Condition-based Maintenance: Model vs. Statistics A Performance Comparison

Abstract

The current development in industrial applications shows a variety of approaches to perform maintenance: With reactive maintenance, only parts which fail will be replaced. This causes high costs and high unexpected failure rates. Preventive maintenance uses a predefined service plan and also a wear part exchange schedule. The plan or schedule is often based on real-time or an operation time. This often results in fixed maintenance cycles or an operation time-based maintenance. This can lead to a replacement or maintenance of a completely healthy component or to ignoring components that need to be replaced more frequently. Condition-based maintenance is an advanced approach which is based on measured component data to identify the current status of a component. This status is used to determine the date of maintenance or exchange as estimated end of life. Thus, only damaged components are maintained or exchanged. The scope of this paper is to implement a model-based maintenance algorithm in a real industrial application to determine the remaining lifetime of a component. A very important requirement is a good identification process for the model and the component. However, short commissioning times and a variety of different components pose an increased effort to identify the parameters. Thus, this paper presents an approach for a parameter identification which solely relies on data being present in the numerical control of the machine. The model-based approach is then compared to a simpler statistical approach using data from a running production machine.