Real-Time Asset Tracking for Smart Manufacturing

Krishnan, S. P. T.; Santos, Rochelle Xenia Mendoza

doi:10.1007/978-3-030-67270-6_2

Cited by 7 publications

(5 citation statements)

References 51 publications

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“…The operational data collected by using Internet of Things (IoT) enabled sensors and tracking data from IPS provides useful insights for resource utilisation, monitoring activities and flow of WIP, factory layout optimisation, etc. Such data can also help to identify the bottlenecks, developed informed planning and scheduling, and hence an overall improvement in strategic decision making and productivity [29].…”

Section: Discussionmentioning

confidence: 99%

Generating Insight from Indoor Positioning Data in Manufacturing Assembly Line

Rahman

Rana

Baumgartner

2023

Preprint

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Indoor positioning systems provide a platform to track multiple objects concurrently in an indoor environment. The primary purpose of these systems is to locate objects. However, the tracks generated by moving objects leave characteristic footprints and can be used to infer valuable information for informed decision making. In this paper, we demonstrate how insightful information can be inferred from indoor tracking data of workers in a manufacturing assembly line setup. We utilise two indoor tracking data sets: (i) a real world (and publicly) available data set which contains the tracking data of multiple workers working together on a tricycle assembly line, and (ii) a synthetic data set of a computer assembly line. We aim to develop a set of data analytics methods to infer several Key Performance Indicators (KPIs) or metrices from the tracking data of the workers that include: resource utilization, worker interaction, deviation from ideal workflow and state of the assembly line. While the methods are developed for analysing data from tricycle and synthetic computer assembly line, they can be generalised to any manufacturing assembly line.

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Section: Discussionmentioning

confidence: 99%

Generating Insight from Indoor Positioning Data in Manufacturing Assembly Line

Rahman

Rana

Baumgartner

2023

Preprint

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“…RTLS for asset tracking provides useful data inputs for various purposes such as monitoring of personnel on the factory floor, equipment utilization, inventory levels and flow of work-in-progress (WIP). This in turn leads to better identification of bottlenecks, optimization of factory layouts, more informed planning and scheduling, and hence an overall improvement in productivity [23]. Furthermore, a similar system is used for value stream mapping.…”

Section: State Of the Artmentioning

confidence: 99%

Real-Time Location System (RTLS) Based on the Bluetooth Technology for Internal Logistics

Lorenc

Szarata²,

Czuba³

2023

Sustainability

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The problem of object localization in indoor environments is very important in order to make a company effective and to detect disruption in the logistics system in real-time. Present research investigates how the IoT (Internet of Things) location system based on Bluetooth can be implemented for this solution. The location based on the Bluetooth is hard to predict. Radio wave interference in this frequency is affected by other devices, steel, vessels containing water, and more. However, proper data processing and signal stabilization can increase the accuracy of the location. To be sure that the location system based on the BT (Bluetooth) can be implemented for real cases, an analysis of signal strength amplitude and disruption was made. The paper presents R&D (Research and Development) works with a practical test in real cases. The signal strength fluctuation for the receiver is between 7 and 10 dBm for ESP32 device and between 13 and 14 dBm for Raspberry. For commercial implementation the number of devices scanned in the time window is also important. For Raspberry, the optimal time window is 5 s; in this time six transmitters can be detected. ESP32 has a problem with detecting devices in a short time, as just two transmitters can be detected in 4–8 s time window. Localisation precision depends on the distance between transmitter and receiver, and the angle from the axis of the directional antenna. For the distance of 10 m the measurement error is 1.2–6.1 m, whilst for the distance of 40 m the measurement error is 4.9 to 24.6 m. Using a Kalman filter can reduce the localization error to 1.5 m.

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“…Among all the recently emerging technologies, real-time location systems (RTLSs) are gaining relevance in industrial and home use. RTLSs can enable the autonomous operation of vehicles and robots to improve the productivity and flexibility of production networks [1]- [3] and measure the trajectories of assets for data analytics [4]. Apart from the productivity advantages, RTLSs can also be used to enhance the safety of people at work [5], [6] or at home with ambient assisted living (AAL) applications [7].…”

Section: Introductionmentioning

confidence: 99%

Novel Classification Method to Predict the Accuracy of UWB Ranging Estimates

Arsuaga,

Ochoa-De-Eribe-Landaberea,

Zamora-Cadenas

et al. 2024

IEEE Access

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Real time location systems (RTLSs) are becoming more relevant in a more data driven economy and society due to their wide range of application cases. When the location of an object needs to be tracked with high accuracy, ultra wideband (UWB) technology is usually the best option. Nevertheless, UWB ranging estimates are not completely immune to some sources of error such as non line of sight (NLOS) or multipath conditions. Thus, this paper proposes a real-time classification model based on machine learning (ML) to predict if received ranging estimates are in line of sight (LOS) or NLOS conditions and discard those in NLOS. However, it is also shown that classifying measurements as LOS or NLOS does not guarantee detecting inaccurate ranging estimates, since LOS measurements can also yield large errors. As an example, the ranging root mean square error (RMSE) of the data labelled as LOS in a UWB based localization system database in the literature is of 0,714 m, significantly higher than the theoretical accuracy of a UWB system. Thus, a novel ML-based classification model is proposed to predict the magnitude of the ranging error. After applying the proposed classification model in the same data, the ranging RMSE of those ranging samples classified as most accurate is of only 0.183 m, significantly lower than the best RMSE we can obtain on the classical LOS/NLOS classification approach.INDEX TERMS DWM1000, machine learning, random forest, ranging errors, real time location system, ultra wideband.

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Real-Time Asset Tracking for Smart Manufacturing

Cited by 7 publications

References 51 publications

Generating Insight from Indoor Positioning Data in Manufacturing Assembly Line

Generating Insight from Indoor Positioning Data in Manufacturing Assembly Line

Real-Time Location System (RTLS) Based on the Bluetooth Technology for Internal Logistics

Novel Classification Method to Predict the Accuracy of UWB Ranging Estimates

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