Condition Monitoring in the Cloud

Uhlmann, Eckart; Laghmouchi, Abdelhakim; Hohwieler, Eckhard; Geisert, Claudio

doi:10.1016/j.procir.2015.08.075

Cited by 21 publications

(6 citation statements)

References 5 publications

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“…Figur packet coefficients computed using MATLAB codes [22]. An ascending trend is observed for wavelet packet node (3,7) which is not found in the reaming packet nodes. Hence, in this work statistical features are packet node.…”

Section: An Experimental Comparison Of Random Forest and Support Vectmentioning

confidence: 99%

“…The bagging process randomly selects feature samples from the training set and 1507 editor@tjprc.org WPT and analysed up to 3 level e 6 shows the energy values of the odd wavelet packet coefficients computed using MATLAB codes [22]. An ascending trend is observed for wavelet packet node (3,7) which is not found in the reaming packet nodes. Hence, in this work statistical features are extracted from this wavelet packet node.…”

Section: Scopus Indexed Journalmentioning

confidence: 99%

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An Experimental Comparison of Random Forest and Support Vector Machine for Bearing Fault Diagnosis, A Micro Electro Mechanical Sensor Approach

Kumar¹,

Tjprc²

2019

IJMPERD

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A sudden bearing failure leads to fatal accidents and results in loss of human life and increases the down time of the machine. Neural Network and Support Vector Machines (SVM) are widely used in rotating machinery fault diagnosis, while Random Forest (RF) based on the ensemble learning method, is relatively unknown in this field. Currently, use of Micro Electro Mechanical Sensors (MEMS) for machinery fault diagnosis is receiving more attention as it is low cost, compact and portable. In this paper, vibration signals are collected from the Rolling Element Bearing (REB) using the MEMS sensor. Statistical features have been extracted from the wavelet packet coefficients of the vibration signals and used as input feature for the classification purpose. A framework for the comparison of RF and SVM is presented to identify the best classifier for bearing fault diagnosis. RF emerged as the best classifier based on the classification accuracy especially with a small training set leading to a promising tool for bearing fault diagnosis.

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Section: An Experimental Comparison Of Random Forest and Support Vectmentioning

confidence: 99%

Section: Scopus Indexed Journalmentioning

confidence: 99%

An Experimental Comparison of Random Forest and Support Vector Machine for Bearing Fault Diagnosis, A Micro Electro Mechanical Sensor Approach

Kumar¹,

Tjprc²

2019

IJMPERD

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“…At the detailed design stage, the list of predicted maintenance tasks is deliberated [161]. In the context of Maintenance, Repair and Overhaul (MRO), the Fraunhofer Innovation Cluster develops technologies which seek to optimize such MRO processes which include Condition Monitoring and Diagnosis, MRO Planning and Digital Assistance, Cleaning, as well as actual Repair and Overhaul Technologies [162] [54]. Over long periods of operation time, products may change and the MRO service providers (especially non-OEM's) need to keep up with the lifecycle-based documentation.…”

Section: Servicesmentioning

confidence: 99%

State of the art in Through-life Engineering Services

Redding

Tjahjono

2018

Computers in Industry

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“…Uhlmann et al describe a solution for machine condition monitoring; they developed a wireless sensor network connecting computers, raspberries Pi 2, to sensors to acquire data and process it, after that sending the information to the Cloud. They do not describe the software development framework in details and the software infrastructure used.…”

Section: Related Workmentioning

confidence: 99%

Application of the Fog computing paradigm to Smart Factories and cyber‐physical systems

Brito

Hoque

Steinke

et al. 2017

Trans Emerging Tel Tech

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Since the invention of the steam engine in the 18th century, innovation drove the development of industrial processes. The next industrial revolution will form an ecosystem of over 20 billion connected devices with unforeseeable influence to the gross domestic product by 2020, and connected assets will generate about 44ZB of data, which pose interesting challenges related to privacy, connectivity, scalability, and others. A current line of action that leads to this direction is the development of cyber‐physical systems; considered as the coupling of physical processes and the digital world, its influence in the next industrial revolution is essential. In this work, we discuss its implementation, taking the Fog computing paradigm into consideration. As a starting point, we are extending a standard‐compliant machine‐to‐machine communication architecture to support container‐based orchestration mechanisms to enable cyber‐physical systems to be programmable, autonomous, and to communicate peer‐to‐peer. As the primary field of application, we are considering Industrial Internet domains in general and Smart Factory environments in particular. In this paper, we present an architecture for Fog Nodes, as well a more in‐depth discussion on the orchestration system and programmable characteristics of the Fog Node. On the basis of a simulation model, we show the advantages of having a programmable Fog Node supported by an orchestration system. Finally, we open a discussion about our solution and its application in the field of Smart Factories.

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Condition Monitoring in the Cloud

Cited by 21 publications

References 5 publications

An Experimental Comparison of Random Forest and Support Vector Machine for Bearing Fault Diagnosis, A Micro Electro Mechanical Sensor Approach

An Experimental Comparison of Random Forest and Support Vector Machine for Bearing Fault Diagnosis, A Micro Electro Mechanical Sensor Approach

State of the art in Through-life Engineering Services

Application of the Fog computing paradigm to Smart Factories and cyber‐physical systems

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