Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

Hoffmann, Martin W.; Wildermuth, S.; Gitzel, Ralf; Boyaci, Aydin; Gebhardt, Jörg; Kaul, Holger; Amihai, Ido; Forg, B.; Suriyah, Michael; Stich, Volker; Hicking, Jan; Bremer, Martin; Kaminski, Lars; Beverungen, Daniel; Heiden, Philipp zur; Tornede, Tanja

doi:10.3390/s20072099

Cited by 63 publications

(41 citation statements)

References 91 publications

(99 reference statements)

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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%

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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%

“…Some other authors [64][65][66][67]81,82,[84][85][86][87][88]90,116,117] studied RF technique. In the last years, use of SVM technique has received attention from authors [14,21,48,56,66,79,[93][94][95][96][97][98][99][100]102,107,111,115,121,124]. Some authors [21,56,82,112,120,124] have given attention to GBM 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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“…Ref. [83] reviews the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear and presents an approach to develop a predictive maintenance system based on novel sensors and machine learning. The interest of operators of electrical equipment and machinery in condition monitoring and predictive maintenance is due to the avoidance of catastrophic failures, the reduction of operational cost, and the lifetime extension of the equipment.…”

Section: Recyclementioning

confidence: 99%

Circular Economy Strategies for Equipment Lifetime Extension: A Systematic Review

et al. 2021

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Even if the economy nowadays is still locked into a linear model of production, tighter environmental standards, resource scarcity and changing consumer expectations are forcing organizations to find alternatives to lighten their impacts. The concept of Circular Economy (CE) is to an increasing extent treated as a solution to this series of challenges. That said, the multitude of approaches and definitions around CE and Life Cycle Extension Strategies (LCES) makes it difficult to provide (Small and Medium Enterprise) SMEs with a consistent understanding of the topic. This paper aims at bridging this gap by providing a systematic literature review of the most prominent papers related to the CE and lifetime extension, with a particular focus on the equipment and machinery sector. A taxonomy was used to define and cluster a subset of selected papers to build a homogeneous approach for understanding the multiple strategies used in the industry, and the standards in maintenance and remanufacturing strategies. As a final research step, we also propose a Strategy Characterization Framework (SCF) to build the ground for the selection of the best strategy to be applied for production equipment life cycle extension on several industrial use cases.

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“…In a current research project, we developed a CPS for medium-voltage switchgear ( Figure 2 ). The purpose of the CPS is continuous condition monitoring of the asset [ 17 ] to identify potential failures early and thus increase the availability of the switchgear. In order to do so, infrared camera sensors are built into the electrical switchgear.…”

Section: Examples For Industrial Use Casesmentioning

confidence: 99%

“…Figure 2 summarizes the medium-voltage switchgear CPS. Details of the components and corresponding research challenges are discussed in [ 17 ].…”

Section: Examples For Industrial Use Casesmentioning

confidence: 99%

Developing Industrial CPS: A Multi-Disciplinary Challenge

Hoffmann¹,

Malakuti²,

Grüner³

et al. 2021

Sensors

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Industrial Cyber–Physical System (CPS) is an emerging approach towards value creation in modern industrial production. The development and implementation of industrial CPS in real-life production are rewarding yet challenging. This paper aims to present a concept to develop, commercialize, operate, and maintain industrial CPS which can motivate the advance of the research and the industrial practice of industrial CPS in the future. We start with defining our understanding of an industrial CPS, specifying the components and key technological aspects of the industrial CPS, as well as explaining the alignment with existing work such as Industrie 4.0 concepts, followed by several use cases of industrial CPS in practice. The roles of each component and key technological aspect are described and the differences between traditional industrial systems and industrial CPS are elaborated. The multidisciplinary nature of industrial CPS leads to challenges when developing such systems, and we present a detailed description of several major sub-challenges that are key to the long-term sustainability of industrial CPS design. Since the research of industrial CPS is still emerging, we also discuss existing approaches and novel solutions to overcome these sub-challenges. These insights will help researchers and industrial practitioners to develop and commercialize industrial CPS.

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Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

Cited by 63 publications

References 91 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

Circular Economy Strategies for Equipment Lifetime Extension: A Systematic Review

Developing Industrial CPS: A Multi-Disciplinary Challenge

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