Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing.
Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing.
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DOI：
10.3390/s18092946
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发表时间：
2018-09-04
期刊：
影响因子：
--
通讯作者：
Rhee J
中科院分区：
文献类型：
--
作者：
Syafrudin M;Alfian G;Fitriyani NL;Rhee J
随着制造过程中采集的数据量的增加，监控系统正在成为管理决策的重要因素。目前的技术，如基于物联网(IoT)的传感器，可以被视为一种解决方案，可以提供对制造过程的高效监控。在这项研究中，提出了一个利用基于物联网的传感器、大数据处理和混合预测模型的实时监控系统。首先，开发了一种基于物联网的传感器，收集温度、湿度、加速度计和陀螺仪数据。物联网制造过程中产生的传感器数据具有实时性、大数据量、非结构化等特点。提出的大数据处理平台使用阿帕奇·卡夫卡作为消息队列，使用阿帕奇风暴作为实时处理引擎，使用MongoDB存储制造过程中的传感器数据。其次，对于所提出的混合预测模型，分别使用基于密度的带噪声应用空间聚类(DBSCAN)的离群点检测和随机森林分类来去除离群点传感器数据和提供制造过程中的故障检测。提出的模型在韩国的一条汽车制造装配线上进行了评估和测试。结果表明，基于物联网的传感器和所提出的大数据处理系统足以有效地监控制造过程。此外，以传感器数据为输入，提出的混合预测模型比其他模型具有更好的故障预测精度。预计拟议的系统将通过改善决策来支持管理，并将有助于防止制造过程中的错误造成意外损失。
With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.
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10.3390/s18072183
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Sensors (Basel, Switzerland)
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