Applications of virtual reality in maintenance during the industrial product lifecycle: A systematic review

As a state-of-the-art computer technology, virtual reality (VR) is considered to play an important role in helping manufacturing companies stay competitive in the international market. However, despite the achievements made in the field of VR, it is still an emerging technology that lacks deeper exploration and development in industrial application scenarios, especially in the coming fourth industrial revolution (Industry 4.0). This paper aims to systematically investigate the applications of VR in industrial maintenance to discover evidence of its values, limitations, and future directions so that VR can be guided to better serve manufacturing enterprises in remaining competitive in the coming Industry 4.0. A systematic literature review (SLR) methodology is adopted to review primary studies on this topic, by which 86 studies are ultimately included. The results show that VR has proved its value in benefiting maintenance issues through the product lifecycle. However, VR is still not an indispensable element for the lifecycle management of products regarding maintenance-related issues. Several key findings are concluded based on the analysis of the 86 studies. This review is valuable for researchers who are interested in the application of VR technology in maintainability design, maintenance training or maintenance task assistance.     

基于功率调节的IWSN实时可靠路由研究

针对工业无线传感器网络对延迟、可靠传输以及成本的特殊要求,研究了一种基于功率调节的实时可靠路由协议,将两跳速度策略引入到路由算法中,提高工业无线传感器网络的实时性;将节点剩余能量以及能耗同时考虑,以平衡网络能量并提高网络寿命;引入功率调节机制以绕开路由空洞,提高节点可靠性.仿真结果显示,与已有方法相比,采用的方法在总可靠度、截止期错失率以及包平均延迟上有着显著改善.     

Efficiency evaluation of robots in machining applications using industrial performance measure

The paper is devoted to the robotic based machining. The main focus is made on robot accuracy in milling operation and evaluation robot capacity to perform the task with desired precision. Particular attention is paid to the proper modeling of manipulator stiffness properties and the cutting force estimation. In contrast to other works, the robot performance is evaluated using the circularity norm that evaluates the contortion degree of the benchmark circle to be machined. The developed approach is applied to five industrial robots of KUKA family, which have been ranked for several machining tasks. The validity of the proposed technique was confirmed by experimental study dealing with robot-based machining of circular grooves for several workpiece samples and different locations.     

特定人群智能终端功能使用行为研究

目的为设计更符合特定人群需求的产品,分析需求在产品设计中的表现形式,提出采用多种定性与定量分析方法结合的用户需求重要度分析决策方法。方法采用因子分析和回归分析法,定性分析用户需求的影响因素及重要度,对用户所需功能重要度排序。最后采用卡方检验,比较不同年龄段对功能需求的差异性。结果年龄增加对功能需求变化产生了显著影响,用户对游戏功能的需求差异显著。结论老年人对智能手机的功能偏重于娱乐和社交。     

基于范式语法的工控协议Fuzzing测试技术

针对工控协议Fuzzing测试存在测试脚本编写工作量大、测试用例的覆盖面小、测试效率低等问题,文章提出了一种基于范式语法的工控协议Fuzzing测试方法。方法首先以改进的扩展巴科斯范式(Modified Augmented Backus-Naur Form: MABNF)描述工控协议;然后根据范式语法模型,将报文样本解析为范式语法变异树,进而生成范式语法变异树的描述脚本;文章提出了基于MABNF变异树的测试用例生成算法(MABNF-mutation-tree based Test-cases Generating Method: MTGM),算法先对MABNF变异树进行深度优先遍历,再采用语义变异策略对节点实施变异;最后利用MTGM得到冗余少、有效性高的测试用例集。使用该方法,文章实现了基于Peach的工控协议Fuzzing测试原型系统M-Peach,对供水SCADA系统进行了测试。与Peach的对比实验表明,文章所提出的方法能有效减少测试冗余,提高测试用例集的覆盖面和测试效率。     

A hybrid intelligent model for network selection in the industrial Internet of Things

Industrial Internet of Things (IIoT) plays an important role in increasing productivity and efficiency in heterogeneous wireless networks. However, different domains such as industrial wireless scenarios, small cell domains and vehicular ad hoc networks (VANET) require an efficient machine learning/intelligent algorithm to process the vertical handover decision that can maintain mobile terminals (MTs) in the preferable networks for a sufficient duration of time. The preferred quality of service parameters can be differentiated from all the other MTs. Hence, in this paper, the problem with the vertical handoff (VHO) decision is articulated as the process of the Markov decision aimed to maximize the anticipated total rewards as well as to minimize the handoffs’ average count. A rewards function is designed to evaluate the QoS at the point of when the connections take place, as that is where the policy decision for a stationary deterministic handoff can be established. The proposed hybrid model merges the biogeography-based optimization (BBO) with the Markov decision process (MDP). The MDP is utilized to establish the radio access technology (RAT) selection’s probability that behaves as an input to the BBO process. Therefore, the BBO determines the best RAT using the described multi-point algorithm in the heterogeneous network. The numerical findings display the superiority of this paper’s proposed schemes in comparison with other available algorithms. The findings shown that the MDP-BBO algorithm is able to outperform other algorithms in terms of number of handoffs, bandwidth availability, and decision delays. Our algorithm displayed better expected total rewards as well as a reduced average account of handoffs compared to current approaches. Simulation results obtained from Monte-Carlo experiments prove validity of the proposed model.     

Robot apology as a post-accident trust-recovery control strategy in industrial human-robot interaction

Due to safety requirements for Human-Robot Interaction (HRI), industrial robots have to meet high standards of safety requirements (ISO 10218). However, even if robots are incapable of causing serious physical harm, they still may influence people's mental and emotional wellbeing, as well as their trust, behaviour and performance in close collaboration. This work uses an HTC Vive Virtual Reality headset to study the potential of using robot control strategies to positively influence human post-accident behaviour. In the designed scenario, a virtual industrial robot first makes sudden unexpected movements, after which it either does or does not attempt to apologise for them. The results show that after the robot tries to communicate with the participants, the robot is reported to be less scary, more predictable and easier to work with. Furthermore, postural analysis shows that the participants who were the most affected by the robot's sudden movement recover 74% of their postural displacement within 60 s after the event if the robot apologised, and only 34% if it did not apologise. It is concluded, that apologies, which are commonly used as a trust-recovery strategy in social robotics, can positively influence people engaged with industrial robotics as well.Relevance to industryFindings can be used as guidelines for designing robot behaviour and trust-recovery control strategies meant to speed up human recovery after a trust-violating event in industrial Human-Robot Interaction.     

Anomaly Detection in ICS Datasets with Machine Learning Algorithms

An Intrusion Detection System (IDS) provides a front-line defense mechanism for the Industrial Control System (ICS) dedicated to keeping the process operations running continuously for 24 hours in a day and 7 days in a week. A well-known ICS is the Supervisory Control and Data Acquisition (SCADA) system. It supervises the physical process from sensor data and performs remote monitoring control and diagnostic functions in critical infrastructures. The ICS cyber threats are growing at an alarming rate on industrial automation applications. Detection techniques with machine learning algorithms on public datasets, suitable for intrusion detection of cyber-attacks in SCADA systems, as the first line of defense, have been detailed. The machine learning algorithms have been performed with labeled output for prediction classification. The activity traffic between ICS components is analyzed and packet inspection of the dataset is performed for the ICS network. The features of flow-based network traffic are extracted for behavior analysis with port-wise profiling based on the data baseline, and anomaly detection classification and prediction using machine learning algorithms are performed.     

Specifying and optimizing robotic motion for visual quality inspection

Installation or even just modification of robot-supported production and quality inspection is a tedious process that usually requires full-time human expert engagement. The resulting parameters, e.g. robot velocities specified by an expert, are often subjective and produce suboptimal results. In this paper, we propose a new approach for specifying visual inspection trajectories based on CAD models of workpieces to be inspected. The expert involvement is required only to select – in a CAD system – the desired points on the inspection path along which the robot should move the camera. The rest of the approach is fully automatic. From the selected path data, the system computes temporal parametrization of the path, which ensures smoothness of the resulting robot trajectory for visual inspection. We then apply a new learning method for the optimization of robot speed along the specified path. The proposed approach combines iterative learning control and reinforcement learning. It takes a numerical estimate of image quality as input and produces the fastest possible motion that does not result in the degradation of image quality as output. In our experiments, the algorithm achieved up to 53% cycle time reduction from an initial, manually specified motion, without degrading the image quality. We show experimentally that the proposed algorithm achieves better results compared to some other policy learning approaches. The described approach is general and can be used with different types of learning and feedback signals.     

基于东盟自由贸易背景下的中国工业设计教育国际化研究

本文对基于东盟自由贸易背景下的国际化工业设计教育进行分析和探讨。分析了东盟自由贸区背景下工业设计教育改革的重大意义,研究了基于东盟自由贸易背景下的国际化工业设计教育的教学培养目标、教育模式、师资配备等,提出了一系列在国际视野下工业设计教育有效开展的具体办法,为设计教育国际化改革提供参考。