基于人因工程对现代制造企业操作者脑力疲劳的研究

发布时间：2018-11-05 09:10

【摘要】：现代制造企业自从有了先进科学技术的支撑,生产方式、生产模式、生产范围相较传统制造企业产生了巨大的变化。现代制造企业越来越追求最大程度上的机械化、自动化,以至于达到无人化工厂的地步,但是真正的无人化工厂是不可能实现的。在现代制造企业中,人并没有被排除在现代生产系统之外,与传统制造企业相比,人在现代制造企业中仅仅是角色、地位和工作方式发生了变化。人的体力劳动开始逐步被机械自动化设备所代替,人的体力劳动在减少的同时脑力劳动和脑体相结合的劳动逐步开始增多。在这样的背景环境下由脑力疲劳作业造成的生产效率下降、生产质量和安全事故现象屡屡发生。因此,如何准确的测量出人在现代制造业中的脑力疲劳程度,预防脑力疲劳作业成为人因工程在现代制造企业中一个新的研究热点。通过阅读大量的国内外关于疲劳研究的文献,分析了国内外基于人因工程的疲劳研究动态,作者发现关于脑力疲劳的文章主要侧重于疲劳测试手段的研究,对于疲劳状态测量的应用仅仅涉及到汽车和飞机的驾驶疲劳,而对于现代制造企业中操作者的疲劳仅处于研究人机结构设计来减缓操作者肌肉疲劳的阶段,几乎没有考虑操作者的脑力疲劳。本文通过分析比较目前常用的脑力疲劳评价方法,选取主观法和生理法相结合的方法对现代制造企业中操作者的脑力疲劳进行评价。生理法的评价指标经过分析比较确定为脑电波和眼动。随后本文采用划消实验、警戒绩效作业实验和注意力集中实验来模拟现代制造企业中操作者的工作内容,并采集模拟实验过程中操作者的主观评价数据和生理数据。对采集的实验数据经过处理分析以后,又使用BP神经网络建立了操作者疲劳状态的评价模型,利用实验所得的真实数据进行了神经网络的训练,又对训练后的神经网络进行了实测数据的验证。研究结果表明:采用脑电波和眼动作为评价指标,利用BP神经网络建立的现代制造企业操作者脑力疲劳状态的评价模型可以准确的确定操作者的脑力疲劳状态,其准确度可达93.3%,比主观评价法更为精确和方便。
[Abstract]:With the support of advanced science and technology, the production mode, production mode and production range of modern manufacturing enterprises have changed greatly compared with traditional manufacturing enterprises. Modern manufacturing companies are increasingly pursuing maximum mechanization and automation to the point where they reach the level of an unmanned chemical plant, but true unmanned chemical plants are impossible to achieve. In modern manufacturing enterprises, people are not excluded from the modern production system. Compared with the traditional manufacturing enterprises, the role, status and working mode of human beings in modern manufacturing enterprises have changed. People's physical labor began to be replaced by mechanical automation equipment gradually, human physical labor in the reduction of mental work and brain-body combination of labor gradually began to increase. In such a background environment, the production efficiency due to mental fatigue work decreased, production quality and safety accidents occurred frequently. Therefore, how to accurately measure the degree of mental fatigue in modern manufacturing industry and prevent mental fatigue has become a new research hotspot in modern manufacturing enterprises. By reading a large number of domestic and foreign literatures on fatigue research, this paper analyzes the research trends of fatigue based on human engineering at home and abroad. The author finds that the article on mental fatigue mainly focuses on the research of fatigue testing methods. The application of fatigue state measurement only involves the driving fatigue of cars and aircraft, but the fatigue of operators in modern manufacturing enterprises is only at the stage of studying man-machine structure design to reduce the fatigue of operator muscles. Almost no account is taken of the operator's mental fatigue. Through the analysis and comparison of the commonly used mental fatigue evaluation methods, this paper selects the subjective method and the physiological method to evaluate the mental fatigue of the operators in modern manufacturing enterprises. The evaluation index of physiological method was determined as brain wave and eye movement by analysis and comparison. Then this paper uses the erasure experiment, the alert performance experiment and the attention concentration experiment to simulate the work content of the operator in the modern manufacturing enterprise, and collects the subjective evaluation data and physiological data of the operator in the simulation experiment process. After processing and analyzing the collected experimental data, the BP neural network is used to establish the evaluation model of the operator fatigue state, and the real data obtained from the experiment are used to train the neural network. The trained neural network is verified by the measured data. The results show that the brain wave and eye movement are used as evaluation indexes, and the evaluation model of operator's mental fatigue state established by BP neural network can accurately determine the operator's mental fatigue state. The accuracy can reach 93.33, which is more accurate and convenient than subjective evaluation.
【学位授予单位】：中北大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TB18;F425

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