The main goal of maintenance of complex systems is to minimize downtimes to make the system as much available as possible. Condition-Based Maintenance (CBM) is one of the most effective policies used by Companies nowadays, based on the monitoring of different parameters of machines that reflect its health status. CBM can be implemented by using the Prognostic Health Management approach, made up of four main steps: data collection, signal processing, diagnostic, and prognostic. It is a proactive process that requires the development of predictive models that can trigger the alarm for corresponding maintenance. The huge amount of data that need to be collected has suggested the use of models coming from statistic theory and data mining, in order to discover regular pattern in large data sets and generate knowledge that will be useful in the maintenance decision-making process. In this paper, different intelligent methods for diagnostic purpose, such as Decision trees, K-NN algorithm, Artificial Neural Networks and support Vector Machine, are used to classify the health condition of a rotating component. Collected signals are processed in the time-domain and in the time-frequency-domain in order to extract relevant features to give as input data for the intelligent methods. Such methods are finally compared by evaluating the related accuracy value for both training and testing. The main result of this work is that the time-frequency analysis improves accuracy in classifying the health condition of machines and that new intelligent models can perform in an effective way even in the time-domain

Components monitoring and intelligent diagnosis tools for Prognostic Health Management approach

CALABRESE, FRANCESCA;Regattieri Alberto;Piana Francesco
2018

Abstract

The main goal of maintenance of complex systems is to minimize downtimes to make the system as much available as possible. Condition-Based Maintenance (CBM) is one of the most effective policies used by Companies nowadays, based on the monitoring of different parameters of machines that reflect its health status. CBM can be implemented by using the Prognostic Health Management approach, made up of four main steps: data collection, signal processing, diagnostic, and prognostic. It is a proactive process that requires the development of predictive models that can trigger the alarm for corresponding maintenance. The huge amount of data that need to be collected has suggested the use of models coming from statistic theory and data mining, in order to discover regular pattern in large data sets and generate knowledge that will be useful in the maintenance decision-making process. In this paper, different intelligent methods for diagnostic purpose, such as Decision trees, K-NN algorithm, Artificial Neural Networks and support Vector Machine, are used to classify the health condition of a rotating component. Collected signals are processed in the time-domain and in the time-frequency-domain in order to extract relevant features to give as input data for the intelligent methods. Such methods are finally compared by evaluating the related accuracy value for both training and testing. The main result of this work is that the time-frequency analysis improves accuracy in classifying the health condition of machines and that new intelligent models can perform in an effective way even in the time-domain
Proceedings of the 23rd Summer School "Francesco Turco" - Industrial Systems Engineering 2018
1
7
Calabrese Francesca, Casto Andrea, Regattieri Alberto, Piana Francesco
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/672035
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