Abstract
Early identification of failures is a critical task in predictive maintenance, preventing potential problems before they manifest and resulting in substantial time and cost savings for industries. We propose an approach that predicts failures in the near future. First, a deep learning model combining long short-term memory and convolutional neural network architectures predicts signals for a future time horizon using real-time data. In the second step, an autoencoder based on convolutional neural networks detects anomalies in these predicted signals. Finally, a verification step ensures that a fault is considered reliable only if it is corroborated by anomalies in multiple signals simultaneously. We validate our approach using publicly available Air Production Unit (APU) data from Porto metro trains. Two significant conclusions emerge from our study. Firstly, experimental results confirm the effectiveness of our approach, demonstrating a high fault detection rate and a reduced number of false positives. Secondly, the adaptability of this proposal allows for the customization of configuration of different time horizons and relationship between the signals to meet specific detection requirements.

Abstract
To maintain the performance of the latest generation of onshore and offshore wind turbine systems, a new methodology must be proposed to enhance the maintenance policy. In this context, this paper introduces an approach to designing a decision support tool that combines predictive capabilities with anomaly explanations for effective IoT predictive maintenance tasks. Essentially, the paper proposes an approach that integrates a predictive maintenance model with an explicative decision-making system. The key challenge is to detect anomalies and provide plausible explanations, enabling human operators to determine the necessary actions swiftly. To achieve this, the proposed approach identifies a minimal set of relevant features required to generate rules that explain the root causes of issues in the physical system. It estimates that certain features, such as the active power generator, blade pitch angle, and the average water temperature of the voltage circuit protection in the generator's sub-components, are particularly critical to monitor. Additionally, the approach simplifies the computation of an efficient predictive maintenance model. Compared to other deep learning models, the identified model provides up to 80% accuracy in anomaly detection and up to 96% for predicting the remaining useful life of the system under study. These performance metrics and indicators values are essential for enhancing the decision-making process. Moreover, the proposed decision support tool elucidates the onset of degradation and its dynamic evolution based on expert knowledge and data gathered through Internet of Things (IoT) technology and inspection reports. Thus, the developed approach should aid maintenance managers in making accurate decisions regarding inspection, replacement, and repair tasks. The methodology is demonstrated using a wind farm dataset provided by Energias De Portugal.

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