# A Real-Time Novelty Recognition Framework Based on Machine Learning for Fault Detection
## 5 Real Case Study
### First Industrial Prototype Implementation
Indeed, the order of magnitude about time always remains around seconds when performing the computation. The AIU requires more time than the MCU due to the higher computational effort; the training process is the hardest computation processed by the AIU. All the performances strictly depend on the number of windows elaborated: a high number of windows corresponds to high requirements in terms of time performance. Concerning the MCU, Table 10 refers to the agent's mean and variance time performances during the execution of its task on a single window. Each agent requires a small amount of time to perform a prediction compared to AIU. MCU works each agent one at a time, following a time scheduling approach (when it concludes with one agent, it can start with the following one); for this reason, the number of windows influences the MCU's time performance in a preponderant way. This computation can be performed by multiplying the execution time of each agent by the total number of windows:  
\[T_{m}=T_{e}\cdot w\]  
\(T_{m}\) is the Total MCU Time, \(T_{e}\) is the execution time, and \(w\) is the windows number. The different domain examples proposed in this paper prove the flexibility and adaptability of the framework in any domain where it is adopted. Bearing degradations are analyzed for both Simulink simulations and real case, in which the framework was able to understand when the system deviates excessively, alerting the maintainer before the breakdown. Different types of faults are used to test the framework's capability: low lubrication increases the system's noise in terms of vibration captured by the sensors and processed by the framework, giving an estimation of when this vibration becomes too high. Then, a ball-pass-inner-race-fault is tested to prove the framework's flexibility, in which a force impulse acts on the bearing with a specific period; hence, these impulses can be seen only in some of the windows that include them. The predictors related to such windows recognized that something was changing from the trained data, alerting the maintainer. While in the first case, all predictors showed a general error increase, only some showed this behavior in the second test, as wanted. Finally, the framework is tested in a completely different real environment, a resistance test, always to show flexibility and adaptability properties. In this case, the framework has to work with another physical variable, understanding if some probes' resistance is changing from the beginning and giving an alert to the maintainer when the probe will be worn. As shown, the framework works well again in finding that probes are healthy till that moment without a false positive warning sent to the maintainer. As reported in Section 2, thanks to the algorithm's ease and adaptability, it can be used in numerous domains by comparing the trained data with the actual ones. To summarize, the framework's advantages are as follows:  
* Experience and data history is not necessary: this framework can detect novelties in observed data such as errors, faults, problems, and changes related to machines, as well as environmental data such as vibrations or noises, temperature, or magnetic field comparing to the trained ones. Furthermore, this property can help collect datasets and maintenance warnings for future definitive PdM applications;
* Flexibility/Scalability: the framework is very flexible and scalable; a maintainer can add a sensor in the machine anytime by updating or retraining the framework creating a new starting point, but always recording past data for a future PdM application;
* Low computational effort required: the framework uses straightforward ML models to estimate when and how the trained and current data differ with low computational effort, facilitating a future implementation on embedded devices.