Maintenance plan adaptation based on health ratings of servitised machines through a fleet-wide machine clustering method

The increased requests for value-added services to integrate product performance push manufacturing companies to extend their service offerings to meet customers’ needs. In this context, maintenance planning can leverage new possibilities offered by digital technologies for data analytics services. The present research then proposes an approach for maintenance plan adaptation based on a data-driven method applied over a fleet of machines installed in different production sites. The method relies on collaborative prognostics to develop a clustering of machines’ behaviour aimed at providing the health ratings of the machines and the subsequent maintenance plan adaptation due to the deviation from the expected behaviour. The method is adopted from the perspective of an Original Equipment Manufacturer, as part of a transformation path towards an advanced provision of digitalization for maintenance service offerings. The method is validated in the context of two lines at selected customer’s premises. This demonstrates the viability and effectiveness of adapting the maintenance plans thanks to the data analytics in light of the current behaviour of the machines within the lines. • Current maintenance services by OEMs lack adequate exploitation of data from machines. • Threshold definition and health rating are based on human subjectivity and experience. • An automatic data-driven health rating enables maintenance plan adaptation. • Data exchange includes only machine status habilitating fleet-wide machine similarity identification. • Validation via application to two lines realising medical devices for therapeutic uses.