# An Open Source Framework Approach to Support Condition Monitoring and Maintenance
## 6 Conclusions  
The use of the Arrowhead Framework provides the possibilities to use all the modules and tools of its ecosystem as well as interoperability between the various systems. It results in options, such as the condition monitoring of geologically distributed machines that belong to a local cloud. Besides, it gives the system the potential use of the service-oriented architecture (SOA) with non-functional requirements, such as the service registry and discovery, as well as security. Thus, it helps in bridging the gap between the maintenance industry and other communities supported by open source technologies. In this sense, the present work is a step further for the democratization of advanced monitoring and maintenance strategies. Further work will be devoted to the design of a core service that contains the model of the machines to be monitored, both for easing the configuration of existing and new local clouds, as well as to allow to communicate and exchange monitoring models. The Arrowhead Framework provides the data acquisition and collection of multiple sources, such as bearing data or machine status data collected from various I/O ports. This paper, thus, presents the innovative integration between the IoT, in this case, the Arrowhead Framework with the predictive  
Figure 8: Faulty vibration signal in the time domain with the characteristics in Table 1.  
Figure 9: Faulty vibration signal in frequency domain with the characteristics of the Table 1.  
health monitoring approach that supports the rolling element bearing condition, i.e., rolling element bearing (REB) fault analysis with the support of the Python language. The use of the Arrowhead Framework gives not only the possibilities to add new modules like the one mentioned in the current paper, i.e., REB fault analysis, but also other suitable machine learning algorithms for purposes of condition monitoring and maintenance decision making. In addition, it considers the best practices and standards in the domain of interest, i.e., OSA-CBM and the MIMOSA CRIS. The use of Python brings some positive features, such as less development time, which is based on a large number of built-in libraries. Thus, the use of the Python language affects the model reusability, required development efforts and design complexity. Consequently, it is believed that less time and efforts in the development phases reduce related costs. When it comes to less development time, the more favorable development aspects can motivate smaller companies with a limited budget to make investments in systems similar to the one presented in this paper, thus opening the door to employing advanced condition monitoring, prognosis, and decision making aspects for REBs fault analysis system. [PERSON] contributed by conceptualizing the work and article as well as the solution, developing its methodology. He was also involved in writing the original draft as well as reviewing and editing the article. [PERSON] helped in the development of the methodology, especially the parts related to IoT testing and maintenance, as well as a review of the paper. [PERSON] contributed to the conceptualization of the solution, the analysis of the benefits of the Arrowhead approach; he worked on the integration with IoT platforms in general and the Arrowhead platform in particular, as well as a review of the article. [PERSON] worked on the Arrowhead platform, as well as a review of the paper. [PERSON], created the rolling element simulated data as well as performed the analysis and results of it. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The present work has been part of the MANFIS Cyber-Physical System based Proactive Collaborative Maintenance project, which has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 662189.