IIOT and Real Time Data Analytics - Maximizing the Impact on Safety and Productivity

Ryan Daher, Nesma Aldash
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Abstract

With the global push towards Industry 4.0, a number of leading companies and organizations have invested heavily in Industrial Internet of Things (IIOT's) and acquired a massive amount of data. But data without proper analysis that converts it into actionable insights is just more information. With the advancement of Data analytics, machine learning, artificial intelligence, numerous methods can be used to better extract value out of the amassed data from various IIOTs and leverage the analysis to better make decisions impacting efficiency, productivity, optimization and safety. This paper focuses on two case studies- one from upstream and one from downstream using RTLS (Real Time Location Services). Two types of challenges were present: the first one being the identification of the location of all personnel on site in case of emergency and ensuring that all have mustered in a timely fashion hence reducing the time to muster and lessening the risks of Leaving someone behind. The second challenge being the identification of personnel and various contractors, the time they entered in productive or nonproductive areas and time it took to complete various tasks within their crafts while on the job hence accounting for efficiency, productivity and cost reduction. In both case studies, advanced analytics were used, and data collection issues were encountered highlighting the need for further and seamless integration between data, analytics and intelligence is needed. Achievements from both cases were visible increase in productivity and efficiency along with the heightened safety awareness hence lowering the overall risk and liability of the operation. Novel/Additive Information: The results presented from both studies have highlighted other potential applications of the IIOT and its related analytics. Pertinent to COVID-19, new application of such approach was tested in contact tracing identifying workers who could have tested positive and tracing back to personnel that have been in close proximity and contact therefore reducing the spread of COVID. Other application of the IIOT and its related analytics has also been tested in crane, forklift and heavy machinery proximity alert reducing the risk of accidents.
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工业物联网和实时数据分析——对安全和生产力的影响最大化
随着全球对工业4.0的推动,许多领先的公司和组织都在工业物联网(IIOT)上投入了大量资金,并获得了大量数据。但是,如果没有适当的分析,将数据转化为可操作的见解,那么数据只是更多的信息。随着数据分析、机器学习、人工智能的发展,许多方法可以用来更好地从各种工业物联网积累的数据中提取价值,并利用分析来更好地做出影响效率、生产力、优化和安全的决策。本文着重于两个案例研究——一个来自上游,一个来自下游,使用RTLS(实时定位服务)。目前存在两类挑战:第一类挑战是在紧急情况下确定现场所有人员的位置,并确保所有人员及时集合,从而减少集合时间并减少落下某人的风险。第二个挑战是确定人员和各种承包商,他们进入生产性或非生产性领域的时间,以及在工作期间完成其工艺内的各种任务所需的时间,从而考虑到效率、生产力和降低成本。在这两个案例中,我们都使用了高级分析,并且遇到了数据收集问题,这突出了数据、分析和智能之间进一步无缝集成的需求。这两个案例的成果都是生产力和效率的显著提高,同时提高了安全意识,从而降低了作业的总体风险和责任。新/附加信息:两项研究的结果都强调了工业物联网及其相关分析的其他潜在应用。针对COVID-19,测试了这种方法在接触者追踪方面的新应用,确定可能检测呈阳性的工作人员,并追踪到近距离接触和接触的人员,从而减少了COVID-19的传播。工业物联网及其相关分析的其他应用也已在起重机,叉车和重型机械接近警报中进行了测试,以降低事故风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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