# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 6 Discussion and Conclusions  
In this paper, a method for data reduction, collection, and labeling is validated on five datasets collected from the industrial field. The main considerations will be described at two levels: the data analysis method and the data and information collection framework. At the analysis level, two primary considerations can be drawn. First, the features extracted in the time domain and selected through the Pearson correlation analysis, which reduces the redundancy of information included in the features by selecting only the less correlated ones, are able to reveal all health and fault conditions, except for one fault condition of the case packer and one fault condition of the sealing group. Therefore, these methods can properly extract and select relevant information from raw data, even if few data are available for each condition. In contrast, if a fault condition is not included in the training set, it may happen that the selected features cannot distinguish it from the nominal  
Figure 14: Results of the AAD + ADP algorithm (sealing group). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Conditions** & \begin{tabular}{c} **Cycle of** \\ **Detection** \\ \end{tabular} & \begin{tabular}{c} **Latency** \\ **(Sample)** \\ \end{tabular} & \begin{tabular}{c} **N Created** \\ **Clusters** \\ \end{tabular} & \
\begin{tabular}{c} **N Points** \\ **Alarms** \\ \end{tabular} \\ \hline Nominal-Fault 1 & 706 & 10 & 0 &
\begin{tabular}{c} Cluster 1 = 705 \\ Cluster 2 = 324 \\ Cluster 1 = 705 \\ Cluster 2 = 191 \\ Cluster 1 = 886 \\ \end{tabular} & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performances of AAD + ADP (sealing group). behavior. However, in a previous paper [58], the authors demonstrated that the fault condition of the sealing group not detected by the AAD + ADP approach is not recognized by a classification-based approach either. Therefore, the issue is related to the collected signal, which cannot provide information about that improper condition. The second consideration at the analysis level concerns the application of the streaming AAD + ADP algorithm. This approach effectively recognizes known and novel behaviors with low latency in all case studies. In addition, it is fast and requires little computation effort. At the framework level, the description of the datasets provided by industries and the application of the streaming clustering-based approach for fault and novelty detection led to two main considerations. The first consideration concerns the characteristics of the available datasets. As can be seen in Table 1, the datasets are significantly unbalanced. In particular, when the electric motor failed, the system broke down and only 25.8 min of fault signals could be collected. In contrast, 344.4 min of signals are collected during the nominal operating conditions. Similarly, the detachment of the second section cups in one of the monitored automatic machinery led to an acceptable working condition; therefore, only 3.52 s of signals were collected. In the case study introduced in Section 5.4, aimed at recognizing the operating condition of the system, it seems that no faulty data are available at all. However, the data cover a long period (from 1 to 2 years), and several improper working conditions occurred during those periods. The data collection problems, in this case, concern the lack of a label and the quantity of data. Because the machine generates many gigabytes of data, it cannot be monitored continuously. Therefore, only some batches of data per day are collected, which are unlabeled and without any contextual information. This consideration implies a huge effort for the data analysts and makes the data-driven approach less effective. The second consideration concerns the possibility of solving the above issues by applying a streaming approach at the edge of the machinery to reduce the quantity of data to transfer, store, and automatically label the collected data. Indeed, raw signals can be processed at the edge to extract relevant features.