Using Operational Data to Represent Machine Components Health and Derive Data-Driven Services

When it comes to the procurement of technical components that are required for the maintenance of machinery and equipment, many enterprises face significantly increased prices, as well as long and uncertain delivery times. As a consequence, they consider potential alternatives to meet material requirements. One of these alternative approaches is to extend the useful life of required technical components based on an assessment of their current condition and a prognosis of the remaining time till a probable default. This approach is also driven by a general trend towards more sustainability. Assessment, prognosis and the generation or provision of required data can be regarded as a data-driven Services that is related to a physical product and thereby forms in combination a Smart Product Service System (Smart PSS). However, a reliable prognosis of condition in this context is a challenging task. Usually, the development of an appropriate prognosis model requires a substantial amount of data and significant effort. To decide, if it is beneficial to invest in the development and marketing of such a PSS, several data-related assessments have to be made. The objective of the approach presented here is to support enterprises in their decision on PSS implementation that are based on a wide database and cause a high development effort. This comprises the identification and analysis of different relevant types of data and sources of data as well as the evaluation of the effort to obtain this data in different phases of the PSS life cycle. The discussion is based on an example of a PSS that is aiming at the prognosis of the remaining useful life of a technical component.