# A Real-Time Novelty Recognition Framework Based on Machine Learning for Fault Detection
## 5 Real Case Study
### First Industrial Prototype Implementation  
Furthermore, another real case is analyzed by implementing the whole framework on an industrial machine provided by Spea Spa--a company that designs systems able to test electrical and electronic devices--to validate the prototype on a real industrial case. Figure 17: (**a**) Predictions with BPFI fault \([x_{acc}]\); (**b**) Predictions with BPFI fault \([y_{acc}]\). Figure 18: Predictions with BPFI fault \([y_{acc}]\) and update. The machine chosen is a multi-purpose machine with x/y/z motion capabilities, used by the company to perform different tasks, from MEMS to spring probes tests. This latter ability regards the main focus of the data collection and framework implementation. The company designs a stress program to test the probe's wear over time. Usually, probes are considered spoiled after a given number of cycles using a preventive maintenance approach. Spea chose this threshold considering the past probes' electrical resistance value degradation. If the resistance exceeds the minimum threshold, the probe is intended to be worn. The company has wanted to change this approach by applying the framework to evaluate when the probe deviates in an anticipated manner following a predictive method. The [PERSON]'s machine keeps a set of probes as the main tool. It measures the electrical resistance of probes by knocking them repeatedly on a conducting test surface. The machine executes this operation continuously, collecting raw data during the process. A customized raw dataset template has been built. It is composed of sampling each probe's resistance and the related time references. The AIU preprocessing task transforms the custom raw dataset into the standard AIU data format treated by AIUP. Each dataset comprises 10 resistance values collected for each probe over time. The same framework's storage is maintained for this experiment: MongoDB as Storage and Data Source Unit database. The AIU Configuration used for this evaluation is the following (Table 7):  
As for the previous experiments, AIU trains its models on a set of initial raw data coming from the machine, producing results related to the model's errors. The result obtained after this framework application is reported in Figure 19.  
The result of AIU computation is the blue dashed line in the graphs above, corresponding to the MAE error degradation of a probe computed on electrical resistance data over time. MAE is stable and fixed around zero; apart from a few low amplitude disturbances, no degradation appeared on the dataset. Hence, there are no significant changes from the initial trained model, meaning that the probe is not worn. As explained in the real case, the blue straight dashed lines that appear in the data pattern are due to data missing during that period. The data acquisition lasts a few hours, while the idle period without acquisitions lasts numerous days; this different scale range creates long lines. The graph reported in Figure 19 shows that no shift appears in the data pattern; finally, no general deviation appears, which is a symptom that no relevant changes or problems are happening. This prototypical application confirms that incoming data does not change from the training ones. Hence, future works regard the ongoing collection of other data to identify possible future changes related to failures. \begin{table}
\begin{tabular}{c c} \hline Train Elements & 50 \\ \hline Test Elements & 2 \\ \hline Windows & 5 \\ \hline \end{tabular}
\end{table} 
Table 7: AIU Configuration—Spea Prototype. \begin{table}
\begin{tabular}{c c} \hline Average & 2 \\ \hline Lowest Bound & 0.20 \\ \hline Highest Bound & 1 \\ \hline \end{tabular}
\end{table} 
Table 8: MCU Configuration—Spea Prototype. The table results lead to the idea of how the system works efficiently. The training stage requires a very small amount of time, thanks to the easy models used. This time also gives the possibility to maintain the framework in real-time when other sensors are added to the machine.