Sensor data analysis for equipment monitoring

Garcia, Ana Cristina Bicharra; Bentes, Cristiana; Melo, Rafael H. C. de; Zadrozny, Bianca; Penna, T. J. P.

doi:10.1007/s10115-010-0365-1

Cited by 17 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can enable improved stability in plant operations as well as a reduction in the high alarm rates that can hamper the operations. Garcia et al (2011) proposes such a monitoring model for the case of an oil processing plant. In addition to improvement in plant utilization (efficiency) and availability (less downtime), benefits will appear in terms of a reduction in operating costs as well as the ability to make real-time decisions.…”

Section: Operations Managementmentioning

confidence: 99%

Big Data and the Internet of Things

Shah¹

2015

Studies in Big Data

View full text Add to dashboard Cite

Abstract. Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things -IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.

show abstract

Section: Operations Managementmentioning

confidence: 99%

Big Data and the Internet of Things

Shah¹

2015

Studies in Big Data

View full text Add to dashboard Cite

show abstract

“…Recently, routing in Wireless Sensor Networks (WSNs) has been recognized as an important research area and much work has been carried out. As a result, a great number of studies have discussed the application issues of evolutionary computation techniques [26], clustering [27], and data mining [28] in this kind of networks. Some of them will be referenced along this paper.…”

Section: Related Workmentioning

confidence: 99%

“…In this way, the genetic algorithm cycle is activated by a source node, only when the search phase of the underlying QoS routing protocol is needed. It is important that GATAS uses the GA in an online manner like in real-time systems [12, 28]. The reason is that the path repair function could be activated by the neighbor discovery process and it is crucial that the repair function is not activated during the search phase as the routing protocol will converge slowly.…”

Section: Description Of the Proposalmentioning

confidence: 99%

A Genetic QoS-Aware Routing Protocol for the Smart Electricity Networks

Zaballos

Vernet

Selga

2013

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

This paper presents a QoS-aware routing protocol suitable for distribution of smart electricity grids based on heterogeneous machine to machine communications. The distribution Smart Grid needs high performance communication networks capable of handling QoS, an issue that is addressed by the present paper. The proposed algorithm is a merger between a genetic algorithm (GA) and Ticket-Based Routing (TBR), which is an on-demand routing protocol for ad hoc networks that provide quality of service. A suitable parameterization of the GA parameters is needed in order to use this protocol in the coming Smart Grid networks. The resulting routing protocol, named genetic algorithm with TBR algorithm for Smart Grids (GATAS), is an adapted intelligent evolution of the TBR. The performance of TBR has been improved by reducing the overhead of routing packets in the network and by minimizing the communication latency due to its on-demand behavior. Experimental evidence indicates that the likelihood of finding the optimum route using multiobjective dynamic metrics increases when the genetic algorithm is applied. In this paper, the main simulation results on the parameterization carried out are discussed, and the proposed attributes of the GA are described.

show abstract

“…Extraction of information from raw data generated by the sensors is a complex task, which is why solutions such as SDAEM (Sensor Data Analysis for Equipment Monitoring) are being developed -which has three main goals: data mining, interpretation of correlated data from sensors and adjustment of setpoints. This model has been used to monitor oil refining facilities in Brazil [Garcia et al, 2011].…”

Section: Introductionmentioning

confidence: 99%

Connecting a Virtual Production and a PLC

Kubr¹,

Novikov²,

Hořejší³

et al. 2021

MM SJ

View full text Add to dashboard Cite

This article deals with the implementation of virtual manuals for mass production workplaces in the automotive industry. The aim is to conduct a pilot study as a source for future research and the possibility of connecting virtual instruction manuals to production systems. A system was designed that uses its own software solution for creating instructions, which was connected to a PLC (Programmable Logic Controller) in a manufacturing company. The whole system is connected to the company’s production cycle. This solution has been validated. Three months after the introduction of the innovation, it can be stated that during the application of the system, the scrap rate was reduced from an initial 12 pieces to a final state of 3 pieces, and the length of time for training newly arrived operators was shortened. The analysis focused mainly on the evaluation of scrap, the aspect of time in this case did not need to be stated due to the high workplace cycle. The proposed solution thus contributed to the streamlining and modernization of production and will be an important input for the creation of a general IoT methodology.

show abstract

Sensor data analysis for equipment monitoring

Cited by 17 publications

References 27 publications

Big Data and the Internet of Things

Big Data and the Internet of Things

A Genetic QoS-Aware Routing Protocol for the Smart Electricity Networks

Connecting a Virtual Production and a PLC

Contact Info

Product

Resources

About