矿用直线振动筛结构故障分析

针对煤矿选煤厂直线振动筛工作过程中出现的结构故障问题,采用有限元仿真与现场测试结合的方法对振动筛出现故障的原因进行了分析研究。介绍了振动筛的主要结构及常见故障,并以某矿选煤厂发生大梁断裂的振动筛为例,对该振动筛进行三维建模和有限元模态分析,分析结果表明:随着模态阶次的增加,振型的变化逐渐由移动转为结构本身的扭曲变形。在该振动筛空载工作状态下,选用本质安全型便携式测振记录仪进行振动测试并处理测试信号,得到该振动筛的工作频率。结合模态分析结果和实际测试结果,判断出空载时频繁运行振动筛是造成其大梁断裂的主要原因。根据故障原因给出维护建议:应当尽量避免振动筛在空载或物料较少的状态下长时间运行;在振动筛投入使用前,应预先了解振动筛的工作状态及可能存在的薄弱环节,尽可能避免结构故障,保证振动筛可靠运行。     

Input-to-state learning of recurrent neural networks with delay and disturbance

This article deals with the issue of input-to-state stabilization for recurrent neural networks with delay and external disturbance. The goal is to design a suitable weight-learning law to make the considered network input-to-state stable with a predefined -gain. Based on the solution of linear matrix inequalities, two schemes for the desired learning law are presented via using decay-rate-dependent and decay-rate-independent Lyapunov functionals, respectively. It is shown that, in the absence of external disturbance, the proposed learning law also guarantees the exponential stability of the network. To illustrate the applicability of the present weight-learning law, two numerical examples with simulations are given.     

An enhanced state estimation for linear parabolic PDE systems with mobile sensors

This paper studies an enhanced state estimation problem of distributed parameter processes modeled by a linear parabolic partial differential equation using mobile sensors. The proposed estimation scheme contains a state estimator and the guidance of mobile sensors, where the spatial domain is decomposed into multiple subdomains according to the number of sensors and each sensor is capable of moving within the respective subdomain. The state estimator is desired to make the state estimation error system exponentially stable while providing an performance bound. The mobile sensor guidance is used to enhance the transient performance of the error system. By the Lyapunov direct technique, an integrated design of state estimator and mobile sensor guidance laws is developed in the form of bilinear matrix inequalities (BMIs) to meet the desired design objectives. Moreover, to make the performance bound as small as possible, a suboptimal enhanced state estimation problem is formulated as a BMI optimization one, which can be solved via an iterative linear matrix inequality algorithm. Finally, numerical simulations are given to show the effectiveness of the proposed method.     

Determining Stability Constants Using the AKUFVE Technique

ABSTRACT

The AKUFVE techniques were developed by Rydberg and co-workers in the 1960s. The main aim was to be able to perform a series of liquid-liquid extraction data varying one or more parameters and at the same time achieve very pure phases. As such, this technique was later used for short-lived isotope studies in the SISAK system, but also as a standalone unit for a large number of thermodynamic studies of extraction systems both for fundamental understanding as well as more applied investigations. In this paper, the apparatus with modifications made over the decades is described. In addition, studies with stability constant determinations for the zirconium-water-acetylacetone system as well as lanthanide extraction using bromodecanoic acid are exemplified to demonstrate the potential use of the technique. The results shown clearly demonstrate the versatility and ability of the AKUFVE system.     

Novel high-energy ionic molecules deriving from new monovalent and divalent 4-oxyl-3,5-dinitropyrazolate moieties

ABSTRACT

In recent years, the exploration of a practical strategy for novel energetic molecules with high energy and low sensitivity is very desirable but highly challenging. Novel ionic energetic molecules have attracted much attention in this area due to their prominent advantages including low sensitivities, high thermal stability, and excellent energy performances. Herein, five different ionic energetic molecules based on new monovalent and divalent 4-oxyl-3,5-dinitropyrazolate moieties with enhanced oxygen balance have been synthesized, characterized and evaluated as potential high-energy materials. Thermal stability, sensitivities and energy output test were measured and studied in detail. The heats of formation and energetic parameters were calculated by using Gaussian 09 suite of programs and EXPLO 5 code. The results suggest that all as-prepared new molecules exhibit good thermal stability with high decomposition temperature (3, 231°C; 5, 160°C; 6, 185°C; 7, 180°C; 8, 213°C), and relative low sensitivity (IS > 20 J, FS = 324 N). Inheriting the significant oxygen content of monovalent and divalent 4-oxyl-3,5-dinitropyrazolate moieties, they also possess good energy properties (v _D = 8238 ~ 9208 m s^?1, P = 26.8 ~ 36.7 GPa, V _o = 481.8 ~ 959.4 L kg^?1), which make them competitive high-energy materials.     

LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

The influence of cryogenic rolling on a transformation-induced plasticity steel

ABSTRACT

Here, a novel cryogenic rolling plus intercritical annealing process was applied to a transformation-induced plasticity (TRIP) steel with a low chemical composition of carbon and manganese. Compared with traditional cold rolling, obvious grain refinement was observed, due to a high amount of dislocations retained. In addition, austenite volume fraction was increased, because of a unique nucleation mechanism. Subjected to cryogenic rolling, strength and ductility were increased, due to the enhanced austenite stability, which provided continuous and active TRIP effect. Consequently, tensile strength was increased to 1030?MPa, and elongation was increased to 38.2%. Thus, a great mechanical combination was obtained in a steel with a relatively low chemical composition with carbon and manganese, only by cryogenic rolling process.     

Hybrid-triggered fault detection filter design for networked Takagi–Sugeno fuzzy systems subject to persistent heavy noise disturbance

In the network environment, the single time-triggered scheme wastes limited bandwidth resources due to all the sampled data are transmitted to the networks, and the single event-triggered scheme may increase system error because of ignoring factors such as changes in network utilization. To reduce the design conservatism, this paper is concerned with the hybrid-triggered L₁ fault detection filter design for a class of nonlinear networked control systems (NCSs) described by Takagi–Sugeno (T-S) fuzzy model. Taking the effects of time-triggered scheme and event-triggered scheme into consideration simultaneously, we construct a fuzzy fault detection system. New results on stability and L₁ performance are proposed for fuzzy fault detection system by exploiting the Lyapunov–Krasovskii functional and by means of the integral inequality method. Specially, attention is focused on the design of fault detection filter that guarantees a prescribed L₁ noise attenuation level . Finally, two examples are presented to demonstrate the effectiveness of the proposed method.     

基于灰色系统理论的实验台温度控制系统故障诊断研究

实验台温度控制系统工作正常与否是决定煤自然发火实验成功与否的关键因素。在构建数据采集处理系统的基础上 ,建立灰色预测GM(1,1)模型预报温度传感器的未来工作状态 ,通过预测数据与实际数据比较根据门限值判定其工作状态 ,建立故障诊断软件进行诊断