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Resumo-Este artigo apresenta uma forma de realizar o diagnóstico de falhas em máquinas rotativas através da análise de sinais de vibração. O presente trabalho realiza a classificação de 9 tipos de defeitos que acontecem em motores: desbalanceamento; desalinhamento paralelo horizontal; desalinhamento paralelo vertical; defeito na gaiola, na esfera e na pista externa do mancal não invertido; defeito na gaiola, na esfera e na pista externa do mancal invertido. A separação dos conjuntos de treinamento e testeé feita através do método k-fold. O algoritmo usado para fazer a classificaçãoé o Random Forest que atingiu uma acurácia de 97,48%.Palavras-Chave-Diagnóstico de falhas, k-fold, Random Forest.Abstract-This article introduces a way to realize the fail diagnosis in rotation machines analysing vibrations signals. The present work realizes the classification of 9 types of faults that happen in motors: unbalance; horizontal parallel misalignment; vertical parallel misalignment; defect in the cage, in the roller and in the outer track of non inverter bearing; defect in the cage, in the roller and in the outer track of inverter bearing. The separation of train and test sets is made by the k-fold method. The algorithm used to make the classification is the Random Forest that reached an accuracy of 97,48%.

Predictive maintenance has been employed to reduce maintenance costs and production losses and to prevent any failure before it occurs. The framework proposed in this work performs diesel engine prognosis by evaluating the absolute value of the failure severity using random forest (RF) and multilayer perceptron (MLP) neural networks. A database was implemented with 3500 failure scenarios to overcome the problem of inducing destructive failures in diesel engines. Diesel engine failure signals were developed with the zero-dimensional thermodynamic model inside a cylinder coupled with the crankshaft torsional vibration model. Artificial neural networks and random forest regression models were employed for classifying and quantifying failures. The methodology was applied alongside an engine simulator to assess effectiveness and accuracy. The best-fitting performance was obtained with the random forest regressor with an RMSE value of 0.10 ± 0.03%.

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Gearboxes are widely used in various industries such as aircrafts, automobiles, wind turbines, ship industries among others. Due its complex configuration, it is a challenging task to identify fault and failures patterns. Its internal components, such as bearings and gears, have different fault patterns, that can appear in one or in both components. The vibration signals were processed using the Empirical Mode Decomposition (EMD) and the Pearson Correlation Coefficient (PCC) to select the significant IMFs and then 18 features were extract from this IMFs. Four features ranking techniques (ReliefF, Chi-Square, Max Relevance Min Redundancy (mRMR) and Decision Tree) were used in a committee to select the best feature set, among the 10 with the highest rank, that appears at least in 3 of the 4 methods. The new feature set was used as an input to Support Vector Machine (SVM), Random Forest (RF) and Artificial Neural Networks (ANN) algorithms. The results showed that the use of the PCC value as a tool for selecting the significant IMFs, combined with the feature committee led to good results for this classification problem. In this case study, the ANN model outperformed the SVM and the RF algorithms, by using only 4 features to achieve 95.42% of accuracy and 6 features to achieve 100% of accuracy.