In the recent years, manufacturing companies are investing in sensors and information systems to implement condition-based maintenance (CBM), thus pursuing the benefits of digital transformation. Nevertheless, to implement CBM as advanced digital system, significant investment should be made to gather and manage all needed data from different sources; besides, qualified human resources are required for data analytics. Given this premise, the present paper aims at describing an industrial project where an advanced CBM system for high-critical industrial fans is implemented in a foundry. Indeed, the goal is to use already available data from the extant automation and additional vibration data to develop state detection and to identify any abnormal behaviour of the assets. The evidence from the project is that: i) the vibration analysis remains an easy and cost-effective, yet well-performing way, to monitor the state and the health of machines with rotating components; ii) automatic regulation system may mask the underlying behaviour and degradation of complex assets; iii) already gathered data from extant automation are mainly focused on the process parameters and provides an aid to describe the working state of the assets, but have limited potentialities for novelty detection. Eventually, the paper envisions future development of a more integrated approach aimed at a combined elaboration of data from the extant automation and vibration data. The integrated approach is under development, hence the paper provides insights on the on-going analyses.

Development of an advanced condition-based maintenance system for high-critical industrial fans in a foundry

Polenghi, A.;Cattaneo, L.;Macchi, M.;
2022-01-01

Abstract

In the recent years, manufacturing companies are investing in sensors and information systems to implement condition-based maintenance (CBM), thus pursuing the benefits of digital transformation. Nevertheless, to implement CBM as advanced digital system, significant investment should be made to gather and manage all needed data from different sources; besides, qualified human resources are required for data analytics. Given this premise, the present paper aims at describing an industrial project where an advanced CBM system for high-critical industrial fans is implemented in a foundry. Indeed, the goal is to use already available data from the extant automation and additional vibration data to develop state detection and to identify any abnormal behaviour of the assets. The evidence from the project is that: i) the vibration analysis remains an easy and cost-effective, yet well-performing way, to monitor the state and the health of machines with rotating components; ii) automatic regulation system may mask the underlying behaviour and degradation of complex assets; iii) already gathered data from extant automation are mainly focused on the process parameters and provides an aid to describe the working state of the assets, but have limited potentialities for novelty detection. Eventually, the paper envisions future development of a more integrated approach aimed at a combined elaboration of data from the extant automation and vibration data. The integrated approach is under development, hence the paper provides insights on the on-going analyses.
2022
14th IFAC Workshop on Intelligent Manufacturing Systems IMS 2022
condition-based maintenance, CBM, prognostics, health management, PHM, state detection, health assessment, vibration, warning system, industrial fan, manufacturing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1226939
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