# Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification
## 3 System Description and Experiment
### Feature Extraction and Dataset Preparation for Training  
Stator current signals and vibration data picked for fault-free no load and 40% load of full load at the speed of 1500 rpm conditions with fs = 10000 sampling rate and t = 20 s duration in the presence of both mechanical faults. Unbalance results in high-frequency amplitudes at frequencies at once the rotational speed. The typical fault features of misalignment are dominant frequencies at one or two times the rotational speed depending on the degree of angular misalignment and the type of the couplings. Spectral images also display sub-harmonic multiplies of 1/2 xRPM. However, the different speeds, loads, motor parameters, and operational setups, etc., may affect the fault frequencies. Differences between faulty and healthy conditions can immediately be observed from vibration spectra during the measurement (see Figure 4). It is clearly visible in the spectrum that the machine vibrates strongly around 25 Hz, which indicates a shaft misalignment. From the generator current spectrum, a 50 Hz component appeared in the spectra, while in the motor current spectrum a 75 Hz component (see Figure 5). The reason is that a generator is a 4-pole machine, while the motor has six poles. This indicates exactly that the machine vibrates at 25 Hz. The unbalancing load is placed at the generator's side, so the flexible bellows coupling may attenuate the fault signature in its spectra. For fault extraction, we can also apply the Thomson multitaper spectral estimates that are proven to be efficient in the case of weaker signals [26] and combine the beneficial properties of high resolution and low variance. Figure 6 displays the Thomson multitaper spectrum of the generator's current signal in which we can observe how the distinguished magnitudes are emphasized. This allows a fast, automatic extraction of the peaks. Figure 3: Block scheme of the data acquisition system. Figure 4: Fast Fourier Transform spectrum (upper chart) and time-domain signal (lower chart) of vibration data displayed online by the LabVIEW software during the measurement. A1 (white line) denotes the generator’s vibration signal, and A2 (red line) stands for the motor’s vibration signal. Figure 5: Fast Fourier Transform (FFT) spectra of the motor current (_Im_) stands for the motor’s current component \(a\). The features carrying most of the information of interest are mainly in the frequency domain. However, we have inspected a few time-domain features also. The feature vector is composed of the magnitudes of the distinguished sideband frequencies and RMS variance frequencies from the spectra for all three phases of both machines. The time-domain features cover the form factor, kurtosis, and entropy deviation from the fault-free sample. The fault feature vector also includes the distance calculated between the observed signature and each pure signature associated with the identified fault [121; 122; 123]. It is worth noting that many attempts have been made with the purpose of improving performance through sophisticated feature extraction methods. In his seminal paper on recognition of diagnostic signals based on acoustic signals, [PERSON] and [PERSON] [124] have introduced a shortened method of frequencies selection. In [125], a novel method of selection of amplitudes of frequencies ratio 50-s frequency coefficient was introduced in order to build the feature vector. Recently, the new Method of Selection of Amplitudes of Frequency Ratio of 24% Multiexpanded Filter 8 Hz and k-means clustering approach has been presented in [126]. This technique draws our attention to investigate the applicability of acoustic-based fault feature extraction techniques to ESA in our future work. The dataset is built by synthetically adding a random number of ten to twenty of external disturbing frequency components for the faulty and fault free signals.