基于模糊自适应免疫算法的非线性系统模型参数估计

复杂生产工艺中非线性系统的模型参数估计是系统建模优化问题中的难点, 为避免优化算法过早收敛于错误的参数估计值, 根据生物免疫机理和模糊逻辑原理提出了一种新颖的模糊自适应免疫算法, 该算法采用混沌超变异操作增强算法搜索能力, 并用免疫网络调节策略保持抗体群的多样性, 同时采用模糊逻辑调节算法参数以提高算法的自适应能力. 函数优化仿真结果表明其具有较好的收敛性能, 并能够克服早收敛问题. 最后将其成功应用于重油热解非线性模型参数估计中, 验证了该算法解决实际建模问题的可行性和有效性.     

大时滞系统时间协调参数调整控制策略

针对大时滞系统难以实现闭环稳定控制的问题,引入脉冲响应等效系统的概念,提出时间协调参数修改控制算法.该算法由两部分组成.一是通过时间协调原则修改控制器的输出;二是利用脉冲响应等效系统来修改控制器的参数,使脉冲响应等效系统和大时滞系统匹配,跟踪被控制对象参数变化产生自适应性,保证控制系统精度和稳定性.并用于氧化铝生产过程碳酸化分解工序,将原来手动操作开环控制改为闭环控制,提高了分解率和产品的合格率.该控制算法是递推算法,计算量小,便于在线实现.     

CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis

As a representative deep learning network, Convolutional Neural Network (CNN) has been extensively used in bearing fault diagnosis and many good results have been reported. In Prognostics and Health Management (PHM) field, the CNN’s input size is usually designed as a 1D vector or 2D square matrix, and the convolution kernel size is also defined as a square shape like 3 × 3 and 5 × 5, which are directly adopted from the image recognition. Though satisfying results can be obtained, CNN with such parameter specifications is not optimal and efficient. To this end, this paper elaborated the physical characteristics of bearing acceleration signals to guide the CNN design. First, the fault period under different fault types and shaft rotation frequency were used to determine the size of CNN’s input. Next, an exponential function was involved in fitting the envelope of decaying acceleration signal during each fault period, and signal length within different decaying ratios was used to define the CNN’s kernel size. Finally, the designed CNN was validated with the Case Western Reserve University bearing dataset and Paderborn University bearing dataset. Results confirm that the physics-guided CNN (PGCNN) with rectangular input shape and rectangular convolution kernel works better than the baseline CNN with higher accuracy and smaller uncertainty. The feasibility of designing CNN parameters with physics-guided rules derived from bearing fault signal analysis has also been verified.     

Optimization of correlated multi-response quality engineering by the upside-down normal loss function

Most of the published literature on robust design is basically concerned with a single response. However, the reality is that common industrial problems usually involve several quality characteristics, which are often correlated. Traditional approaches to multidimensional quality do not offer much information on how much better or worse a process is when finding optimal settings. Köksoy and Fan [Engineering Optimization 44 (8): 935–945] pointed out that the upside-down normal loss function provides a more reasonable risk assessment to the losses of being off-target in product engineering research. However, they only consider the single-response case. This article generalizes their idea to more than one response under possible correlations and co-movement effects of responses on the process loss. The response surface methodology has been adapted, estimating the expected multivariate upside-down normal loss function of a multidimensional system to find the optimal control factor settings of a given problem. The procedure and its merits are illustrated through an example.     

Precise identification of moving vehicular parameters based on improved glowworm swarm optimization algorithm

This paper proposes an indirect method for the identification of moving vehicular parameters using the dynamic responses of the vehicle. The moving vehicle is modelled as 2-DOF system with 5 parameters and 4-DOF system with 12 parameters, respectively. Finite element method is used to establish the equation of the coupled bridge–vehicle system. The dynamic responses of the system are calculated by Newmark direct integration method. The parameter identification problem is transformed into an optimization problem by minimizing errors between the calculated dynamic responses of the moving vehicle and those of the simulated measured responses. Glowworm swarm optimization algorithm (GSO) is used to solve the objective function of the optimization problem. A local search method is introduced into the movement phase of GSO to enhance the accuracy and convergence rate of the algorithm. Several test cases are carried out to verify the efficiency of the proposed method and the results show that the vehicular parameters can be identified precisely with the present method and it is not sensitive to artificial measurement noise.     

EDA9033A在分布式风机性能测试系统中的应用

针对以单片机为核心的风机性能测试系统存在稳定性差、抗干扰能力差、精度低等问题,开发了以PLC为核心、采用互感式采集模块EDA9033A采集风机电参数的分布式风机性能测试系统,给出了该风机性能测试系统的结构,详细介绍了EDA9033A的硬件及软件设计。实际应用表明,EDA9033A采集的风机电参数结果稳定、快速、准确。     

2HL055恒流二极管参数测试

2HL055是贵州博越公司生产的恒流二极管,该类器件恒定电流大,极限电压低,可以直接驱动负载,但器件形成实验室成品后,没有进行过参数测试,其基本参数范围不能确定。文章拟通过试验,进一步测试2HL055器件的基本参数,并确定部分参数的测试方法。     

Stochastic differential equations driven by a Wiener process and fractional Brownian motion: Convergence in Besov space with respect to a parameter

A stochastic differential equation involving both a Wiener process and fractional Brownian motion, with nonhomogeneous coefficients and random initial condition, is considered. The coefficients and initial condition depend on a parameter. The assumptions on the coefficients and the initial condition supplying continuous dependence of the solution on a parameter, with respect to the Besov space norm, are established.     

Controlling the diffusionless Lorenz equations with periodic parametric perturbation

Diffusionless Lorenz equations (DLE) are a simple one-parameter version of the well-known Lorenz model, which was obtained in the limit of high Rayleigh and Prandtl numbers, physically corresponding to diffusionless convection. A simple control method is presented to control chaos by using periodic parameter perturbation in DLE. By using the generalized Melnikov method, the parameter conditions could be obtained to guide the controlled DLE to a low-periodic motion. Moreover, the existence conditions of periodic orbits and homoclinic orbits in the system are given. Some results of the numerical simulation are also explained clearly by a rigorous analysis.