The development of a decision making framework for evaluating the trade-off solutions of cleaner seaborne transportation

The general rise in marine fuel prices in combination with ever-more stringent environmental regulations resulting from IMO conventions and EU Directives have become the main industry drivers for seaborne transportation to become cleaner and more energy efficient. Compliance with existing and soon-to-be-enacted regulations requires evaluating the trade-off between often-conflicting options to select the best available technology or fuel source. Although the traditional way of dealing with this issue has been to apply a cost benefit analysis, this kind of analysis does not adequately consider the complexities of the problem, such as incorporating linguistic preferences or interrelations amongst attributes, experts and their preferences.The challenge in such an analysis corresponds to that of a multiple attribute decision-making problem in which a finite number of alternatives are assessed with regards to a finite number of attributes and experts and ranked from the best to the worst.In this paper, a comprehensive and holistic decision-making framework is proposed to overcome the barriers of cost-benefit analysis techniques, facilitating the inclusion of all possible combinations of decision-making parameters and their discrete values, which will eventually help the industry achieve cleaner seaborne transportation.To demonstrate the applicability of the proposed framework, this paper focuses on a real-life study case involving an environmental compliance problem in the Port of Copenhagen, Denmark, in relation to a particular EU Directive. In conclusion, the proposed framework can be applied as a generalised decision-making model to similar compliance issues encountered within other modes of transportation such as rail and road.     

An integrated solution for lane level irregular driving detection on highways

Global Navigation Satellite Systems (GNSS) has been widely used in the provision of Intelligent Transportation System (ITS) services. Current meter level system availability can fulfill the road level applications, such as route guide, fleet management and traffic control. However, meter level of system performance is not sufficient for the advanced safety applications. These lane level safety applications requires centimeter/decimeter positioning accuracy, with high integrity, continuity and availability include lane control, collision avoidance and intelligent speed assistance, etc. Detecting lane level irregular driving behavior is the basic requirement for these safety related ITS applications. The two major issues involved in the lane level irregular driving identification are accessing to high accuracy positioning and vehicle dynamic parameters and extraction of erratic driving behaviour from this and other related information. This paper proposes an integrated solution for the lane level irregular driving detection. Access to high accuracy positioning is enabled by GNSS and Inertial Navigation System (INS) integration using filtering with precise vehicle motion models and lane information. The detection of different types of irregular driving behaviour is based on the application of a Fuzzy Inference System (FIS). The evaluation of the designed integrated systems in the field test shows that 0.5 m accuracy positioning source is required for lane level irregular driving detection algorithm and the designed system can detect irregular driving styles.     

A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation

Although various innovative traffic sensing technologies have been widely employed, incomplete sensor data is one of the most major problems to significantly degrade traffic data quality and integrity. In this study, a hybrid approach integrating the Fuzzy C-Means (FCM)-based imputation method with the Genetic Algorithm (GA) is develop for missing traffic volume data estimation based on inductance loop detector outputs. By utilizing the weekly similarity among data, the conventional vector-based data structure is firstly transformed into the matrix-based data pattern. Then, the GA is applied to optimize the membership functions and centroids in the FCM model. The experimental tests are conducted to verify the effectiveness of the proposed approach. The traffic volume data collected at different temporal scales were used as the testing dataset, and three different indicators, including root mean square error, correlation coefficient, and relative accuracy, are utilized to quantify the imputation performance compared with some conventional methods (Historical method, Double Exponential Smoothing, and Autoregressive Integrated Moving Average model). The results show the proposed approach outperforms the conventional methods under prevailing traffic conditions.     

电控发动机ECU故障诊断仪的开发

介绍了基于16位单片机MC9S12DP256的电控发动机ECU故障诊断仪的开发,阐述了其硬件设计及 软件开发。该故障诊断仪能够实现对发动机的参数测量、在线故障诊断和执行器测试等功能,结构简单,成本低, 性能可靠等。     

液压挖掘机液压系统的常见故障及诊断排除

列举了液压挖掘机液压系统整机无动作、整机动作缓慢无力以及局部故障3种常见故障,分析了故障发生的原因,并给出了故障的排除方法.     

浅谈比亚迪E5电池管理系统故障诊断与排除

本文根据2018款比亚迪E5结构与原理,通过对比亚迪E5车辆电池管理系统的故障进行分析、诊断,并结合维修手册和相关资料去思考,找到该故障所在位置,排除车辆无法上电的故障,恢复该车正常行驶。这充分证明了如何运用所掌握的知识和原理,以及车辆新技术,并结合维修手册和相关资料去思考和解决车辆故障修理难题。     

煤炭码头自动化运输系统容错控制方法

为了解决煤炭码头自动化运输系统对控制的可靠性和安全性要求, 提出了一种容错控制方法。运用径向基函数神经网络构造故障诊断器, 通过容错故障范例检索, 建立了从故障诊断到控制律重构之间的映射关系, 实施运输系统的控制律实时重构。应用结果表明: 所提出的容错控制方法明显提高了煤炭码头自动化运输系统的技术性能和安全可靠性, 系统的各类故障的识别率大于97%, 各类故障的误诊率小于1%, 系统的有效度由原来的90%提高到98%以上。     

基于自适应多尺度形态学分析的车轮扁疤故障诊断方法

建立了56自由度车辆动力学模型与车轮扁疤模型, 计算了车辆的动态响应。车辆的振动信息往往受到轨道不平顺和车速波动等因素的影响, 为了能在强噪声背景下有效提取轮轨冲击特征, 提出了自适应多尺度形态学滤波分析方法, 研究了车轮扁疤引起的轴箱振动特征, 分析了轨道激扰和车辆运行速度对车轮扁疤故障诊断效果的影响。仿真结果表明: 在100、150、200km·h^-1的车速和美国五级谱、三级谱的激扰下, 分别使用7个和9个尺度的结构元素进行形态学滤波, 正确地识别出10、15、20Hz车轮扁疤故障频率。实测结果表明: 当车速为40km·h^-1时, 使用7个尺度的结构元素进行形态学滤波, 提取出了2 Hz的故障频率, 此频率与理论故障频率相对应, 诊断结果可靠。     

基于瞬时转速的内燃机模糊神经网络故障诊断方法的研究

文章研究了基于瞬时转速波形分类的内燃机故障诊断方法,从瞬时转速波形中定义了3种用于诊断的波形特征参数,提出了用模糊理论对内燃机瞬时转速波形识别的故障诊断方法,并进行了大量的实验研究与分析。实验表明该方法对内燃机故障诊断是一种较为有效的方法。     

基于模糊灰色关联神经网络的综合故障诊断方法研究

考虑到灰色关联诊断中模式向量各参数量纲不同,数量级存在差异,且各种特征因子有各自的应用缺陷,通过将模糊理论和灰色关联度相结合.形成所提取故障特征值的模糊关联度,将模糊关联度作为神经网络的输入量进行故障种类的识别,提出了模糊灰色关联神经网络故障识别方法,实现了针对在不同工作状态下的故障识别.对舰船主动力系统故障进行实例诊断研究,诊断结果表明,该方法准确、有效.