A review on different pipeline fault detection methods

Pipeline faults like leakage and blockage always create problem for engineers. Detection of exact fault quantity and its location is necessary for smooth functioning of a plant or industry and safety of the environment. In this paper brief discussion is made on various pipeline fault detection methods viz. Vibration analysis, Pulse echo methodology, Acoustic techniques, Negative pressure wave based leak detection system, Support Vector Machine (SVM) based pipeline leakage detection, Interferometric fibre sensor based leak detection, Filter Diagonalization Method (FDM), etc. In this paper merit and demerits of all methods are discussed. It is found that these methods have been applied for specific fluids like oil, gas and water, for different layout patterns like straight and zigzag, for various lengths of pipeline like short and long and also depending on various operating conditions. Therefore, a comparison among all methods has been done based on their applicability. Among all fault detection methods, Acoustic reflectometry is found most suitable because of its proficiency to identify blockages and leakage in pipe as small as 1% of its diameter. Moreover this method is economical and applicable for straight, zigzag and long, short length pipes for low, medium and high density fluid.     

太子河流域硅藻群落与驱动因子的定量关系

定量研究水生生物对水环境参数的适宜值是评估栖息地质量和维持生物完整性的主要途径. 以辽宁省太子河流域为研究范例,选择Y(优势度指数)大于0.000 1的硅藻为研究对象,结合水环境参数,采用CCA(典范对应分析)、CART(分类回归树)和WA(加权平均回归分析)等方法,分析硅藻与水环境因子的关系,并计算硅藻对驱动因子的最适值. CCA结果表明,IOS(底质指数)、ρ(TDS)(TDS为总溶解固体)和ρ(COD_Mn)是硅藻群落的驱动因子;CART预测结果表明,IOS高的水环境硅藻密度高于IOS低的水环境,ρ(TDS)和ρ(COD_Mn)低的水环境硅藻密度高于ρ(TDS)和ρ(COD_Mn)高的水环境;WA结果显示,96种硅藻对IOS、ρ(TDS)和ρ(COD_Mn)的最适值范围分别为1.00~6.44、60.29~820.30 mg/L和0.46~2.89 mg/L. 钝端菱形藻解剖刀变种和尖端菱形藻适宜栖息于IOS较低而ρ(COD_Mn)较高的水环境, Gomphonema trancatum和肿大桥弯藻则适宜栖息于IOS较高的水环境;缠结异极藻二叉变种和尖细异极藻适宜栖息于ρ(TDS)较高的水环境,弧形峨眉藻和克洛钝脆杆藻则适宜栖息于ρ(TDS)较低的水环境;弧形峨眉藻和隐头舟形藻威蓝变种适宜栖息于ρ(COD_Mn)较低的水环境. 针杆藻和桥弯藻对IOS的最适值高于舟形藻和菱形藻以及其他藻种,96种硅藻对ρ(TDS)和ρ(COD_Mn)的最适值均表现为菱形藻和异极藻较高、针杆藻和桥弯藻较低.      

基于故障树分析法研究变电站检修触电事故防控

对变电站检修工作人员触电事故进行了故障树分析,通过定性分析,得出最小割集和结构重要度,并提出变电站检修触电事故的预防控制措施。     

基于BN的FTA在通用航空风险评价中的应用

针对事故树分析法(FTA)在风险评价中的局限性,采用以事故树为基础建立的贝叶斯网络(BN)风险模型,对通用航空中的两机空中相撞事故进行分析和推理,对事故模型进行改进和修正时,注重基事件的多态性和事件间的逻辑合理性。根据贝叶斯推理得出的数据,找到了事故的主要致因。结果表明,基于BN的FTA既能向前预测顶事件的发生概率,又能向后诊断基本事件的后验概率,可以更好地对通用航空风险进行评价。     

电气设备触电危险因素分析

我国现阶段生产、生活中触电事故增加,采用故障树的分析方法,对用电设备触电事故进行危险因素分析,得出保护接地在供配电系统中的重要作用。简要介绍保护接地,并指出目前我国用电设备保护接地中存在的问题及接地故障的防范措施。     

长江干线船舶碰撞原因的故障树分析一以武汉海事局辖区水域为例

以2007年武汉海事局辖区船舶碰撞资料为基础,利用故障树分析方法构建该水域船舶碰撞故障树,进行定性、定链分析,得到包括航行疏于戒备、车舵控制失误、疏于了望等14个主要危险因素及各因素的相关事故数。在故障树分析的基础上,构建布尔代数和最小割集,计算各危险旧素的概率重要度,以衡精各危险因素发生概率的变化给碰撞事故发生概率带来的影响;计算临界重要度,从敏感度和概率双重角度衡量每个危险因素的重要度。通过分析发现应急操作不当、违反其他规定、航行疏于戒备、违反分道通航、疲劳驾驶等人为因素是引起该水域船舶碰撞的重要原因。建议船公司重视船员应急培训,提高船员应急处置能力,海事管理机构加大相关法规宣传力度和监管力度。     

树皮配比对污泥生物-物理联合干燥中试的影响

污泥生物-物理联合干燥技术具有停留时间短、能耗低、减量显著等优势。研究利用自主研制的污泥生物-物理联合干燥反应系统考察了脱水污泥:树皮分别为5:3,7:3和9:3时,污泥生物-物理联合干燥过程中温度、含水率等参数的变化规律。结果表明,污泥温度随干燥时间的延长先增大后减小,含水率随反应时间延长逐渐降低。当脱水污泥:树皮的比例为7:3时,污泥温度迅速升高,在48 h达到3组辅料配比最大值59℃,而后迅速降低,经过168 h处理后含水率从78.6%降低到60.9%,获得水分去除率的最大值57.6%。向脱水污泥中添加适量树皮,能提高其生物-物理联合干燥过程中污泥温度,增强水分去除效果。     

可控震源振动器系统不确定性分析

为了解决可控震源施工过程中振动器系统的安全性问题,采用故障树法建立仿真 模型。通过失效分析得出各故障单元的失效模式及单元间分布规律,计算出振动器系统 的可靠度。开展对该系统敏感性的不确定性分析,得出影响振动器系统可靠性的关键因 素为重锤上部密封失效。采用有限元法对其应力场进行模拟仿真。结果表明:随着工作 频率的升高,O形圈应力波动越大,密封越容易失效。Von Mises应力随压缩率的增大先 减小后增大,随槽口半径的增大而增大;接触应力在一定范围内随压缩率及槽口半径的 增大而增大。为振动器的结构优化与可靠性设计提供理论依据,并为油气资源勘探开发 的安全作业提供工程应用指导。     

A new fault detection method for non-Gaussian process based on robust independent component analysis

Conventional fault detection method based on fast independent component analysis (FastICA) is sensitive to outliers in the modeling data and thus may perform poorly under the adverse effects of outliers. To solve such problem, a new fault detection method for non-Gaussian process based on robust independent component analysis (RobustICA) is proposed in this paper. A RobustICA algorithm which can effectively reduce the effects of outliers is firstly developed to estimate the mixing matrix and extract non-Gaussian feature called independent components (ICs) by robust whitening and robust determination of the maximum non-Gaussian directions. Furthermore, a monitoring statistic for each extracted IC is constructed to detect process faults. Simulations on a simple example of the mixing matrix estimation and a fault detection example in the continuous stirred tank reactor system demonstrate that the RobustICA achieves much higher estimation accuracy for the mixing matrix and the ICs than the commonly used FastICA algorithm, and the RobustICA-based fault detection method outperforms the conventional FastICA-based fault detection method in terms of the fault detection time and fault detection rate.     

Simulation-based fault propagation analysis—Application on hydrogen production plant

Recently production of hydrogen from water through the Cu–Cl thermochemical cycle is developed as a new technology. The main advantages of this technology over existing ones are higher efficiency, lower costs, lower environmental impact and reduced greenhouse gas emissions. Considering these advantages, the usage of this technology in new industries such as nuclear and oil is increasingly developed. Due to hazards involved in hydrogen production, design and implementation of hydrogen plants require provisions for safety, reliability and risk assessment. However, very little research is done from safety point of view. This paper introduces fault semantic network (FSN) as a novel method for fault diagnosis and fault propagation analysis by using evolutionary techniques like genetic programming (GP) and neural networks (NN), to uncover process variables’ interactions. The effectiveness, feasibility and robustness of the proposed method are demonstrated on simulated data obtained from the simulation of hydrogen production process in Aspen HYSYS^®. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns among process variables.