# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 6 Discussion  
In this paper, a study on the methods used to determine the operating conditions in which machinery works is conducted. There are two relevant aspects to identifying a system's operating condition. First, it affects the behavior of components, meaning that their failure modes, progression, and thresholds depend on processed materials, products, or environmental factors. Although many efforts have been dedicated towards building models that are independent of the system's condition, industrial data collected from machinery are difficult to analyze if no information about the setting is provided. For instance, a sudden jump in the temperature values collected from an extruder, which in fact corresponds to a change in the processed material, might be confused with an anomaly if the information concerning the setting change is not provided. Second, the absence of information about the machinery setting may represent a limit for machinery producers that want to offer a full maintenance service to their clients. Machinery producers cannot know the actual operating conditions of their equipment, and their clients (machinery users) are not willing to share sensitive information that can be extracted from the knowledge of the implemented setting, e.g., the number of pieces per product type, or the production rate. Hence, datasets collected from machinery working in their operating environment may refer to different use modes, which are unknown to the producer. This results in the impossibility of building general diagnostic and prognostic models that can be used for the same type of machinery operating in different environments. Therefore, it is fundamental to recognize when a setting change occurs in an unsupervised way or to determine whether the component behavior in that setting has already been analyzed (whether the operating condition is known). In this way, it is possible to automatically label the dataset with a class corresponding to the operating condition and increase knowledge about the machinery behavior in the actual operating condition. The proposed autoencoder-based architecture allows the approach to perform three distinct tasks, i.e., classification, novelty detection, and localization, which support the collection of labeled datasets and the detection of novel situations. In particular, the classification task is to identify which operating condition among those known at the moment of the model training is implemented in any given moment. Novelty detection is to determine whether the operating condition implemented at that moment were not \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**C1**} & \multicolumn{3}{c}{**C2**} & \multicolumn{3}{c}{**C3**} & \multicolumn{3}{c}{**C4**} \\ \hline
**Precision** & **FC** & **CNN** & **LSTM** & **BiLSTM** & **FC** & **CNN** & **LSTM** & **BiLSTM** & **FC** & **CNN** & **LSTM** & **BiLSTM** & **FC** & **CNN** & **LSTM** & **BiLSTM** \\ \hline p1 & 93.84 & 1.89 & 87.88 & 33.78 & 54.99 & 67 & 39.77 & 34.31 & 100 & 40.38 & 95.02 & 12.77 & 0 & 59.57 & 5.91 & 0 \\ p3 & 99.94 & 20.33 & 100 & 100 & 91.77 & 94.48 & 87.38 & 82.47 & 100 & 100 & 36.27 & 0.35 & 94.36 & 17.11 & 4.89 \\ p5 & 100 & 58.85 & 100 & 100 & 98.74 & 99.28 & 98.96 & 96.95 & 100 & 100 & 100 & 72.62 & 25.62 & 99.74 & 26.06 & 18.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Precision @k for most anomalous signal. included in the classification model training, and therefore is novel. Finally, localization is to determine which signals are most involved in the novelty detection. As demonstrated in the previous section, the deep learning models outperform traditional methods in establishing the working condition of a given system. From experiments conducted in Section 5, it emerges that when no fault occurs in one of the components, the proposed approach classifies the distinct settings of the machinery with a high accuracy and precisely recognizes novel conditions with respect to those used during the model training (See Table 3).