###### Abstract  
Taking the multi-component perspective in Predictive Maintenance (PdM) is one promising approach to improve prediction quality. Therefore, detection and modeling of interdependencies within systems are important, especially as systems become more complex and personalized. However, existing solutions in PdM mostly focus on a single-component perspective, neglecting the dependencies between components, even if interdependencies can be found between most components in the real world. The major reason for this lost opportunity is the challenge of identifying and modeling interdependencies between components. This paper introduces a framework to identify interdependencies and explain their impact on PdM within a Multi-Component System (MCS). The contribution of this approach is two-fold. First, it shows the impact of modeling interdependencies in predictive analytics. Second, it helps to understand which components interact with each other and to which degree they affect the deterioration state of corresponding components. As a result, our approach can identify and explain the existence of interdependencies within components. In particular, we demonstrate that _time from last change of component_ is a valuable feature to quantify interdependencies. Moreover, we show that taking into account the interdependencies provides a statistically significant improvement of f1-score by 7% on average compared to the model where interdependencies are neglected. We expect that our findings will improve maintenance scheduling in the industry while improving prediction models in general. predictive maintenance; Industry 4.0; Shapley; XAI; Multi-component Systems; Interdependencies 20233088020233083  
PdM [4]. However, the availability of condition monitoring data due to various embedded sensors within manufacturing systems provides a solid basis to consider data-driven PdM in the context of MCS. Moreover, existing research works in the field of MCSs towards PdM assumed and showed that interdependencies between components could be found [2; 3; 4]. In practice, the interdependencies are described and modeled based on domain knowledge. In real-world scenarios where an old worn-out, and a newly changed component are coupled within a complex system, the deterioration rate of the new component and the system might accelerate due to the effect of the old worn-out component. In this case, the degree of interdependencies evolves faster, which might diverge from the description provided in the service manual. In many cases, the manufacturer is unwilling to share details that describe interdependencies between components due to the sensitivity and business-critical knowledge they contain [6]. Further, understanding and describing all interdependencies from experience in a highly customized product is much more challenging. Therefore, it is crucial to identify and model the interdependencies entirely from data in an accurate way to provide suitable decision support [7]. This could provide different benefits, such as increasing predictive results or improving model explainability, thus, optimizing maintenance scheduling and providing more realistic solutions, which are applicable in real complex systems [2; 4; 8; 9]. However, to the best of our knowledge, the impact of modeling interdependencies in predictive models is not demonstrated so far. [PERSON] and [PERSON] [10] introduced a common PdM use case application in an industrial setting. This work aims to predict component failures using sensor and maintenance data. In particular, the usage time of a specific component is included under the assumption that this information helps to predict the failures of this component. This behavior is known as the intrinsic effect in literature [2], which assumes and considers the existence of own dependency concerning the corresponding component. However, the interdependencies between components are not assumed.