Approximate method for reliability assessment of complex phased mission systems

Phased-mission systems (PMSs) have wide applications in engineering practices, such as manmade satellites. Certain critical parts in the system, such as cold standby, hot standby and functional standby, are designed in redundancy architecture to achieve high reliability performance. State-space models such as Markov process have been used extensively in previous studies for reliability evaluation of PMSs with dynamic behaviors. The most popular way to deal with the dynamic behaviors is Markov process, but it is well known that Markov process is limited to exponential distribution. In practice, however, the lifetime of most machinery products can follow non-exponential distributions like the Weibull distribution which cannot be handled by the Markov process. In order to solve this kind of problem, we present a semi-Markov model combined with an approximation algorithm to analyze PMS reliability subjected to non-exponential failures. Furthermore, the accuracy of the approximation algorithm is investigated by comparing to an accurate solution, and a typical PMS (attitude and orbit control system) is analyzed to demonstrate the implementation of the method.     

Reliability Analysis of Excavator Rectifier Feedback System with Multi-State Components Based on Belief Universal Generating Function Method

In view of the complexity and uncertainty of system, both the state performances and state probabilities of multi-state components can be expressed by interval numbers. The belief function theory is used to characterize the uncertainty caused by various factors. A modified Markov model is proposed to obtain the state probabilities of components at any given moment and subsequently the mass function is used to represent the precise belief degree of state probabilities. Based on the primary studies of universal generating function(UGF)method, a belief UGF(BUGF) method is utilized to analyze the reliability and the uncertainty of excavator rectifier feedback system. This paper provides an available method to evaluate the reliability of multi-state systems(MSSs) with interval state performances and state probabilities, and also avoid the interval expansion problem.     

FUZZY RELIABILITY ANALYSIS OF STRUCTURE

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Product Quality Evaluation Method Based on Product Gene Theory

Traditional quality inspection based product quality evaluation method with complex process has high operating cost and requires more professional knowledge. To remove the above limitation, this paper leads product gene theory into product quality evaluation. Methods of quality influencing factors based modeling and encoding are established. Combined with similarity theory and product gene theory, a product gene similarity analysis based quality evaluation method is proposed. The proposed method is low cost, operates easily and requires less specialized knowledge. A case study is conducted to prove the correctness and feasibility of this method.     

Non-probabilistic reliability research on multi-variable hydraulic turbine blade model

Based on the interval mathematics and possibility theory, the variables existing in hydraulic turbine blade are described. Considering the multi-failure mode in turbine blade, multi-variable model is established to meet the actual situation. Thus, non-probabilistic reliability index is presented by comparing with the output range and the given range.     

基于现代设计技术的铁路起重机方案设计专家系统

建立了基于现代设计技术的铁路起重机方案设计专家系统，阐明了系统的总体结构和各模块的功能及其相互关系。该系统不但广泛地应用了优化设计、有限元分析、仿真价值工程先进现代设计技术，而且在知识获取和推理中结合了神经网络技术，并可对铁路起重机进行模糊设计。     

应用神经网络的疲劳裂纹演化规律的描述

疲劳裂纹数密度和裂纹扩展速率是描述材料疲劳损伤程度、预测材料疲劳寿命的两个重要参数。关于光滑试件表面疲劳裂纹数密度演化及疲劳裂纹扩展规律的定量化描述的研究，人们一直非常重视，业已提出了一些模型，但其适用范围均受到限制。因此，目前尚未有被广泛接受的裂纹演化定量模型。研究用ＢＰ神经网络描述疲劳裂纹数密度演化、裂纹扩展速率演化规律，结果表明：该方法合理可行，解决了传统描述方法确定长短裂纹分界点的困难，同     

机电一体化技术在工程机械上的应用

简要介绍了机电一体化技术,论述了该技术在工程机械上的应用情况,展望了工程机械机电一体化技术的未来,最后结合国内实际情况,提出了加速发展我国工程机械机电一体化产品的若干技术对策.     

Imprecise reliability assessment for heavy numerical control machine tools against small sample size problem

Small sample size problem is one of the main problems that heavy numerical control (NC) machine tools encounter in their reliability assessment. In order to deal with the small sample size problem, many indirect reliability data such as reliability data of similar products, expert opinion, and engineers’ experience are used in reliability assessment. However, the existing mathematical theories cannot simultaneously process the above reliability data of multiple types, and thus imprecise probability theory is introduced. Imprecise probability theory can simultaneously process multiple reliability data by quantifying multiple uncertainties (stochastic uncertainty, fuzzy uncertainty, epistemic uncertainty, etc.) together. Although imprecise probability theory has so many advantages, the existing natural extension models are complex and the computation result is imprecise. Therefore, they need some improvement for the better application of reliability engineering. This paper proposes an improved imprecise reliability assessment method by introducing empirical probability distributions to natural extension model, and the improved natural extension model is applied to the reliability assessment of heavy NC machine tool spindle to illustrate its effectiveness.