Fault diagnosis is critical to any maintenance industry, as early fault detection can prevent catastrophic failures as well as be a waste of time and money. Traditional methods involve a high cost in time and people to identify failures, which has caused machine learning methods to grow in recent years, especially unsupervised learning methods. This paper proposes a model to cluster, identify, and diagnose six different failures in electric motors using only uniaxial acceleration signals. For this, the Gaussian mixture model is combined with the Mahalanobis distance. The proposed method is verified through a series of experiments with real electric motors. The results show that the proposed method is an efficient way to identify failures in electric motors, especially bearing, unbalanced, and mechanical loss failures. The accuracy presented values from 93.4 to 100 with an average accuracy of 97.9 considering all cases, showing that the GMM combined with the Mahalanobis distance was able to understand the pattern involved in the vibration signal of the engines. As the experiments are based on real electric motors and faults, the proposed method can be used for early detection of fault conditions.
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