Applying Sound-Based Analysis at Porsche Production: Towards Predictive Maintenance of Production Machines Using Deep Learning and Internet-of-Things Technology

Matthias Auf Mauer (), Tristan Behrens (), Mahdi Derakhshanmanesh (), Christopher Hansen () and Stefan Muderack ()
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Matthias Auf Mauer: Porsche Digital Lab
Tristan Behrens: Porsche Digital Lab
Mahdi Derakhshanmanesh: Porsche Digital Lab
Christopher Hansen: Porsche Digital Lab
Stefan Muderack: Porsche Digital Lab

A chapter in Digitalization Cases, 2019, pp 79-97 from Springer

Abstract: Abstract (a) Situation faced: All mechanical and mechatronic devices are subject to wear, tear and breakdown. Failure of such devices can cause significant costs, e.g., in automotive factories. Established predictive maintenance approaches usually require deep integration with the specific machine. Such approaches are not practically feasible because of technical, legal and financial restrictions. A non-intrusive, lightweight and generic solution approach is desired. (b) Action taken: A solution concept was developed which, at its heart, is based on deep learning algorithms that monitor sound sequences captured from a microphone, analyze them and return classification results for use in further steps of a control loop, such as planning actions and execution steps. We named this approach the ‘Sound Detective’ and it was evaluated by retrofitting a coffee machine using simple microphones to capture production sounds. The sound sequences are subsequently analyzed using neural networks developed in Keras and TensorFlow. During prototyping, multiple kinds of neural networks and architectures were tested and the experiment was realized with two different kinds of coffee machines to validate the generalizability of the solution to different platforms. (c) Results achieved: The prototype can analyze sounds produced by a mechanical machine and classify different states. The technical realization relies on cheap commodity hardware and open-source software, demonstrating the applicability of existing technologies and the feasibility of the implementation. Especially, it was described that the proposed approach can be applied to solve predictive maintenance tasks. (d) Lessons learned: The present work demonstrates the feasibility of the Sound Detective’s reference architecture and discusses challenges and learnings during implementation. Specifically, key learnings include the importance of data quality, preprocessing and consistency, influences of the experimental setup on real-world prediction performance and the relevance of microcomputers, the target hardware and type of the programming language for complex analyses.

Keywords: Production Machines; Predictive Maintenance Solutions; Coffee Machine; Sound Detection; Deep Learning Algorithms (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:mgmchp:978-3-319-95273-4_5

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DOI: 10.1007/978-3-319-95273-4_5

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