PredMaX: Predictive maintenance with explainable deep convolutional autoencoders - Hajgató, Gergely; Wéber, Richárd; Szilágyi, Botond; Tóthpál, Balázs; Gyires-Tóth, Bálint; Hős, Csaba (Advanced engineering informatics, 2022)
A novel data exploration framework (PredMaX) for predictive maintenance is introduced in the present paper. PredMaX offers automatic time period clustering and efficient identification of sensitive machine parts by exploiting hidden knowledge in high-dimensional, unlabeled temporal data. Condition monitoring systems often provide such data, which is further analyzed by human experts or used for training predictive models. PredMaX reduces data dimensionality in two steps: An explainable deep convolutional autoencoder is applied on the data first, followed by principal component analysis. The automatic clustering is performed in the latent space of the autoencoder, ensuring higher accuracy than the clustering in the space of principal components. If clusters of normal and abnormal operation are known, the reasoning module is able to reveal the measurement channels that contributed the most to the latent representation moving from normal to abnormal operation. Beyond the detailed presentation of the PredMaX approach, the paper presents the case study of identifying the most important signals that can be used for predicting oil degradation in an industrial gearbox. The case study is performed on a data-driven basis with minimal human assistance and without preliminary knowledge of the machine. [Display omitted] •High-dimensional temporal data is clustered in the latent space of an autoencoder.•Explainable neural networks are used to identify sensitive machine parts.•Clustered data are visualized after sequential dimension reduction in a concise way.•The effectiveness of the methodology is presented in a real-life case study.