## 1 Introduction  
Nowadays, automated production has become a trend. The number of unmanned factories is increasing, which means that the stability requirements of machinery and equipment are also increasing. Therefore, failure analysis of the motor and how to determine the type of failure has become an important subject. In general, motor failures are divided into two categories: electrical and mechanical failures. Most mechanical damage occurs in stators, rotors and bearings. Among them, bearing failure is the most common as shown in Table 1 [1].  
In this research, the current signal amount was measured and analyzed under different motor conditions. Current signal was selected for measurement because it was less affected by vibration and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Failure Type** & **Occurrence Percentage** \\ \hline Bearing damage & 45\% \\ Stator damage & 35\% \\ Rotor damage & 10\% \\ Others damage & 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proportion of motor failure types.  
temperature signals. It also could be used for online monitoring to understand the motor operating status in real time. There are several studies focusing on data analysis methods, such as fast Fourier transform (FFT), wavelet transform (WT) and Hilbert-Huang transform (HHT). However, data of FFT need periodicity and selection of wavelet functions is difficult for wavelet multianalysis [2; 3]. The HHT use empirical mode decomposition (EMD) to decompose the signal and allow intrinsic mode functions (IMF) to hold unique frequency information that can capture more useful features.  
In recent years, measurement signals have been converted by analysis methods and classified by algorithms such as artificial neural network (ANN) [4], probability neural network (PNN) [5; 6] and backpropagation neural network (BPNN) [7; 8]. These have been frequently used in different fields. For the BPNN, its neurons are set in advance. The number of the neurons and the learning rate are extremely important. The gradient method is used to find the best solution. Therefore, this research uses BPNN for discussion.  
In [9], [PERSON], [PERSON] and [PERSON], [PERSON] proposed a fast correlation-based filter (FCBF) feature-selection method that uses symmetric uncertainty (SU)--instead information gain (IG)--to determine the correlation between features and categories, or whether there is redundancy between the features. This study was done through the calculation of the SU value between features and categories, features and features, and compared with the results of Relieff feature selection proposed by [PERSON] in 1994. The FCBF is one of the feature-weighting algorithms [10] to address complex and multi-category situations. The comparison results show that the FCBF method is faster than other methods and obtains corresponding classification results. Furthermore, if a higher correlation threshold is set, the efficiency of the FCBF can be further improved. In addition, the FCBF method can obtain a better classifier recognition result by changing the feature selection order [11].  
In this study, particle-swarm optimization (PSO) is applied to optimize the weights of important features after screening. Inspired by the observation of swarming bird behavior during foraging, it is used to find the best solution to improve the accuracy (Acc) of the classifier. The PSO is a group-based optimization search technology, similar to a genetic algorithm (GA) [12]. PSO and GA have ability to randomly initialize, and both use adaptive values for random search. However, although there is no guarantee that the best solution can be found, for PSO, it has memory.