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In the current study, an automatic and interpretable predictive maintenance system is proposed. The system provides a fully automatic training process for predictive maintenance models without human intervention. On the other hand, as failure reasons are critical for product development. The proposed pipeline also demonstrates the interpretation on automatic trained model to present insights for engineers to acquire mechanism of interested events. To study the system, four automatic machine learning methods and two interpretation modules are evaluated for the pipeline with Isuzu’ real vehicle data correspondingly. The overall performance of the automatic and interpretable system is demonstrated as well. Key words: predictive maintenance, AutoML, interpretation