Modal analysis and condition monitoring for an electric motor through MEMS accelerometers

Piezoelectric accelerometers are commonly employed for diagnosing machine faults, due to their accuracy. In the last few years, however, MEMS (Micro Electro-Mechanical Systems) accelerometers have attracted strong interest thanks to their low cost. In this work, a synchronous electric motor with an integrated MEMS sensor is studied and results are compared from both MEMS and piezoelectric sensors. A modal analysis is performed, using data from all available sensors. Comparing the frequency response functions and the natural frequencies shows the limitations of the MEMS sensor. One can then correct the MEMS measurements, by using global statistical parameters calculated on the data or by defining a “filter” function between the signals, thus improving the signal-to-noise ratio. It is found that MEMS sensors may replace piezoelectric ones for diagnostic applications. This way, an inexpensive measurement system (which needs to be calibrated only once, before installation, against higher-accuracy sensors) can be used for vibration monitoring of electric motors

A European Researchers’ Night project on mechanical vibrations for high school students

The present works were conceived to be exhibited during the 2022 European Researchers’ Night (ERN 2022), at the University of Modena and Reggio Emilia. The idea is to illustrate the key concepts of mechanical vibration through the use of 3D models and virtual simulation analysis. The paper is directed to high school students planning to enroll in a mechanical engineering bachelor’s degree, in order to approach or consolidate some fundamental concepts of mechanical vibration. Topics not easy to explain, such as the natural frequencies of a body, could be presented more effectively using physical models. Mathematical formalism will be kept to a minimum, as it is beyond the scope of this paper

Ottimizzazione Cinematica Multi-body e Calibrazione del Modello Cinematico della Mano per il Motion Capture

La mano umana è uno degli arti più complessi da analizzare dal punto di vista biomeccanico. La sua pletora di gradi di libertà indipendenti le conferisce capacità funzionali ineguagliabili. Tuttavia, questa stessa complessità presenta sfide sostanziali, in particolare nella modellazione cinematica e nel controllo. Il punto cruciale della ricerca sulla cinematica della mano ruota attorno all'individuazione della posizione e dell'orientamento dei centri di rotazione (CdR) delle 27 articolazioni della mano. Questa determinazione deriva principalmente dalle misurazioni di motion capture, che possono utilizzare varie tecniche, tra cui marcatori ottici, sistemi di misurazione inerziale e sistemi di telecamere senza marcatori. La creazione di un modello cinematico accurato è fondamentale per decifrare le complessità del movimento della mano. Nell'ambito del controllo, una sfida significativa consiste nel discernere le sinergie delle mani. Queste si riferiscono a gruppi di movimenti legati da determinate regole di dipendenza. Decostruendo i movimenti della mano in segmenti più afferrabili, l'obiettivo è quello di colmare la dicotomia tra la biomeccanica umana e la concettualizzazione di sofisticati sistemi artificiali. Questo ponte mira ad amplificare la loro efficienza operativa, la praticità e l'affinità con l'utente. Nell'ambito del controllo, una sfida significativa consiste nel discernere le sinergie delle mani. Queste si riferiscono a gruppi di movimenti legati da determinate regole di dipendenza. Decostruendo i movimenti della mano in segmenti più afferrabili, l'obiettivo è quello di colmare la dicotomia tra la biomeccanica umana e la concettualizzazione di sofisticati sistemi artificiali. Questo ponte mira ad amplificare la loro efficienza operativa, la praticità e l'affinità con l'utente.The human hand stands as one of the most intricate limbs to analyze from a biomechanical perspective. Its plethora of independent degrees of freedom endows it with unparalleled functional capabilities. However, this same complexity presents substantial challenges, particularly in kinematic modeling and control. The crux of hand kinematic research revolves around pinpointing the position and orientation of the Centers of Rotation (CoR) for the hand's 27 joints. This determination primarily derives from motion capture measurements, which can utilize various techniques, including optical markers, inertial measurement systems, and marker-less camera systems. Crafting an accurate kinematic model is paramount for deciphering hand motion intricacies. In the realm of control, a significant challenge lies in discerning hand synergies. These refer to clusters of movements bound by certain dependency rules. By deconstructing hand movements into more graspable segments, the objective is to bridge the dichotomy between human biomechanics and the conceptualization of sophisticated artificial systems. This bridge aims to amplify their operational efficiency, practicality, and user affinity. Consequently, the focus of this work will be twofold. Initially, we will conceptualize an optimization model to ascertain the positions and orientations of the hand's CoR. This endeavor aims to achieve the most precise kinematic model feasible. Following this, we intend to put forth a decomposition model for the hand's actions, targeting the identification of potential synergies. These synergies will aid in streamlining hand control and lay the foundation for designing prosthetics and exoskeletons that harmoniously align with the hand's biomechanics