# Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification
## 5 Discussion  
In modern industrial systems, complexity is increasing as multi-sensor network systems are expanding towards largescale systems. The intelligent solution of Electrical Signature Analysis allow simplifying the fault identification processes because it does not require a large number of sensors, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**id** & **Ia\_motor** & **Ib\_motor** &... & **isUnbalance** & **isMisalignm** & **Severity** \\ \hline
0 & 0.687150 & 0.028560 &... & 0 & 1 & ‘Good’ \\ \hline
1 & 0.019699 & 0.388238 &... & 1 & 0 & ‘Good’ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Structure of the dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Method and Label (0/1)** & **Precision** & **Recall** & **f1-Score** \\ \hline
**Binarized** & 0 & 0.79 & 0.88 & 0.83 \\
**Decision Tree** & 1 & 0.85 & 0.94 & 0.89 \\
**Classifier Chain** & 0 & 0.79 & 0.88 & 0.83 \\
**Classifier Chain** & 1 & 0.78 & 1.00 & 0.88 \\
**KNN** & 0 & 0.76 & 0.76 & 0.76 \\
**KNN** & 1 & 0.82 & 1.00 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation of the tested models. which results in remarkable cost reduction, and supports sensorless and largescale technologies. Furthermore, a well-developed theoretical and practical methodology could serve as a basis for reliable remote diagnostic tasks of electrical machines, especially important diagnostics carried out in hazardous environments (for instance, in nuclear plants). The development of appropriate diagnostic-prediction methods, which are capable of reliably and timely evaluating the health status of the system on the basis of representative parameters acquired directly or indirectly, is an important part of the modern electric drivetrain monitoring system. The state-of-health of a certain dynamic system and its possible initial failures can be addressed by a number of approaches published in the literature. The strengths and weaknesses of conventional fault diagnosis methods have already been proven. In recent years, diagnostics research has focused on the prediction algorithms that can identify the fault features of progressive malfunctions. As such algorithms are highly technology-dependent, it is important to define the method as a function of a large number of parameters of the dynamic system for fitting to the system under consideration, taking into account boundary conditions. Data-driven technologies aim such difficulties. Fault classification methods have already been established for the investigation of the relations between the symptom and the fault feature. It is clear that binary relationships can be easily represented by such systems. Early versions of these algorithms are used rather for visualizing diagnostic reasoning. However, practical engineering problems demanded the development of new automatic methods that are able to deal with multiple fault scenarios and noisy or uncertain fault features. A possible solution is the extension of the most commonly applied classification methods. Recently, binary trees, Chain Classifiers, Neural Networks, and other multi-label and multi-class techniques were introduced in the literature. However, these algorithms need more development and improvement, especially in terms of robustness. In addition, the application of Machine Learning techniques on sensory data is still not well-established and requires the synergy of classical signal processing and intelligent data analysis.