Unified uncertainty analysis by the mean value first order saddlepoint approximation

Uncertainties exist in products or systems widely. In general, uncertainties are classified as epistemic uncertainty or aleatory uncertainty. This paper proposes a unified uncertainty analysis (UUA) method based on the mean value first order saddlepoint approximation (MVFOSPA), denoted as MVFOSPA-UUA, to estimate the systems probabilities of failure considering both epistemic and aleatory uncertainties simultaneously. In this method, the input parameters with epistemic uncertainty are modeled using interval variables while input parameters with aleatory uncertainty are modeled using probability distribution or random variables. In order to calculate the lower and upper bounds of system probabilities of failure, both the best case and the worst case scenarios of the system performance function need to be considered, and the proposed MVFOSPA-UUA method can handle these two cases easily. The proposed method is demonstrated to be more efficient, robust and in some situations more accurate than the existing methods such as uncertainty analysis based on the first order reliability method. The proposed method is demonstrated using several examples.     

Analysis of maintenance policies for finite life-cycle multi-state systems

Maintenance policies for multi-state systems (MSS) are often analyzed under infinite horizon assumptions. In practice, it is important to consider maintenance policies under a finite horizon because the life cycles of most systems are finite. In this paper, we consider a finite life-cycle MSS that is subject to both degradation and Poisson failures. We study two classes of maintenance policies – preventive replacements and corrective replacements, and their effectiveness in controlling the customer’s expected discounted maintenance cost (EDMC). For both policies, replacement decisions are modelled via two control parameters – a threshold on the current system state and a threshold on the residual life cycle, which is measured as the time span from present to the end of life cycle. We derive close-to-explicit forms of the cost models under each of the policy. Methodologies for optimizing the maintenance thresholds are further proposed. Computational results verify that preventive replacements outperform corrective replacements typically when the downtime cost per failure is relatively high compared to the repair cost.     

循环冷却水酸度动态变化的研究

本文研究了循环冷却水的动态过程及其变化规律,根据南京栖化厂循环水的报表数据及实验结果表明:循环冷却水酸度的动态变化过程具有随机周期性,它与加酸时间及加酸方法有关,长历程(4个月)的数据可用时间序列模型(1-B~3)AR(8)表示。短历程(74小时)可用(1-B)AR(2)描述。按照模型及谱分析提出了合适的加酸方法、发挥缓蚀药剂的作用,以及提前预报,加强水质的管理。     

XPS研究Fe2O3纳米粒子表面包覆无机膜

针形铁黄纳米粒子表面包覆无机物如Si、Co等氧化物形成复合纳米粒子，是改善金属磁性记录粉性能的重要方式。采用ICP，TEM，XPS方法研究复合纳米粒子的表面性质，结果表明包硅复合粒子的表面形成均匀、致密的SiO2薄膜，表面层与基体表面间的界面结构类似异质结，导致粒子的XPS谱图中Fe 2p谱峰发生2.7eV的化学位移；而包钴复合粒子由于钴在铁黄表面的吸附发生在部分晶面上而无法形成均匀、致密的薄膜，XPS谱图主要发生因表面荷电而导致的物理位移。     

Reliability assessment for systems suffering common cause failure based on Bayesian networks and proportional hazards model

The Bayesian network (BN) is an efficient tool for probabilistic modeling and causal inference, and it has gained considerable attentions in the field of reliability assessment. The common cause failure (CCF) is simultaneous failure of multiple elements in a system under a common cause, and it is a common phenomenon in engineering systems with dependent elements. Several models and methods have been proposed for modeling and assessment of complex systems with CCF. In this paper, a new reliability assessment method is proposed for the systems suffering from CCF in a dynamic environment. The CCF among components is characterized by a BN, which allows for bidirectional reasoning. A proportional hazards model is applied to capture the dynamic working environment of components and then the reliability function of the system is obtained. The proposed method is validated through an illustrative example, and some comparative studies are also presented.     

An algorithm of discrete-time Bayesian network for reliability analysis of multilevel system with warm spare gate

Discrete-time Bayesian network (DTBN) is a popular tool for the reliability analysis of dynamic systems, which, however, is insufficient in analyzing the reliability of multilevel system (MLS) with warm spare (WSP) gates. Additionally, conventional DTBNs are not able to consider the situation that dormant components and primary components may fail during the same time interval. To this end, this paper analyzes the dynamic reliability characteristics of dormant systems with WSP gates by utilizing DTBNs. Moreover, an algorithm of modeling the conditional probability table (CPT) for WSP gates together with a new schedule of constructing dynamic Bayesian networks is put forward. The validation of the proposed techniques is implemented by Monte Carlo simulation (MCS) and reliability analysis of an actual communication station system.     

A comparison study of support vector machines and hidden Markov models in machinery condition monitoring

Condition classification is an important step in machinery fault detection, which is a problem of pattern recognition. Currently, there are a lot of techniques in this area and the purpose of this paper is to investigate two popular recognition techniques, namely hidden Markov model and support vector machine. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. The comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that support vector machine has better classification performance in this area.     

串联混合动力公交车控制策略的设计及实现

为了提高串联混合动力公交车的燃油经济性和排放性能,对串联混合动力公交车的控制策略进行了研究.从分析整车的基本工作模式及能量流出发,设计了功率跟随与恒温器相结合的综合控制策略,介绍了其控制功能的软件实现.结果表明,该策略不仅减少了蓄电池的过度循环和大电流充放电,而且避免了发动机的频繁起停,并使发动机工作点稳定在经济性和排放性较好的区域.     

试论复配添加剂充当非法添加物载体的风险

以近年来发生的苏丹红染料和邻苯二甲酸酯类增塑剂污染食品为例, 说明了复配食品添加剂充当非法添加物载体的风险;分析了复配食品添加剂潜伏风险因素的成因;讨论了非法添加物通过复配食品添加剂携带污染食品的特点;从复配添加剂生产、食品生产中复合食品添加剂使用与以及对上述两者的监管等方面提出了风险管理与应对的措施。