Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance

Fernandes, Sofia; Antunes, Mário; Santiago, Ana Rita; Barraca, João Paulo; Gomes, Diogo; Aguiar, Rui L.

doi:10.3390/info11040208

Cited by 32 publications

(12 citation statements)

References 16 publications

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“…They have been successfully applied in several field of PdM applications. Some authors [10,30,[46][47][48][75][76][77]107,114,[118][119][120][121] focused on ANN ML algorithms. Some other authors [64][65][66][67]81,82,[84][85][86][87][88]90,116,117] studied RF technique.…”

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confidence: 99%

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

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Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

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confidence: 99%

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

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“…Miniaturisation of current sensors and nanotechnology [157] provides higher flexibility of maintenance systems. Fernandes et al [158] emphasise the role of data visualisation, data mining, and data storage. These models have a standard data flow process which is a part of the possible prediction preparation.…”

Section: Sensor-based Smart Factory Discussionmentioning

confidence: 99%

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

Pech

Vrchota

Bednář

2021

Sensors

232

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With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems’ decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper’s main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.

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“…Finally, as previously stated (in Section 2 ), the Data Mining component is a key component in the platform. However, in previous work, we already detailed the most important details regarding their components [ 6 ]. The inner workings of this component is out of the scope of this work.…”

Section: Scot Architecture and Implementationmentioning

confidence: 99%

“…As SCoTv1 was used in the deployment of IoT scenarios, it became evident that the European Telecommunications Standards Institute (ETSI) IoT standards were too complex and limited for the realisation of larger scenarios, which became commonplace in the intervening years. This led to several evolutions in our platform [ 5 , 6 ]. The first evolution was replacing the complex ETSI IoT standards with the agile Eclipse IoT.…”

Section: Introductionmentioning

confidence: 99%

Building an IoT Platform Based on Service Containerisation

Antunes

Santiago

Manso

et al. 2021

Sensors

Self Cite

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IoT platforms have become quite complex from a technical viewpoint, becoming the cornerstone for information sharing, storing, and indexing given the unprecedented scale of smart services being available by massive deployments of a large set of data-enabled devices. These platforms rely on structured formats that exploit standard technologies to deal with the gathered data, thus creating the need for carefully designed customised systems that can handle thousands of heterogeneous data sensors/actuators, multiple processing frameworks, and storage solutions. We present the SCoT2.0 platform, a generic-purpose IoT Platform that can acquire, process, and visualise data using methods adequate for both real-time processing and long-term Machine Learning (ML)-based analysis. Our goal is to develop a large-scale system that can be applied to multiple real-world scenarios and is potentially deployable on private clouds for multiple verticals. Our approach relies on extensive service containerisation, and we present the different design choices, technical challenges, and solutions found while building our own IoT platform. We validate this platform supporting two very distinct IoT projects (750 physical devices), and we analyse scaling issues within the platform components.

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Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance

Cited by 32 publications

References 16 publications

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

Building an IoT Platform Based on Service Containerisation

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