# An Open Source Framework Approach to Support Condition Monitoring and Maintenance
## 1 Introduction
### Our Contribution  
This paper points focuses on selected perspectives that are important for these specific domains, namely for industrial maintenance engineering in particular and asset management in general. The authors, therefore, build over existing works on CBM by introducing the usage of open source solutions; paving the road to interoperability by adhering to mature standards (e.g., OSA-CBM); promoting efficiency and improving interoperability even further by the servitization of predictive health monitoring methods in connection with the promising ICTs such as the IoT. The social and technological benefits of our approach comprise the democratization and the wide acceptance of CBM.  
The driving force that led to the definition of the architecture, and the creation of the prototype, was to allow for a set of benefits to be enjoyed by CBM activities. First of all, usually, the analytics activities needed for CBM need to be executed in a computing center or on the cloud, and most implementations have to create their own solution to move the data from the factory to where the data can be used; our work aims to demonstrate how to allow this by means of the Arrowhead Framework. Second, many IoT devices have limitations in terms of computational capabilities, memory, and it is cumbersome to update their software, leading to security concerns; we will show that the devices can receive seamless security by being integrated into an Arrowhead local cloud. Third, evolving and extending an industrial solution is usually challenging, since a number of subsystems need to be updated to enable the communication of new devices in the field; by means of the Registry service and the Orchestration service, which are part of the Arrowhead Framework, new devices will be easily incorporated in the solution. It has been chosen to include the rolling element bearing (REBs) fault analysis module in our Arrowhead local cloud since it is a crucial part of condition monitoring (CM) and CBM, as mentioned above. However, in the area of CM and CBM, other related modules can be included in the solution, such as machine learning algorithms that have been tested in the domain [10]. Other modules that can be incorporated into the framework are, for instance, computerized maintenance management system, among other. Finally, in most cases, it is hard for a new company to design a CBM solution since its design, implementation, and deployment all require considerable effort; as discussed previously, by focusing on open source technologies, mainstream programming languages and well accepted software architectures, it is possible to lower the entrance barrier of new actors in the industry.  
The paper is organized as follows: Section 2 introduces the open system architecture and condition-based maintenance (OSA-CBM) and Machinery Information Management Open System Alliance Common Relational Information Schema (MIMOSA CRIS). The Section also describes the predictive methods. Section 3 presents the Internet of Things (IoT) frameworks from the literature. The Arrowhead Framework and its benefits are presented in Section 4, and we delve deeper into how it can help for the servitization of predictive health monitoring methods. Implemented data analysis testbeds that were incorporated into the Arrowhead local cloud are presented in Section 5. Conclusions are drawn in Section 6.