# An Open Source Framework Approach to Support Condition Monitoring and Maintenance
## 2 The Open System Architecture--Condition-Based Maintenance
Event data are a record of the information related to various events of the machine. These events can either be the happenings to the machine, for instance, fault occurrence, overhaul of the systems, etc. or the actions were taken by the maintainer on the machine (e.g., part replacement, oil and lubricant change, etc.). On the other hand, the condition monitoring data are those that are actually picked up by the sensors to reflect the health of the machine [13]. The most critical part of a CBM program is to decide the measurement parameters and the location where the sensors must be placed. Once the CBM system is in place, data acquisition is the first stage. The collected data from the physical asset are passed to the next stage of data processing. Data processing in itself is a two-stage process that involves the cleaning of data as well as the analysis of them. There are numerous methods and techniques that are used to enhance the quality of the data. The analysis part of the process is undertaken only after the quality of the data has been improved to acceptable levels. Figure 2 is a pictorial representation of the stages in asset health monitoring. Diagnostic and prognostic are the two main types of health monitoring processes. Diagnostic monitoring is the process of identifying the faults after they have occurred, and the machine has broken down. It can be termed as the postmortem of the events where the fault is detected after it has already happened. Prognostics is the technique which prevents failures by predicting the likely future time of their occurrence. In medical terms, it is akin to conducting surgery to save the patient from dying. Instinctively, it can be seen that prognostics has more promise than diagnostics because it can prevent the faults and thus ensure that the machines continue to operate as intended. If the machine is allowed to run to failure, it can cause catastrophic damage by causing secondary failures in the machine. Prognostics is also useful in cutting down the logistical delays that often happen if the machine fails unexpectedly. Prognostic systems are at least able to predict a future failure, if not prevent it. This helps the maintainer in the pre-positioning of spare parts, tools, and maintenance personnel, which can eventually reduce the diagnosis and repair times. It is evident that one cannot do-away with diagnostics. Even if a good prognostic system is in place, it cannot accurately predict everything all the time. There will still be occasions when the machines fail without any warning from the prognostic system. At such times, one has to rely on diagnosis techniques to identify the fault and conduct repair on the machine by either replacing the component or repairing it. Prognostic techniques derive knowledge from the historical data to identify how the asset deteriorates before failure. This helps in charting the route on which the asset will progress, leading to the failure, and also tells the changes in the measured parameters as the asset follows this path. Health monitoring helps in identifying where on this path currently is the physical asset. In combination, these two can predict the future failures of the asset being monitored. Figure 2: Data operations in the asset health monitoring process. The data-driven prognostic models use artificial intelligence (AI) and are given the historical data as input. These data may come from CBM, Supervisory control and data acquisition (SCADA) measurements, etc. [14; 15]. These prognostic models use one of the following techniques or AI algorithms for fault prediction: particle filtering, simple trend projection model, data interpolation using artificial neural networks (ANN), regression analysis and fuzzy logic, time series prediction model, exponential projection using ANN, recursive Bayesian technique, Hidden Markov Models, hidden semi-Markov model, etc. [16; 17]. The emergence of big data and their various methods of analysis has introduced newer methods like clustering and classification for the prediction of faults.