# Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification
## 1 Introduction
Therefore, the ML techniques are highly suitable to support the processing of such extracted information. There are some intelligent methods that have evolved in recent years with the purpose of improving fault diagnosis methods of electrical motor and drive systems associated with various fault events on the basis of current or vibration signature analysis. For instance, in [89] it has been shown that motor current signature analysis based Support Vector Machine classifier achieves better accuracy in case of gearbox multiple fault detection than decision trees. In [90], the authors state that hybridization of machine learning methods, e.g., adaptive neuro-fuzzy inference system (ANFIS) in combination with Classification and Regression Tree (CART) has the potential for fault diagnosis of induction motors. The proposed method also employs vibration and current signals and achieves nice performance; however, the method lacks the flexibility to easily fit with specific types of machines. Random Forest (RF) classifier was developed for multi-class bearing faults in [91]. This work also employs input features extracted from vibration signals. Worth mentioning that the bearing fault is an important source of faults in the rotating electrical machines that has been modeled by machine learning methods. [PERSON] [92] and [PERSON] et al. [93] provide extended description on the causes of the bearing fault. A number of studies report the advantages of the wavelet transform in signal detection and fault feature identification (see, e.g., [94; 95; 96]). The decision tree's capability, in combination with the well-studied wavelet technique, also provides a possible tool for fault detection [97]. In [97], a large number of possible wavelets are analyzed in order to find the best match. The selection of a suitable wavelet function is still a challenge for the users for specific applications. The investigation of mechanical fault signatures of misalignment and unbalance is carried out in [97], where the authors found that a multilayer perceptron model only with three layers, can classify the faults. High accuracy is reported that is achieved by training the network on a large dataset. In [98] a Convolutional Neural Network (CNN) is successfully applied for fault classification. This approach relies on the S-transform of vibration signals into images displaying the time-frequency patterns in which the pattern recognition is performed. We can clearly observe that the common pattern recognition methods are relying on the assumption of a single fault scenario, and only a limited number of work proposes flexible and comprehensive solutions for multiple fault detection. In the presence of multiple faults, the single fault recognition techniques' performance may be degraded. In addition, the other machines operating in the motor's environment or the coupled subsystems, etc., may introduce further noise components in the measured signal. As a result, it may occur that one of the faults or noise components obscures a particular fault feature and makes it impossible to recognize it. Its further consequence is that the fault isolation operations may become rather difficult. However, the above brief literature review shows that only a few studies have been published on the investigation of the multilabel prediction performance of recent classifier algorithms in the presence of disturbing noises in the case of electrical signature analysis. One of the major drawbacks from adopting these algorithms to ESA based motor diagnoses is that they require a large training set, which is difficult and time-consuming to collect. In addition, each individual electrical machine and drive system would require their own type- and operation-specific pattern fault library. The feature extraction step raises further questions. The performance of a classifier strongly depends on the effectiveness of the features representing the patterns of different fault events.