the construction of admissible fields, and we propose here to use the proper generalized decomposition to facilitate this task. In addition to making the implementation into commercial FE software easier, it is shown that the use of proper generalized decomposition enables to optimize the verification procedure and to get both accurate and reasonably expensive upper bounds on the discretization error. Numerical illustrations are presented to assess the performance of the proposed approach.     

安时-卡尔曼交叉运行的电池荷电状态估算策略及其微控制器在环验证

为了提高电池管理系统(BMS)的性能,研究了电池荷电状态(SOC)的估算方法,并根据SOC估算算法精度和系统实时性要求,提出了安时(AH)积分算法-卡尔曼(Kalman)算法(AH-Kalman)交叉运行的SOC估算策略。该策略用开路电压(OCV)法确定SOC初值,以实时性较强的AH积分法为主,采用间歇运行的Kalman滤波法修正安时计量法积分误差。建立了系统仿真模型,验证了卡尔曼滤波算法对安时积分法积累误差的修正作用。将控制算法生成C代码下载到目标控制器,搭建微控制器在环测试验证(PILS)平台,进行了与传统卡尔曼滤波算法的复杂度对比分析。结果表明,所提出AHKalman交叉运行的SOC估算策略在保证了SOC估算精度的同时也具有较好的实时性,便于实际应用。     

基于Kriging模型和无迹卡尔曼滤波的转向架构架模型修正

为提高转向架构架模型的修正效率和实时性,提出了一种基于Kriging模型和无迹卡尔曼滤波的模型修正方法.首先,对构架进行模态分析,引入信息熵确定模态阶数来优选频响函数频率区间.其次,构造Kriging模型,将频响函数经过小波变换并提取第4层低频系数作为Kriging模型输出,并通过改进的灰狼算法(grey wolf o...     

基于改进Thevenin模型的混合动力镍氢电池SOC估算研究

基于混合动力汽车用NiMH电池实验数据,对Thevenin电池等效电路模型进行了改进,分充放电两个不同的方向来辨识模型参数,在Matlab/Simulink中建立电池模型,并基于此模型,研究了卡尔曼滤波法在估算电池荷电状态(SOC)中的应用.仿真结果与电池实验数据的对比表明,改进后的模型能更真实地模拟电池特性,所研究的方法能有效解决SOC 初值估算不准和累积误差的问题.     

Testing the hypothesis of a general linear model using nonparametric regression estimation

Summary Given the modelY _i =m(χ _i )+ɛi,whereE(ɛ _i) =0,X _i ≠Ci=1, ...,n, andC is ap-dimensional compact set, we have designed a new method for testing the hypothesis that the regression function follows a general linear model,m(·) ∈ {m _θ(·) =A ^t (·)θ}_{θ∈Θ⊂ℛq} , withA a function fromℜ ^p toℜ ^q. The statistic, denoted ΔASE, used fortesting the given hypothesis is defined to be the difference between the average squared errors (ASE) associated with the non-parametric estimator ofm and the minimum distance parametric estimator ofm. The asymptotic normality of both ΔASE and the minimum distance estimators is proved under general conditions. Alternative bootstrap versions of ΔASE are also considered.     

Active control of forced vibrations in adaptive structures using a higher order model

This paper deals with a finite element formulation for active control of forced vibrations, including resonance, of thin plate/shell laminated structures with integrated piezoelectric layers, acting as sensors and actuators, based on third-order shear deformation theory. The finite element model is a single layer triangular nonconforming plate/shell element with 24 degrees of freedom for the generalized displacements, and one electrical potential degree of freedom for each piezoelectric element layer, which are surface bonded or embedded in the laminate.

The Newmark method is considered to calculate the dynamic response of the laminated structures, forced to vibrate in the first natural frequency. To achieve a mechanism of active control of the structure dynamic response, a feedback control algorithm is used, coupling the sensor and active piezoelectric layers. The model is applied in the solution of illustrative cases, and the results are presented and discussed.     

Maximum likelihood estimation of change point from stationary to nonstationary in autoregressive models using dynamic linear model

Change point estimation is a useful concept in time series models that could be applied in several fields such as financing, quality control. It helps to decrease costs of decision making and production by monitoring stock market and production lines, respectively. In this paper, the maximum likelihood technique is developed to estimate change point at which the stationary AR(1) model changes to a nonstationary process. Filtering and smoothing of dynamic linear model are used to estimate unknown parameters after change point. We also assume that correlation exists between samples' statistics. Simulation results show the effectiveness of the proposed estimators to estimate the change point of stationary. In addition based on Shewhart control chart, filtering has a better accuracy in comparison to smoothing. A real example is provided to illustrate the application.     

Certified real-time shape optimization using isogeometric analysis,PGD model reduction,and a posteriori error estimation

In this paper, we address the effective and accurate solution of problems with parameterized geometry. Considering the attractive framework of isogeometric analysis, which enables a natural and flexible link between computer-aided design and simulation tools, the parameterization of the geometry is defined on the mapping from the isogeometric analysis parametric space to the physical space. From the subsequent multidimensional problem, model reduction based on the proper generalized decomposition technique with off-line/online steps is introduced in order to describe the resulting manifold of parametric solutions with reduced CPU cost. Eventually, a posteriori estimation of various error sources inheriting from discretization and model reduction is performed in order to control the quality of the approximate solution, for any geometry, and feed a robust adaptive algorithm that optimizes the computational effort for prescribed accuracy. The overall approach thus constitutes an effective and reliable numerical tool for shape optimization analyses. Its performance is illustrated on several two- and three-dimensional numerical experiments.     

利用序贯非线性最小二乘技术识别隔震支座模型的参数

为了研究隔震橡胶支座的力学特性,采用简化的Wen模型分析隔震支座的非线性动力行为,并用实验方法测试得到了该模型的力学相关参数。鉴于该模型具有高度的非线性,传统的参数识别方法不再适用,根据非线性系统状态参数估计理论,利用序贯非线性最小二乘方法估计该迟滞模型的非线性参数。该方法具有输入量少、计算精度高、易于实现的优点。通过进行两种地震波激励下的振动台实验,分析结果表明,这种简化的Wen模型能够模拟隔震支座的动态力学行为,而且应用该方法得到的隔震支座的模型参数与实际值比较一致,从而证明了该方法在橡胶隔震支座检测和健康状态评估中的有效性。     

强动载荷下焊接钢板力学性能及本构模型研究

为研究强动载荷下船用焊接钢板的力学性能.开展了典型船用焊接钢板母材、焊缝和热影响区的准静态拉伸试验、高温拉伸试验及SHPB动态压缩试验,分析了焊接钢板材料在不同应力状态下的力学行为,基于力学性能试验结果拟合了焊接钢板母材、焊缝和热影响区材料的本构模型.结果 表明:准静态条件下,与母材相比,焊缝和热影响区材料的屈服强度与...     

Parameter estimation in a thermoelastic composite problem via adjoint formulation and model reduction

Advances in nondestructive material characterization are providing a wealth of information that could be exploited to gain insight into general aspects of material performance and, in particular, discover relationships between microstructure and thermo‐mechanical properties in polycrystalline and other complex composite materials. In order to facilitate the integration of such measurements into existing models, as well as inform new physics‐based predictions, we developed a C++/MPI computational framework for sensitivity analysis and parameter estimation. The framework utilizes a micro‐mechanical modeling based on fast Fourier transforms, direct and adjoint formulations, and Markov chain Monte Carlo sampling techniques. We illustrate the characteristics of this framework and demonstrate its utility by computing the residual stresses arising from thermal expansion of an elastic composite and using data from simulated experiments. We show that the availability of nondestructive 3‐D measurements is crucial to reduce the uncertainty in predictions, emphasizing the importance of an integrated experimental/modeling/data analysis approach for improved material characterization and design. Copyright © 2017 John Wiley & Sons, Ltd.     

Ventricular segmentation and modeling using topological watershed transformation and harmonic state model

This paper proposes an adapted ventricular segmentation method based on topological watershed transform. Segmentation will allow spatio-temporal modeling of trajectories of the different points belonging to the borders of the ventricle using a harmonic motion model that is able to describe such motion over the entire cardiac cycle. In addition, extraction of the adopted canonical state vector and the corresponding state equations guarantees an optimal efficacy and a gradual transition from order n to order n + 1. To validate the proposed approach, an intern-image base was used. Our results show a promising ability to discern whether subjects are healthy or pathological with an 80% success rate.     

部分加速度测量下结构恢复力与质量非参数化识别方法

传统的基于扩展卡尔曼滤波方法的结构非线性行为识别方法往往要求结构质量以及结构非线性恢复力的参数化模型已知。该研究为解决非线性结构质量,结构参数,非线性恢复力的识别问题,提出了一种两阶段识别方法;为提高计算效率采用遗忘因子扩展卡尔曼滤波算法结合等效线性模型实现结构非线性位置的定位,随后采用无迹卡尔曼滤波算法与恢复力的二重切比雪夫多项式非参数化模型识别结构参数,质量与恢复力。在对一个含形状记忆合金(SMA)阻尼器的多自由度体系的数值模型进行了数值模拟验证的基础上,设计了一个含SMA阻尼器的四自由度框架开展动力试验,验证了所提出方法对结构质量以及恢复力的识别效果。     

Fixed‐lag smoothing and state estimation in dynamic electrical impedance tomography

In electrical impedance tomography (EIT), an approximation for the internal resistivity distribution is computed based on the knowledge of the injected currents and measured voltages on the surface of the body. The conventional approach is to inject several different current patterns and use the associated data for the reconstruction of a single distribution. This is an ill‐posed inverse problem. In some applications the resistivity changes may be so fast that the target changes between the injection of the current patterns and thus the data do not correspond to the same target distribution. In these cases traditional reconstruction methods yield severely blurred resistivity estimates. We have earlier proposed to formulate the EIT problem as an augmented system theoretical state estimation problem. The reconstruction problem can then be solved with Kalman filter and Kalman smoother algorithms. In this paper, we use the so‐called fixed‐lag smoother to solve the dynamic EIT reconstruction problem. We show that data storage difficulties that are associated with the previously used fixed‐interval smoother can be avoided using the fixed‐lag smoother. The proposed methods are compared with simulated measurements and real data. Copyright © 2001 John Wiley & Sons, Ltd.     

随机激励作用下硬涂层薄板振动有限元分析及减振预估

硬涂层减振是一项新兴的阻尼减振技术,硬涂层复合结构在随机激励载荷作用下的振动特性建模与分析方法还未开展研究.将有限元建模方法和虚拟激励法(pseudo excitation method,PEM)相结合,实现了随机激励作用下硬涂层悬臂薄板结构振动建模及减振性能预估.首先,基于等效单层法创建了双面涂敷硬涂层的薄板结构振动...     

Protecting free-entangled and bound-entangled states in a two-qutrit system under decoherence using weak measurements

We demonstrate a method to protect free-entangled and bound-entangled states in a two-qutrit system under amplitude damping and the generalized amplitude damping channels based on measurement reversal from weak measurement. We find that this scheme can circumvent the distillability sudden death by changing weak measurement strength and the free-entangled and bound-entangled states can be robust against decoherence in amplitude damping case. Moreover, the negativity and realignment negativity almost tend to a constant respectively in spite of sufficiently strong decoherence when weak measurement strength is strong. We also show that under the generalized amplitude damping, the life of free-entangled and bound-entangled states can be prolonged using weak measurements in certain conditions.     

板带轧机随机干扰模型与ARMA谱分析

板带轧机随机干扰模型不易建模,是制约轧机随机动力特性研究的主要难点之一。基于某铝热连轧机轧制１２３５铝合金的轧制力随机数据,得到平稳、正态、零均值的时间序列,采用时序分析法建立轧制力的ＡＲＭＡ模型及轧机动态干扰功率谱密度模型,分析了轧制力的ＡＲＭＡ谱特性,采用麦夸特法＋通用全局优化算法,得出便于工程应用的轧制力功率谱表达式,构建轧制力随机干扰模型。对轧制力的预报、轧机动刚度的确定及对轧机与轧制过程的随机动态性研究与分析具有一定的意义。