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传统制造业如何看待工业物联网的冲击?

作者: 来源: 日期:2018-9-20 10:49:37 人气:102

制造行业的企业往往拥有并运行大量的工业设备,所有这些设备都需要监控和维护。对于现有的部署,工业物联网使得基于更精确数据的制造过程中决策的改进成为可能。它还可以用来提高生产质量和正常运行时间,因为从网络上的设备和钱汇娱乐收集的数据可以实现对生产设备的实时和预测性维护。

工业物联网背后的主要思想是使机器在制定决策时比人类更智能、更高效。这依赖于准确、一致地捕获和传递数据。信立科技多年从事工业物联网的核心产品无线钱汇娱乐,在数据的抓取和传输上技术成熟,开发了系列网关、测控、传感装置,并应用到各个制造业中,为合作伙伴带来了高效率的生产决策基础。

在大数据支撑下,机器学习,系统可以被训练以发现潜在的模式,这些模式将对未来的失败给出指示。如果结果是有关的,他们可以立即调查。这些信息以前需要花费数周的时间才能发现,并依赖于每个技术熟练的专业人员的可用性。现在,使用实时数据可以帮助那些拥有合适技能的人在多个地点监控更多的机器,从而更快地做出维护决策。反过来,制造业效率可以大大加快。

除了预测性维护以外,机器学习在工业性能方面也有很大的改进。要做到这一点,企业需要了解所有可用的数据,对数据进行量化,并根据收到的信息提供有关如何最好地进行制造过程的深入见解。机器学习可以用来演示如何提高性能,并在同一级别的投资提供更快的结果。

在制造行业中,时间就是金钱。随着工业物联网战略的实施,从单独的钱汇娱乐到可用的分析和自动化,制造商可以围绕整体效率和成本做出更好的业务决策。工业物联网战略在可持续和绿色生产实践以及供应链可追溯性方面也具有很大的潜力。

随着“循环经济”在企业的应用,从产品到包装的所有要素都应该被重复使用,并在整个价值链中循环利用,工业物联网的实施对其追踪结果至关重要。

Manufacturing enterprises often own and operate a large number of industrial equipment, all of which need to be monitored and maintained. For existing deployments, the Industrial Internet of Things makes it possible to improve decision-making in manufacturing processes based on more accurate data. It can also be used to improve production quality and uptime, as data collected from devices and sensors on the network enables real-time and predictive maintenance of production equipment.

The main idea behind the industrial Internet of things is to make machines more intelligent and efficient than human beings in making decisions. This depends on accurate and consistent capture and transmission of data. Xinli Technologies has been engaged in wireless sensor which is the core product of industrial Internet of Things for many years. It has developed a series of gateways, measurement and control, sensor devices and applied them to various manufacturing industries. It has brought high-efficiency production decision-making basis for partners.

Supported by large data, machine learning, systems can be trained to discover potential patterns that indicate future failures. If the results are relevant, they can investigate immediately. It took weeks to discover this information and depended on the availability of each skilled professional. Now, using real-time data can help those with the right skills monitor more machines in multiple locations, making maintenance decisions faster. In turn, manufacturing efficiency can be greatly accelerated.

In addition to predictive maintenance, machine learning has also improved in terms of industrial performance. To do this, companies need to understand all the available data, quantify the data, and provide in-depth insights into how best to conduct the manufacturing process based on the information received. Machine learning can be used to demonstrate how to improve performance and provide faster results at the same level of investment.

In manufacturing, time is money. With the implementation of the industrial Internet of Things strategy, from individual sensors to available analysis and automation, manufacturers can make better business decisions around overall efficiency and cost. The industrial Internet of Things strategy also has great potential for sustainable and green production practices and supply chain traceability.

With the application of "circular economy" in enterprises, all elements from products to packaging should be reused and recycled throughout the value chain. The implementation of the Industrial Internet of Things (IOT) is crucial to its tracking results.

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