# An Open Source Framework Approach to Support Condition Monitoring and Maintenance
## 2 The Open System Architecture--Condition-Based Maintenance  
Both the military and the industrial domains are showing a wider acceptance of CBM. CBM systems have to cope with a complexity that would scare most system engineers since they span over very different hardware (computers, and embedded systems such as sensors) and software that must be able to operate together to power CBM applications. The main approaches that are relevant for this work are MIMOSA CRIS and OSA-CBM. The Machinery Information Management Open System Alliance MIMOSA consortium, founded in 1994, developed the MIMOSA CRIS standard, which aims to facilitate the exchange of information between actors in the maintenance industry. To this aim, a number of open international agreements (standards) were developed, targeting different aspects of machine maintenance [3]. In particular, the most common concretization of the standard is a relational database model for different data types related to CBM applications, and it was also the main driver for the structure of the systeminterfaces and database schema itself. From the point of view of licensing and reach, MIMOSA's interface definitions are open and portray a standard data exchange for modern CBM systems. OSA-CBM is the result of an industry initiative that had the goal of developing and demonstrating an open system architecture for condition-based maintenance. Among the stakeholders that developed the approach, there was the US Navy, and the MIMOSA consortium. The resulting platform is a de facto standard that covers all functional requirements related to this domain, such as data collection and the recommendation of maintenance actions. Modern ICT technologies allowed us to apply distributed software architectures to CBM and to make it more cost-effective than traditional standalone systems. Figure 1 represents the seven layers of architecture of OSA-CBM [11]. For the sake of argumentation regarding the benefits of OSA-CBM to the problem at hand, we will now describe the layer that the platform is composed of:  
* Layer 1--Data acquisition: This layer is devoted to collecting data from physical sensors, digitalizing it, and passing it upstream;
* Layer 2--Data manipulation: Data are pre-processed by means of signal processing techniques while converting them in a format that is more suitable for analysis in later stages;
* Layer 3--Condition monitor: This stage looks for outliers by comparing processed data with expected data. If a threshold is reached, this layer generates an alert for the user;
* Layer 4--Health assessment: Data are received from downstream, and other health assessment modules and the layer determine the level of machinery deterioration. If this is the case, relevant fault conditions are computed;
* Layer 5--Prognostics: Collected data are processed together with statistical and historical data, to compute a projection of the machinery's remaining useful life (RUL);
* Layer 6--Decision support: This layer acts a recommender system that suggests an action among a set of alternatives, most of them usually being maintenance actions;
* Layer 7--Human interface (Presentation layer): This layer takes care of displaying to the user all the information computed by the lower layers, such as the health of the machine (Layer 4), the prognosis of its condition (Layer 5) and the recommendations for maintenance actions (Layer 6). CBM has become the go-to methodology for the health monitoring of machines in the automated production and processing industries. However, it is a complex process that requires the collection and analysis of data from the selected asset, and the process is customized to the type of asset [12]. Figure 1: Data flow within an open system condition-based maintenance (CBM) design [11]. A CBM system collects the machine data through the embedded sensors (or other offline data collection methods) and sends them to the central server. These collected data can either be the event data or the condition monitoring data.