Refine
Document type
Language
- English (6)
Is part of the Bibliography
- Yes (6)
Keywords
- Cloud (1)
- Compliance (1)
- Data acquisition (1)
- Deep learning (1)
- Distributed monitoring system (1)
- GRU (1)
- IoT-cloud integration (1)
- LSTM (1)
- Machine learning (1)
- Mechanical characteristics (1)
Time series Classification is a vital task across various domains such as finance, healthcare, and environmental science. Recurrent Neural Networks in combination with Convolutional Neural Networks have emerged as powerful tools for Time series classification due to their ability to capture temporal dependencies. Long Short-Term Memory and Gated Recurrent Unit networks, along with their bidirectional variants have been widely employed for Time series tasks, but a comparison of these architectures under different hyperparameter configurations has not yet been analysed in detail. This paper fills the gap and provides a comprehensive comparison of these four architectures. We conduct experiments on two datasets representing a healthcare and industrial domain to evaluate there performance in terms of Classification accuracy, training time, and model complexity. The results of our experiments provide insights into the strengths and weaknesses of each architecture, aiding practitioners in selecting the most suitable model for their specific tasks. The superiority of the GRU architecture was demonstrated both in terms of learning speed and accuracy.
ARTHUR – Distributed Measuring System for Synchronous Data Acquisition from Different Data Sources
(2023)
In industrial manufacturing lines, different machines are well orchestrated and applied for their well-defined purpose. As each of these machines must be monitored and maintained in the first place, there are scenarios in which a Data Acquisition system brings enormous benefits. Since the cost of such professional systems is often not appropriate or feasible for research projects or prototyping, a proof of concept is often achieved by applying end-user hardware. In this work, a distributed measurement system for supporting the collection of data is described with respect to AI-based projects for research and teaching. ARTHUR (meAsuRing sysTem witH distribUted sensoRs) is arbitrarily expandable and has so far been used in the field of data acquisition on machine tools. Typical measured values are Accoustic Emission values, force plates X-Y-Z force values, simple PLC switching signals, OPC-UA machine parameters, etc., which were recorded by a wide variety of sensors. The overall ATHUR system is based on Raspberry Pis and consists of a master node, multiple independent measurement worker nodes, a streaming system realized with Redis, as well as a gateway that stores the data in the cloud. The major objectives of the ARTHUR system are scalability and the support for low-cost measuring components while solely applying open-source software. The work on hand discusses the advantages and disadvantages regarding the hard- and software of this TCP/IP-based system.
On the way to the smart factory, the manufacturing companies investigate the potential of Machine Learning approaches like visual quality inspection, process optimisation, maintenance prediction and more. In order to be able to assess the influence of Machine Learning based systems on business-relevant key figures, many companies go down the path of test before invest. This paper describes a novel and inexpensive distributed Data Acquisition System, ARTHUR (dAta collectoR sysTem witH distribUted sensoRs), to enable the collection of data for AI-based projects for research, education and the industry. ARTHUR is arbitrarily expandable and has so far been used in the field of data acquisition on machine tools. Typical measured values are Acoustic Emission values, force plate X-Y-Z force values, simple SPS signals, OPC-UA machine parameters, etc. which were recorded by a wide variety of sensors. The ARTHUR system consists of a master node, multiple measurement worker nodes, a local streaming system and a gateway that stores the data to the cloud. The authors describe the hardware and software of this system and discuss its advantages and disadvantages.