Giacomo Guidotti

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.

With the growing demand for hybrid and electric vehicles, the accurate prediction of NVH (Noise, Vibration, and Harshness) behavior in Permanent Magnet Synchronous Machines (PMSMs) has become a critical aspect of electric motor design. This paper presents a detailed modeling approach for electromagnetic-induced noise and vibrations in PMSMs, integrating both analytical and numerical methods. The model focuses on quantifying the contributions of radial and tangential electromagnetic forces, which are key drivers of vibro-acoustic responses. The analytical part employs curved beam theory and a simplified acoustic model, offering rapid insights during early design stages. In parallel, a detailed numerical model based on finite element analysis is developed using a physics-based approach that accounts for the actual geometry and material properties of the PMSM prototype. This allows for enhanced accuracy without relying on experimental material parameter identification. Moreover, the detailed model includes the fluid–structure interaction introduced by the channels of the cooling fluid of the electric machine, which, although poorly addressed by the existing literature, was found to play a key role in driving the vibrational behaviour of the structure. By combining analytical speed with numerical precision, the proposed approach enables consistent and physically-based NVH predictions across various design phases, ultimately supporting improved electric machine performance and reducing development time and costs. Validation against experimental data confirms the ability of the model to accurately predict both sound pressure levels and housing surface vibrations. The novelty of this work lies in its integration of fluid–structure interaction and material modeling without the need for empirical parameter tuning, offering a robust tool for NVH design in electric vehicle applications.

The automotive industry is experiencing a period of transition from traditional internal combustion engine (ICE) vehicles to electric vehicles. Although electric machines have always been used in many applications, they are generally designed neglecting the sources of uncertainty, even such uncertainty can lead to significant deterioration of the motor performance. The aim of this paper is to compare the results obtained from the multi-objective optimization of an interior permanent magnet synchronous motor (IPMSM) using a robust approach versus a deterministic one. Unlike other studies in the literature, this research simultaneously considers different sources of uncertainty, such as geometric parameters, magnet properties, and operating temperature, to assess the variability of electric motor performance. Different designs of a 48 slot–8 pole motor are simulated with finite element analysis, then the outputs are used to train artificial neural networks that are employed to find the optimal design with different approaches. The method incorporates an innovative use of the neural network-based variance estimation (NNVE) technique to efficiently calculate the standard deviation of the objective functions. Finally, the results of the robust optimization are compared with those of the deterministic optimization. Due to the small margin of improvement in robustness, both methods lead to similar results.