The Autonomous Industrial Plant -Future of Process Engineering, Operations and Maintenance

Gamer, Thomas; Hoernicke, Mario; Kloepper, Benjamin; Bauer, Reinhard; Isaksson, Alf

doi:10.1016/j.ifacol.2019.06.104

Cited by 19 publications

(11 citation statements)

References 4 publications

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“…It is interesting to note that some of the presented requirements correspond with identified requirements on Industrial AI correspond to research fields recently identified for Scientific Machine Learning [33]. We foresee future developments of Industrial AI in the areas of i) production system autonomy [18], ii) product life cycle management [15], especially because AI life cycles will become more complex to manage [23], iii) virtual industrial assistants [34], iv) explainable AI [35] and v) the seamless fusion of different data pools in production sites.…”

Section: Discussionmentioning

confidence: 55%

“…A parallel operation with conventional automation technologies is desirable in terms of confidence generation. The power of decision should be gradually transferred from the conventional automation technology to the industrial AI, similar to the gradual increase of autonomy of the system [18]. It is to be noted though that for safety and reliability reasons, some critical functionality may remain in a conventional automation system.…”

Section: )mentioning

confidence: 99%

“…During the introduction phase of Industrial AI in production systems, human should remain in control of all decisions, i.e. the Industrial AI may only serve as expert system or decision support system, corresponding to lower levels of autonomy [18]. Only by time, the autonomy of the Industrial AI may increase.…”

Section: )mentioning

confidence: 99%

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Proposal for requirements on industrial AI solutions

Hoffmann¹,

Drath

Ganz

2020

Technologien Für Die Intelligente Automation

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The rise of artificial intelligence (AI) promises productivity gains in industrial practice. While IT technology offers a variety of technological advances, plant owners strive for stability and robustness of the production process. To overcome this tension field, we propose a set of 16 requirements for the development of industrial AI solutions to foster i) the adaptation process, ii) support the solution engineering and iii) ease the embedding into the existing system landscape while respecting iv) safety aspects to build up v) trust into industrial AI solutions. The proposed requirements can guide industrial stakeholders to focus on the right solution approach for specific production challenges and support them in voicing their own needs towards novel AI solutions. This will help AI developers to speed up time-to-market as well as to increase market acceptance of industrial AI solutions. Overall, specifying requirements on industrial AI will foster the acceptance and utilization rates of AI solutions in industrial practice.

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Section: Discussionmentioning

confidence: 55%

Section: )mentioning

confidence: 99%

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Proposal for requirements on industrial AI solutions

Hoffmann¹,

Drath

Ganz

2020

Technologien Für Die Intelligente Automation

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“…It is enhancing industrial systems from automation to production optimization to supply chain management [65,66]. Furthermore, industrial players are building on advancing autonomy in industrial systems through the application of AI methods [66][67][68].…”

Section: Technology: Machine Learning For Predictive Maintenancementioning

confidence: 99%

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

Hoffmann¹,

Wildermuth²,

Gitzel³

et al. 2020

Sensors

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The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.

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“…Intelligent manufacturing is mainly supported by IAI technologies. Although the corresponding research is still in the early stage, IAI is attracting increasing attention, incurring rapid technical development and making remarkable progress in applications (e.g., [3]). In essence, IAI involves six key techniques: modeling, diagnostics, prediction, optimization, decision and deployment.…”

Section: Introductionmentioning

confidence: 99%