Condition-based maintenance: an industrial application on rotary machines

Abstract

PurposeThe purpose of this paper is to describe a model for the design and development of a condition-based maintenance (CBM) strategy for the cutting group of a labeling machine. The CBM aims to ensure the quality of labels' cut and overall machine performances.Design/methodology/approachIn developing a complete CBM strategy, two main difficulties have to be overcome: (1) appropriately dealing with incomplete and low-quality production database and (2) selecting the most promising predictive model. The first issue has been addressed applying data cleansing operations and creating ad hoc methodology to enlarge the training data. The second issue has been handled developing and comparing an empirical model with a machine learning (ML)-based model; the comparison has been performed assessing capabilities thereof in predicting erroneous label cuts on data obtained from an operating plant located in Italy.FindingsResearch results showed that both empirical and ML-based approaches exhibit good performances in detecting the operating conditions of the cutting machine. The advantage of adopting an ML-based model is that it can be used not only as a condition indicator (i.e. a model able to continuously provide the health status of an asset) but also in predictive maintenance policies (i.e. a CBM carried out following a forecast of the degradation of the item).Research limitations/implicationsThe study described in this manuscript has been developed on the practices of a labeling machine developed by an international company manufacturing bottling lines for beverage industry. The proposed approach might need some customization in case it is applied to other industries. Future researches can validate the applicability of such models on different rotary machines in other companies and similar industries.Originality/valueThe main contribution of this paper lies in the empirical demonstration of the benefits of CBM and predictive maintenance in manufacturing, through the overcoming of a specific production issue. The large number of variables involved in thin label cutting lines (film thickness between 30 and 38 µm), the high throughput and the high costs due to production interruptions render the prediction of non-conforming labels an economically relevant, albeit challenging, goal. Moreover, despite the large scientific literature on CBM in rolling bearing and face cutting movements, papers dealing with rotary labeling machines are very unusual and unique.

AI-enhanced subsea maintenance for improved safety and efficiency: Exploring strategic approaches

“…CBM minimizes downtime by performing maintenance only, when necessary, based on the actual condition of the equipment (Acernese et al, 2021). By avoiding unnecessary maintenance activities, equipment downtime is reduced, and production uptime is maximized.…”

Maintenance work management process model: incorporating system dynamics and 4IR technologies

“…Hatem Algabroun et al developed a concept for digital maintenance through 4IR technologies (Algabroun et al, 2022). Acernese et al describe a model for developing a condition-based maintenance strategy through the adaption of empirical and machine learning-based models for comparison purposes (Acernese et al, 2021). The study reveals that the proposed predictive models support the maintenance team in equipment shutdowns through a reliable decision system (Acernese et al, 2021).…”

Dataset of single and double faults scenarios using vibration signals from a rotary machine

“…Machine learning classification is one approach to implement machine health management. The goal of system health management is to enable condition-based maintenance, where parts are replaced as needed, as opposed to scheduled maintenance where parts may be replaced prior to failure and increase system down-time [4] . Machine learning (ML) classification algorithms can be used to identify faults based on experimental training data such as the dataset described in this work.…”