# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 5 Results
### Dataset 1: Electric Motor  
In this case, the clustering-based with the pretraining approach is used. In particular, 70% of observations corresponding to condition 1 and condition 2 are included in the training set. The goal is twofold. First, the analysis at a system level is conducted to recognize known and novel system operating conditions; second, the analysis at a component level is conducted to detect the fault of the electric motor. To achieve these aims, time-domain features are extracted from signal batches of 1 s (12.8 samples). Then, the most relevant time-domain features are selected according to the following criteria: system-level features have to be as similar as possible in the same operating condition and as distinct as possible among different operating conditions; component-level features have to be as constant as possible during the two nominal conditions. The selected system-level and component-level features are shown in Figure 7. At a component level, the mean of three different signals (sensor 2, x-axis and z-axis, and sensor 3, y-axis) is selected, while the impulse factor of sensor 3, z-axis, is selected at a component level. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Goal** & **Approach** & **Feature Engineering Method** \\ \hline \multirow{3}{*}{Electric motor} & Operating Condition & \multirow{3}{*}{Clustering-based with} & \multirow{3}{*}{Manually selected features} \\  & recognition and fault & & \\  & detection & & \\ \cline{1-1} \cline{3-4} \multirow{2}{*}{Case packer} & Fault detection & & \\  & & & \\ \cline{1-1} \cline{3-4} \multirow{2}{*}{Suction cups} & Fault detection & & \\ \cline{1-1} \cline{3-4} \multirow{2}{*}{Automatic Machinery} & Operating Condition & & \\ \cline{1-1} \cline{3-4} \multirow{2}{*}{Sealing group (displacement)} & \multirow{2}{*}{Fault detection and diagnosis} & \multirow{2}{*}{Clustering-based with} & \multirow{2}{*}{Pearson Correlation, Principal} \\  & & & \\ \cline{1-1} \cline{3-4} \multirow{2}{*}{Case packer} & Fault detection & & \\ \cline{1-1} \cline{3-4} \multirow{2}{*}{Suction cups} & Fault detection & & \\ \cline{1-1} \cline{3-4} \multirow{2}{*}{Automatic Machinery} & Operating Condition & & \\ \cline{1-1} \cline{3-4} \multirow{2}{*}{Fealing group (displacement)} & \multirow{2}{*}{Fault detection and diagnosis} & \multirow{2}{*}{Clustering-based with} & \multirow{2}{*}{Pearson Correlation Analysis} \\  & & & \\ \cline{1-1} \cline{3-4} \multirow{2}{*}{} & & & \\ \cline{1-1} \cline{3-4} \multirow{2}{*}{} & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of each case study’s goals, approaches, and feature extraction methods. Then, the ADP algorithm is applied to find the clusters' corresponding local parameters, whose results are shown in Table 3.  
Then, the AAD + ADP algorithm is applied to all observations of each feature set. For the operating condition recognition task, streaming novelty detection aims to create two new clusters when condition 3 and the fault to the electric motor occur. The goal of the fault detection task is to create a new cluster when the fault occurs. Results are shown in Figure 8, and the algorithm's performances are summarized in Table 4.  
For the operating condition recognition, the points corresponding to conditions 1 and 2 are assigned to the initialized clusters correctly. The algorithm detects the change from condition 1 to condition 2 after 15 s and to condition 3 after 10 s. However, the fourth condition is not detected by the selected features. Concerning the fault detection task, the algorithm creates a novel cluster 18 s after the change from condition 3 to condition 4 (yellow dots in Figure 8b); then, a second cluster is created after 175 s, which corresponds to almost 23 min before the motor failure. Therefore, the system breakdown could have been avoided if an alarm was triggered at the second cluster creation time. In addition, all points assigned to cluster 2 can be labeled and used to update classification models for diagnostics.