Abstract
The current growth in digital technologies is transforming the working environment in all industries; even in traditional industries, scientists are experimenting with the Internet of Things (IoT), data analytics, sensor technology and most importantly machine learning. This paper addresses the use of predictive maintenance techniques to improve product lifecycle. The classical purpose of predictive maintenance is to diminish unexpected downtime, resulting in increased productivity and reduced production costs. However, the purpose of this study is to investigate and explore the potential of predictive maintenance and its relation to Industry 4.0, and product/process re-engineering through product lifecycle management (PLM), hence leading to Predictive Maintenance 4.0. During the operating phase of the product lifecycle, results from the Predictive Maintenance 4.0 model will not only help in predicting faults, but it will be crucial in product design and manufacturing advancement. This paper develops the architecture of a Predictive Maintenance platform connecting the industrial unit floor with design and manufacturing engineers. Feedback from the platform and interaction between different stakeholders from design, manufacturing, and operation will help in the advancement of the product itself.
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Saidy, C., Valappil, S.P., Matthews, R.M., Bayoumi, A. (2020). Development of a Predictive Maintenance 4.0 Platform: Enhancing Product Design and Manufacturing. In: Ball, A., Gelman, L., Rao, B. (eds) Advances in Asset Management and Condition Monitoring. Smart Innovation, Systems and Technologies, vol 166. Springer, Cham. https://doi.org/10.1007/978-3-030-57745-2_86
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