Fuzzy reliability analysis of repairable industrial systems using soft-computing based hybridized techniques

The purpose of the present study is to analyze the fuzzy reliability of a repairable industrial system utilizing historical vague, imprecise and uncertain data which reflects its components’ failure and repair pattern. Soft-computing based two different hybridized techniques named as Genetic Algorithms Based Lambda–Tau (GABLT) and Neural Network and Genetic Algorithms Based Lambda–Tau (NGABLT) along with a traditional Fuzzy Lambda–Tau (FLT) technique are used to evaluate some important reliability indices of the system in the form of fuzzy membership functions. As a case study, all the three techniques are applied to analyse the fuzzy reliability of the washing system in a paper mill and results are compared. Sensitivity analysis has also been performed to analyze the effect of variation of different reliability parameters on system performance. The analysis can help maintenance personnel to understand and plan suitable maintenance strategy to improve the overall performance of the system. Based on results some important suggestions are given for future course of action in maintenance planning.     

Quality evaluation of Hanyuan Zanthoxylum bungeanum Maxim. Using computer vision system combined with artificial neural network: A novel method

A novel technology based on computer vision system (CVS) and artificial neural network (ANN) was developed for the quality evaluation of Hanyuan Zanthoxylum bungeanum Maxim (HZB). The quality evaluation of HZB mainly depended on its colour, odour substances, and impurities. In this study, the contents of volatile oil (VOC), total alkylamides (TALC) and impurities (IMC) were determined and used as indices for quality control of HZB. Furthermore, CVS was also performed to determine the colour parameters (RGB values) and further transforms to CIE L*, a^*, and b*. Then, ANN was carried out to analyse the correlations between colour values obtained by CVS and quality parameters of HZB (VOC, TALC, and IMC). Higher performance and stability were presented by using CVS for determining the coloristic values of HZB. In addition, the present results also showed that the established method based on ANN could be used to predict the VOC, TALC, and IMC of HZB with the R² values of 0.9991, 0.9995, and 0.9998, respectively. This novel technology based on CVS combined with ANN could be used for the rapid, non-destructive, and effective evaluation of the quality of HZB.     

Inelastic lateral-torsional buckling capacity of corroded web opening steel beams using artificial neural networks

Steel beams are extensively used as main structural in various building infrastructures such as bridge girders. Steel structures are prone to suffer various types of damage as they get older. Corrosion may be one of the most important types of damage in ageing steel structures. In a pessimistic state, an area of opening may be created along the bottom zone of the beam's web due to corrosion. The existence of opening in the web of a beam can reduce the resistance of the beam to shear, bending or buckling. This study deals with the stability of damaged steel I-beams with web opening. A three-dimensional finite element (FE) model using ABAQUS for inelastic analysis of beams has been developed to assess the effect of web opening on the lateral-torsional buckling (LTB) capacity. Artificial neural network approach has also been employed to derive empirical formula for predicting the residual ultimate LTB moment of damaged steel I-beams using obtained FE results. It is found out that the proposed formula can accurately predict ultimate capacities of doubly symmetric steel I-beams with different sizes of web opening. Hence, the results of this study can be used for better prediction of residual ultimate LTB moment of corrosion damaged girders of steel existing structures by practice engineers.     

A neural network bid/no bid model: the case for contractors in Syria

Despite the crucial importance of the ‘bid/no bid’ decision in the construction industry, it has been given little attention by researchers. This paper describes the development and testing of a novel bid/no bid model using the artificial neural network (ANN) technique. A back‐propagation network consisting of an input buffer with 18 input nodes, two hidden layers and one output node was developed. This model is based on the findings of a formal questionnaire through which key factors that affect the ‘bid/no bid’ decision were identified and ranked according to their importance to contractors operating in Syria. Data on 157 real‐life bidding situations in Syria were used in training. The model was tested on another 20 new projects. The model wrongly predicted the actual bid/no bid decision only in two projects (10%) of the test sample. This demonstrates a high accuracy of the proposed model and the viability of neural network as a powerful tool for modelling the bid/no bid decision‐making process. The model offers a simple and easy‐to‐use tool to help contractors consider the most influential bidding variables and to improve the consistency of the bid/no bid decision‐making process. Although the model is based on data from the Syrian construction industry, the methodology would suggest a much broader geographical applicability of the ANN technique on bid/no bid decisions.     

人工神经网络模型在应对城市内涝中的应用

现在城市排水系统的规划和运营管理一般不考虑城市下游受纳水体变化的影响,这也是国内城市内涝频发的原因之一。目前,越来越多的城市应用模型来应对城市内涝,模型中受纳水体的水位一直以来都是利用经验设定一个恒定值。本文利用ANN(人工神经网络)技术,以城市上游观测站的实测水位为输入,以城市未来某时段的水位为目标选择合理的参数,建立了预测河流水位模型。利用更准确的动态预测值代替恒定值,可以提高城市排水系统水力模型的精度。选择某地区水位站的资料,对预报模型进行了检验,结果表明,在合理选择输入层数据和预测时间段的条件下,可以取得很好的预报结果。     

基于小波包分解和奇异谱分析方法的立铣加工颤振监测

在立铣加工过程中,颤振是加工过程失稳的一个最重要的原因。颤振将会严重影响工件表面质量和材料去除率,加剧刀具磨损和恶化工作环境。虽然大部分颤振监测系统可以监测到颤振发生,但颤振发生时已经对工件和刀具产生了严重的损伤,因此,需要提前监测到颤振特征。在颤振发生过程中,振动信号具有在时域中不断增大,在频域中能量频移的特性。考虑这两个振动信号特征,提出了一种颤振特征提取方法。提取颤振发生频带中振动信号的能量比和奇异谱熵系数作为两个颤振特征,并通过人工神经网络模型实现切削颤振的识别。文中提出的颤振监测系统包括特征提取和分类,能够精确辨识立铣加工中的稳定、过渡和颤振状态。     

利用BP网络预测板材力学性能的软件开发

 针对热轧板带的力学性能预报问题,利用人工神经网络中的BP算法建立原始化学成分和热轧生产的主要工艺参数与产品力学性能之间的关系,并开发出专门的应用软件。软件共分3大部分：数据处理部分、人工神经网络训练部分、运用成熟网络预报部分。本软件的特点是直观、方便、稳定。数据均从稳定生产的现场取得。采用此软件对SS400钢的性能进行预报,经过10万次训练后,产品力学性能的预报值与实际值拟合良好,预报结果的相对误差很小。     

神经网络在引信信号检测中的应用

参考有关文献论述了人工神经网络技术在引信信号检测中的应用,重点说明了ANN在消除噪声和抗有源干扰方面的应用,指明了应用于引信技术时应注意的问题及应用前景。     

组合式非周期缺陷接地结构的RBF神经网络模型

组合式非周期缺陷接地结构(CNPDGS)是由光子带隙结构(PBG)发展而来的,它具有结构简单、电路尺寸小、插入损耗小、设计参数少等优点。本文采用RBF神经网络建立了CNPDGS的神经网络模型。神经网络训练成功后,在其学习范围内,该模型能立刻给出任意尺寸结构的准确可靠的传输系数(S21)。结果证明神经网络建模的方法具有快速、准确、可靠等优点,具有很高的实用价值。