Realization of Data Analytics Projects in Manufacturing Using a Microservice-Based Approach

Höhr, Sara; Tasci, Timur; Verl, Alexander

doi:10.1109/icmech.2019.8722926

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…As modularity enables composability, some [44,48,57] have devised systems that combine services provided by lightweight components to implement tailored industrial control functions. A few [15,30] have proposed modular architectures to support data management facilities. Microservices Flexibility is extensively exploited by quite a number of authors who have proposed control loop systems [9,17,26,44], virtualized networking systems [51,59] and DevOps approaches to the management of production assets [19,37,52,65] respectively.…”

Section: Final Discussionmentioning

confidence: 99%

“…It is no surprise that research communities push to deploy microservices in officefloor departments (which is historically a fertile ground for information technologies) to implement typical tasks of Production Control [10, 19, 35-37, 43, 52, 58, 65] or, more generally, Data Processing [2,18,30,63,66]. In here, according to researchers, the most appreciated quality of microservices is the strong support they provide to develop, maintain, update, deploy and scale industrial software solutions.…”

Section: Final Discussionmentioning

confidence: 99%

“…In [30], authors argue that the success of data analytics solutions in manufacturing environments depends on how tightly IT services are integrated into the manufacturing system, as well as on the ability of machine operators of using such services in collaboration with data scientists. The foundation of their work is CRISP-DM [53], a standard model that describes the necessary activities for the implementation of data analytics as a cross-industry standard process for data mining.…”

Section: Modularity: Cpss As An Ecosystem Of Composable Modulesmentioning

confidence: 99%

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A Survey on the Use of Lightweight Virtualization in I4.0 Manufacturing Environments

Modica

2023

J Netw Syst Manage

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Over the past decade, in the industrial sector we have witnessed the rise of a revolutionary movement, known as Industry 4.0, that promotes the digital transformation as the key to increase the competitiveness of manufacturing factories. Among the many technologies recognized as “drivers” of such revolutionary transition, microservices stand out as a software development paradigm capable of bringing several benefits to the manufacturing process. Whilst the literature offers many examples of initiatives exploiting microservices in digitally-advanced sectors (e.g., finance, telecommunication, retailing), its potential in the industrial manufacturing is yet to be fully unleashed. We conducted an extensive literature survey in the twofold aim of bringing to the reader’s attention the many benefits that the microservices paradigm may offer in industrial manufacturing settings, and drawing a picture of how light virtualization techniques are actually being exploited to achieve Industry 4.0 digitization goals. In this paper, we propose a structured analysis of the collected literature proposals which combines the benefits sought by authors when approaching to the microservices techniques and the specific scope of application of proposals. We conclude the paper highlighting the research aspects that have not been sufficiently explored in the literature and that would deserve further attention in the near future.

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Section: Final Discussionmentioning

confidence: 99%

Section: Final Discussionmentioning

confidence: 99%

Section: Modularity: Cpss As An Ecosystem Of Composable Modulesmentioning

confidence: 99%

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A Survey on the Use of Lightweight Virtualization in I4.0 Manufacturing Environments

Modica

2023

J Netw Syst Manage

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“…By evaluating sensor data with machine and deep learning methods, models can be trained which can be used to determine the bearing state. The implementation of machine-learning based solutions for manufacturing poses several challenges, especially when deploying the models to the production environment (Höhr, Tasci & Verl (2019), Lade, Ghosh & Srinivasan (2017)). It becomes particularly difficult when the data distribution differs in model application compared to model training, which is often the case in manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Feature Based Bearing Fault Detection With Phase Current Sensor Signals Under Different Operating Conditions

Wagner¹,

Sommer²

2021

PHME_CONF

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To avoid sudden motor failures, which might lead to unexpected downtimes of entire production plants, predictive machinery maintenance is of great importance. Since bearingsare the only connection between rotor and stator and for thisreason, among others, more than 40 percent of all motor failures are reasoned by bearing defects, bearing defect detectionis of particular importance for predictive maintenance of motor failures. In the past, mainly external vibration sensorswere used for fault detection. However, in recent years, therehas been increasing research into using phase currents forbearing damage detection, which has the advantage of savingadditional sensors and thus costs. The practical use of phasecurrents is justified by the fact that bearing damage is accompanied by eccentricities between the rotor and stator, whichaffect the magnetic flux and thus also the phase currents. Forthese reasons, we focus on bearing damage detection usingphase currents.We show in the paper that variations of the rotational shaftfrequency, torque loads and radial forces applied to the bearings have a strong influence of the applicability of a defectdetection model trained with phase current data. This means,a defect detection model, which was trained with labeled datacollected under a fixed operating condition is not able to makereliable classifications if the operating conditions differ fromthe training conditions. This is due to discrepancies in feature distributions between the different operating conditions.However, it is of utmost importance for practical applicability that a fault detection model is able to make reliable classifications under a variety of previously unknown operating conditions.To solve this problem, we present a deep learning approachfor diminishing the influence of different operating conditionsby utilizing unsupervised domain adaptation. As input datafor the neural network, we propose to extract hand-craftedfeatures, both from time- and frequency domain, from thephase current signals. This greatly reduces training time andresource utilization compared to training a neural network asfeature extractor on the raw time series sensor signals. Thebasic idea is then to train a neural network with labeled datafrom the source domain, i.e. data of one defined operatingcondition, and at the same time consider unlabeled data ofthe new, previously unknown, target operating condition inorder to achieve higher classification accuracies for this operating condition as well. Thus, labeled data is only necessaryfrom the source domain to train the Softmax classificationlayer of the deep neural network. As the features computedfrom the differing working conditions are the same, domaindiscrepancy can be reduced in an unsupervised manner byweight sharing and formulating a specific loss term (we useMulti-Layer Maximum Mean Discrepancy) that captures thediscrepancy between the different operating conditions. Byapplying this additional loss term to the backpropagation process, discrepancies between the feature means of source- andtarget domain are reduced iteratively.The effectiveness of the proposed method is evaluated with abenchmark dataset from the University of Paderborn, which iswell known in the bearing damage community. The proposedfeature based deep unsupervised domain adaptation methodsignificantly increases classification accuracies for all transfer scenarios. With the best of our knowledge, it is the firsttime one deals with phase current based domain adaptationon statistical fault-features.

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Microservice transition and its granularity problem: A systematic mapping study

Hassan¹,

Bahsoon²,

Kazman

2020

Softw Pract Exp

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Microservices have gained wide recognition and acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. The transition to microservices has been highly motivated by the need for better alignment of technical design decisions with improving value potentials of architectures. Despite microservices' popularity, research still lacks disciplined understanding of transition and consensus on the principles and activities underlying that transition. In this paper, we report on a systematic mapping study that consolidates various views, approaches and activities that commonly assist in the transition to microservices. The study aims to provide a better understanding of the transition; it also contributes a working definition of the transition and technical activities underlying it. We term the transition and technical activities leading to microservice architectures as microservitization. We then shed light on a fundamental problem of microservitization: microservice granularity and reasoning about its adaptation as first-class entities. This study reviews state-of-the-art and-practice related to reasoning about microservice granularity; it reviews modeling approaches, aspects considered, guidelines and processes used to reason about microservice granularity. This study identifies opportunities for future research and development related to reasoning about microservice granularity.

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Realization of Data Analytics Projects in Manufacturing Using a Microservice-Based Approach

Cited by 4 publications

References 14 publications

A Survey on the Use of Lightweight Virtualization in I4.0 Manufacturing Environments

A Survey on the Use of Lightweight Virtualization in I4.0 Manufacturing Environments

Feature Based Bearing Fault Detection With Phase Current Sensor Signals Under Different Operating Conditions

Microservice transition and its granularity problem: A systematic mapping study

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