# Detection of Broken Bars in Induction Motors Using Histogram Analysis of Current Signals
## 4 Conclusions  
In this work, we proposed the detection of 1 BRB and 2 BRB faults in induction motors by applying statistical analysis of one phase's electrical current in the time domain. The main contribution is to provide a low-complexity method combining local statistical methods and standard machine learning classifiers that could be quickly and easily embedded for real-time fault detection. The analysis of nine texture characteristics with different displacements demonstrates the careful comparison of the current behavior varying the sampling points and, consequently, the resolution of the features. The three tests performed help to find the most relevant feature and distances, obtaining 98.16% of global performance for three motor condition classifications using a bagged decision Tree. Moreover, regression analysis shows that our method obtained an acceptable accuracy of 88.15% using only 50 samples but also achieved a percentage of 98.05% for 250 samples, reducing the amount of data to process. Compared to related works, our method shows high classification results using electrical current acquired from an induction motor, avoiding high-computational-cost classifiers and optimizing the number of samples.  
This method could include additional motor faults that the proposed multi-displacement analysis of textures can easily characterize. As a future work, this method could include multiple motor faults by considering three-phase current signals to detect short circuits in the motor coils and impedance estimation by analyzing the offset between voltage and current signals.  
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline
**Work** & **Methodology** & **Fault** & **Signal Type** & **Domain** & **Accuracy** \\ \hline [PERSON] et al. [31] & 1. Fourier transform & HBRB & & & Time & \\  & 2. CNN as estimator & 1 BRB & & & Frequency & 100\% \\  & and classifier & & & & & \\ \hline [PERSON] et al. [22] & 1. Histogram of & & & & & \\  & Oriented Gradients & & & & & \\  & 2. Bayesian classifier. & & & & & \\ \hline [PERSON] et al. [32] & 1. MEEMD energy & & & & & \\  & entropy & & & & & \\  & 2. Artificial neural & & & & \\  & network & & & & \\ \hline [PERSON] et al. [33] & 1. Contrast calculation & 1 BRB & & & & \\  & 2. Fuzzy logic & & 2 BRB & & & \\ \hline Our approach & 1. SDH. & & & & & \\  & 2. Texture features. & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with related works.  
**Author Contributions:** Conceptualization, D.-L.A.-O. and M.-A.I.-M.; Methodology, V.H.-R. and M.-A.I.-M.; Software, V.H.-R.; Validation, J.-L.C.-H. and M.-A.I.-M.; Formal analysis, V.H.-R., D.-L.A.-O. and M.-A.I.-M.; Investigation, J.-J.C.-C.; Resources, J.-L.C.-H.; Data curation, V.H.-R.; Writing--original draft, V.H.-R.; Writing--review & editing, D.-L.A.-O., J.-J.C.-C. and J.-L.C.-H.; Visualization, D.-L.A.-O.; Supervision, D.-L.A.-O. and M.-A.I.-M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CONAHCyT grants 738317 and 809170. Universidad de Guanajuato funded the APC under POA 2023.  
Not applicable.  
**Data Availability Statement:** Motor signals database and their corresponding diagnosis were provided in collaboration with national and international partners based on a privacy agreement with the University of Guanajuato. The agreement avoids publicly sharing or distributing any kind of data.  
The authors declare no conflict of interest. Also, the funders had no role in the study's design, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.