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In industry 4.0, Machine Learning coupled with sensors monitoring leverages new ways to optimise maintenance strategies. In a predictive maintenance case, failure diagnoses are an excellent way to prevent any breakdowns. Up to now, failure diagnoses are focused on the classification of only one failure among many (multi-label classification), even if multiple failures can occur simultaneously. This study proposes an extension to classify simultaneous failures with the most popular classification methods such as random forests or artificial neural networks. Validated on a public predictive maintenance dataset, our methodology achieved classification with equal or best accuracy compared to multi-label classification.