复杂机械系统可靠度分配方法研究

建立了多状态多失效模式混合决策理论,实现了多状态多失效模式机械系统可靠度分配。首先,建立混合图,刻画了单元同一等级不同模式失效的混合关系;建立混合算子,确定单元多状态多失效模式可靠度分配决策。然后,建立串联系统可靠度决策模型,把系统可靠度分配给系统各状态。该模型比传统只考虑故障、完好两状态的系统可靠度分配方法更符合工程实际情况,考虑部件的完好、故障隐患、功能退化和完全失效等多种状态,能有效避免忽略故障隐患、功能退化两种状态的危害性。最后,通过受电弓系统多状态多失效模式可靠度分配的算例,验证该理论方法的可行性、有效性。     

一种系统危害性综合分析方法

针对传统可靠性分析方法忽略故障相关性以及考虑因素单一的缺点,提出了一种优化的故障危害性分析方法。该方法能够对系统不同故障模式的危害性进行全面评估,使得到的故障模式危害度值(综合危害度)更加合理。一方面,通过将危害性矩阵分析方法和风险优先数方法结合,在多因素评价的基础上增加客观性指标;另一方面,把决策实验室(DEMATEL)方法融入质量功能配置(QFD)方法中,对故障模式相关性进行定量分析。最后,以弧焊机器人系统为例,证明了所提方法的有效性。     

不对称润滑对碰摩转子-轴承系统的动力学影响

在考虑非线性油膜力的基础上,建立了具有碰摩故障的转子-轴承系统的动力学模型。用数值方法研究了在非线性油膜力作用下具有碰摩故障的转子系统的动力学特性,并研究了当改变其中一个支承轴承润滑油的粘度时,转子系统的动力学特性。研究发现,随着一轴承润滑油粘度的降低,转子系统亚临界角速度区的混沌区域和拟周期区域扩大了。该结果为采用不同粘度润滑剂的转子-轴承系统的优化设计、安全运行和故障诊断等提供了一定的理论参考     

双盘悬臂转子轴承系统松动碰摩耦合故障分析

针对考虑松动-碰摩耦合故障的双盘悬臂立式转子-轴承系统，建立了该系统的力学模型和有限元模型，并基于非线性有限元方法和接触理论研究了松动刚度和碰摩间隙两个重要参数对系统动力学特性的影响。通过对在不同松动刚度和不同碰摩间隙时系统动力学特性的研究分析，发现转静件碰摩能够减小松动引起的低频振动，支座松动产生的碰摩具有明显的方向性，从而得出转子系统中诊断松动-碰摩耦合故障的依据。     

风能转换系统的故障诊断与主动容错控制

建立了风能转换系统传动部分的故障数学模型,并且在未知干扰情况下构造了自适应故障观测器,用以检测风能转换系统传动部分的故障,并对该故障进行自适应估计。设计了主动容错控制器来保证发生故障时风能转换系统传动部分的稳定性以及可靠运行,并给出各部分详细的稳定性证明。通过对主传动链不同故障类型的仿真,表明了自适应故障诊断及主动容错控制方法的有效性和可行性。     

汽车变速器新产品质量检测系统中关键技术研究

针对汽车变速器下线产品质量检测过程中人工检测存在的客观性差异,以及对故障定位不准确的问题,设计并开发了一种基于阶次分析理论的变速器在线质量检测系统。该系统运用阶次分析理论对振动信号进行有效的特征提取,并运用遗传搜索策略对高维特征向量进行特征选择,以提高故障判别的效率和准确率。为解决系统的通用性问题,还提出了一种通用的变速器结构建模方法。在某变速器生产企业的实际应用,验证了提出的检测方法的有效性和准确性,证明了该方法可实时按工况对变速器产品进行质量检测。对于有质量缺陷的产品,该方法可指出其故障类型和故障位置。     

面向质量控制的制造系统可诊断性分析

针对目前以产品质量控制为目标的制造系统可诊断性和局部可诊断系统的诊断能力研究较少的现状,在构建制造系统状态空间模型的基础上,提出了一套新的制造系统可诊断性分析方法,推导出系统具有可诊断性的充分必要条件,对于局部可诊断系统,建立了可诊断能力评价指数。通过具体的装配实例,针对不同的系统配置, 验证了该分析方法和评价指数的有效性和可行性。该方法为制造系统的可诊断性度量、系统优化配置和在线故障诊断等提供了理论分析依据。     

交流暂态故障对柔性直流系统影响的评判研究

在模块化多电平电压源换流器高压直流输电(MMC-HVDC)系统中,发生在交流输电线路的各类暂态故障或扰动不但会引起与交流系统连接的MMC换流站内上下桥臂不同程度过流、过压现象,也会对对向站的正常运行产生一定程度的负面影响。如何利用MMC换流站内各电气信息实现交流暂态故障或扰动对其运行状态影响程度的评判将是今后MMC-HVDC研究领域的热点问题。针对上述问题,首先分析了交流输电线路暂态故障对站内电压、电流的影响,指出站内各电气信息之间的联系。而后鉴于MMC-HVDC系统阻尼对暂态故障能量传递的抑制作用,考虑故障扰动时站内电压、电流具有变化无规律、频率复杂且扰动特征能量等级低等特点,提出利用Renyi小波包能量熵对交流暂态故障发生时站内电气信息进行特征提取,并根据特征提取结果构建了系统运行状态评判指标集。基于Renyi熵权和灰色关联分析理论,提出了一种双端MMC-HVDC系统运行状态综合评判方法。最后,利用双端31电平MMC-HVDC系统进行了各类交流暂态故障的模拟实验,并基于该方法实现了不同交流暂态故障对MMC-HVDC系统运行状态影响程度的评判,判断结果证明了该方法的有效性。     

一类Neutral时滞系统的鲁棒故障检测方法

针对一类具有不确定输入的 Neutral时滞系统 ,应用 H∞ 控制理论和 L yapunov原理得到了系统的带记忆状态观测器的鲁棒故障检测方法。证明并给出了观测器对不确定性具有 L2 范数界的渐近稳定条件和增益阵设计的线性矩阵不等式(L MI)方法 ,同时使残差体现了对故障信号响应的灵敏度。仿真算例表明了该方法进行鲁棒故障检测的有效性。     

基于多尺度模糊熵的旋转机械故障诊断研究

从旋转机械振动信号复杂度分析的角度,展开相关特征熵提取方法的研究,构造出一组能够有效区分不同故障状态振动信号的多尺度模糊熵。考虑到传统故障模式识别方法对于先验知识过度依赖的理论缺陷,将生物识别领域基于变量预测模型的模式识别方法引入到旋转机械故障诊断之中,并在实例中进行了应用,表明该方法的有效性。     

Motion sensor fault detection and failsafe logic for vehicle stability control systems (VSCs)

The design of a reliable and failsafe control system requires that sensor failures be detected and identified within acceptable time limit so that system malfunction can be prevented. This paper presents a model-based approach to sensor fault detection with applications to vehicle stability control systems. The effectiveness of the proposed method is illustrated through test data-based evaluation. Vehicle test data-based evaluation results show that the proposed fault management scheme can be used for the design of a failsafe VSCs.     

An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection

Bearings are among the most frequently used components. Bearing failure could lead to complete stall of a mechanical system, unpredicted productivity loss for production facilities or catastrophic consequence for mission-critical equipment. As such, bearing fault detection and diagnosis is an imperative part of most of preventive maintenance procedures. This paper presents a parameter independent yet simple to implement fault detection technique. The Teager energy operator is tailored to extract both the amplitude and frequency modulations of the vibration signals measured from mechanical systems. The incorporation of the frequency modulation information into the proposed bearing fault detection method has eliminated the need for interference removal steps. As the amplitude demodulation (AD) is also inherent in the energy operator, the fault frequency can be detected from the spectrum of the energy-transformed signal. The effectiveness of the proposed method has been validated using both simulated and experimental data.     

Surface defect detection in tiling Industries using digital image processing methods: Analysis and evaluation

Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated.     

弹道导弹中段突防弹道设计与验证

针对中段多脉冲弹道规划及评价问题,提出了一种新的弹道设计及评价方法。借鉴轨迹规划中节点搜索及扩展的思想,将多脉冲弹道的脉冲点火点视为节点,建立了多脉冲机动变轨模型。基于该模型,对突防弹道进行了设计及优化。运用"博弈论"的思想,在考虑敌方防御系统的探测机理、性能及部署基础上将敌方预测弹道与实际弹道的差值作为评价函数,以尽量降低敌方预测弹道的精度。最后,设计了导弹突防效能地面验证平台以验证所设计弹道的突防效率与弹道评价方法的有效性。选取了一个例子进行分析及验证,结果表明:相比最省能量弹道,设计的导弹防御系统对突防弹道的预测误差提高了5到15倍,达到了100~300km,飞行时间缩短了8.53%,而且规划时间不到140s。实验显示本文提出的弹道设计方法,能够在很短时间内规划出一条突防概率很高的弹道。     

An Intelligent Feature-Based Design for Stamping System

In this paper, an intelligent feature-based DFS (design for stamping) system has been proposed for implementing the stampability evaluation. The work includes identification of the aims, criteria, and procedure of stampability evaluation, as well as the formalisation of the stampability evaluation knowledge. The stampability evaluation has been oriented to part shape analysis and cost estimation, based on features, and a knowledge-based system has been developed to enable designers to carry out the stampability evaluation automatically. The effectiveness of the intelligent feature-based DFS system for improving the stampability of part designs lies in the integration of design evaluation and cost estimation into a single knowledge-based system. Such integration should enable consistency in cost estimation and design evaluation. The application of the system is illustrated with an example.     

Cloud‐based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images

The advancement of computer‐ and internet‐based technologies has transformed the nature of services in healthcare by using mobile devices in conjunction with cloud computing. The classical phenomenon of patient–doctor diagnostics is extended to a more robust advanced concept of E‐health, where remote online/offline treatment and diagnostics can be performed. In this article, we propose a framework which incorporates a cloud‐based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images. In the proposed approach, shape‐based features are used for the detection of tumor cells. Furthermore, these features are used for the classification of cells into malignant and benign categories using Naive Bayesian and Artificial Neural Network. Moreover, an important phase addressed in the proposed framework is the grading of the affected cells, which could help in grade level necessary medical procedures for patients during the diagnostic process. For demonstrating the e effectiveness of the proposed approach, experiments are performed on real data sets comprising of patients data, which has been collected from the pathology department of Lady Reading Hospital of Pakistan. Moreover, a cross‐validation technique has been performed for the evaluation of the classification accuracy, which shows performance accuracy of 98% as compared to physical methods used by a pathologist for the detection and classification of the malignant cell. Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.     

MINIMISATION OF DECISION ERRORS IN A PROBABILISTIC NEURAL NETWORK FOR CHANGE POINT DETECTION IN MECHANICAL SYSTEMS

Probabilistic neural nets have been applied in the detection of structural damage. These networks, which rely upon approximating the multivariate density of the training data, have been shown to be effective in some applications. However, quantification of decision errors, which must ultimately be used to rank their effectiveness, has received little attention. In this paper, a two-dimensional probabilistic pattern classifier (PPC) based on an L²detector is studied. Each dimension is modelled as a Gaussian random variable in order to directly study the effects introduced by a commonly used density estimator. The region of acceptance is examined for different parameters of the kernel density estimator. The detector behaviour of the original PPC is compared to theory. It is demonstrated that performance is affected by errors introduced by a kernel constructed using a finite set of data, and also from theoretical limitations inherent in the test statistic. A modification of the test statistic is suggested to improve the sensitivity for structural damage detection.     

Data driven uncertainty evaluation for complex engineered system design

Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail. The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carried out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.     

Design of a real-time overload monitoring system for bridges and roads based on structural response

The problem of the general overload of vehicles that causing premature failure of bridges and roads is becoming more and more obvious. Structural behaviors of bridges and roads need real-time monitoring and diagnosis, timely damage detection, safety evaluation and necessary precautions, in order to prevent accidents such as the crack or the collapse of bridges and roads. However, the existing monitoring system is too expensive to be applied in low budget projects. This paper designs a portable, low-cost, low-power structural monitoring system, which is equipped with Polyvinylidene Fluoride (PVDF) traffic sensors, a central processing unit (CPU), an amplifier circuit, an Analog/Digital (A/D) converter circuit, a communication circuit, an alarm circuit, and analysis software. The proposed system can collect the critical data for a certain judge algorithm about the force of the pavement. An alarm will be signaled and the overweight data will be transmitted to the data center to make further analysis. At the same time, the license plate numbers of the overweight vehicles will be collected and sent to the traffic management department. The system has been tested and the results are satisfactory. Because of its features of simple structure, easy implementation, and low cost, the proposed system fills the application gaps of structural health monitoring in low-budget projects.     

智能汽车环境感知算法测试评价系统开发

基于硬件在环仿真平台和六自由度驾驶模拟器,开发了智能汽车环境感知算法测试评价系统。为兼顾仿真测试的可重复性、可拓展性及道路测试的真实性,采用真实交通场景作为输入场景信息;针对现有检测算法评价依赖人工识取的不足,提出了基于机器学习的标准检测算法和基于数据关联的检测评价算法,并将系统测评结果与人工识取结果进行对比。实验结果表明,所开发的测试评价系统方案可行,测试精度较高,实时性较好,在模糊、遮挡、光照变化等复杂环境下均可实现对各种不同特性的车道线和车辆目标的准确测评,能较好地满足智能汽车环境感知算法测试评价的要求。