Predictive Maintenance applications are increasingly complex, with interactions between many components. Black-box models are popular approaches based on deep-learning techniques due to their predictive accuracy. This paper proposes a neural-symbolic architecture that uses an online rule-learning algorithm to explain when the black-box model predicts failures. The proposed system solves two problems in parallel: (i) anomaly detection and (ii) explanation of the anomaly. For the first problem, we use an unsupervised state-of-the-art autoencoder. For the second problem, we train a rule learning system that learns a mapping from the input features to the autoencoder’s reconstruction error. Both systems run online and in parallel. The autoencoder signals an alarm for the examples with a reconstruction error that exceeds a threshold. The causes of the signal alarm are hard for humans to understand because they result from a non-linear combination of sensor data. The rule that triggers that example describes the relationship between the input features and the autoencoder’s reconstruction error. The rule explains the failure signal by indicating which sensors contribute to the alarm and allowing the identification of the component involved in the failure. The system can present global explanations for the black box model and local explanations for why the black box model predicts a failure. We evaluate the proposed system in a real-world case study of Metro do Porto and provide explanations that illustrate its benefits.

Predictive maintenance methods assist early detection of failures and errors in machinery before they reach critical stages. This study proposes a data-driven predictive maintenance framework for the air production unit (APU) system of a train of Metro do Porto by deep learning based on a sparse autoencoder (SAE) network that efficiently detects abnormal data and considerably reduces the false alarm rate. Several analog and digital sensors installed on the APU system allow the detection of behavioral changes and deviations from the normal pattern by analyzing the collected data. We implemented two versions of the SAE network in which we inputted analog sensors data and digital sensors data, and the experimental results show that the failures due to air leakage problems are predicted by analog sensors data while other types of failures are identified by digital sensors data. A low pass filter is applied to the output of the SAE network, and a sequence of abnormal data is used as an alarm for the APU system failure. Performance indicators of the SAE network with digital sensors data, in terms of F1 Score, Recall, and Precision, are respectively, about 33.6%, 42%, and 28% better than those of the SAE network with analog sensors data. For comparison purposes, we also implemented a variational autoencoder (VAE). The results show that SAE performance is better than that of VAE by 14%, 77%, and 37% respectively, for Recall, Precision and F1 Score.