Advanced Artificial Intelligence for Fault Detection of Electric Motors

The growing need to assess the health of components has made fault detection an increasingly important research topic in recent years. While traditional techniques remain widely used, the expansion of artificial intelligence (AI) has introduced innovative approaches. Among these, autoencoders have demonstrated significant potential for detecting anomalies in electric motors. The aim of this paper is to identify which signals are most suitable for AI-based fault detection in permanent magnet synchronous motors (PMSMs). To achieve this, in addition to analyzing acceleration signals, which are commonly studied in the literature, this work broadens the investigation by including current, voltage, and temperature signals acquired from different positions. The proposed method is tested during endurance tests, where motors operate under highly variable and demanding operating conditions. The collected signals are then analyzed using a 1D convolutional neural network autoencoder (1D CNN AE) to detect possible faults. The results highlight the importance of considering not only acceleration but also alternative monitoring signals. In particular, temperature measurements proved to be crucial for identifying and localizing specific faults, while current and voltage provided valuable insights into motor behavior, such as changes in control strategy, even when they are not directly correlated with fault occurrence.