Mechanical imbalances and bearing faults account for a large majority of the faults in a machine, particularly for small-medium size machines. Therefore, their diagnosis is an intensively investigated field of research. Recently, many research activities were focused on the diagnosis of bearing faults by current signals. This paper compares the bearing fault detection capability obtained with the vibration and current signals. The paper contribution is the use of a simple and effective signal processing technique for both current and vibration signals, and a theoretical analysis of the physical link between faults, modeled as a torque disturbance, and current components. The focus of the paper is on the theoretical development of the correlation between torque disturbances and the amplitude of the current components, together with a review of fault models used in the literature. Another contribution is the re-creation of realistic incipient faults and their experimental validation. Radial effects are visible only in case of large failures that result in air-gap variations. Experiments are reported that confirm the proposed approach.

Generalized roughness is the most common damage occurring to rolling bearings. It produces a frequency spreading of the characteristic fault frequencies, thus making it difficult to detect with spectral or envelope analysis. A statistical analysis of typical bearing faults is proposed here in order to identify the spreading bandwidth related to specific conditions, relying on current or vibration measurements only. Then, a diagnostic index based on the computation of the energy in the previously defined bandwidth is used to diagnose bearing faults. The proposed method was validated experimentally with vibration signals, with robust and reliable results. The same procedure can be extended to current signals.

This paper investigates the relationship between vibration and current in induction motors operated under external vibrations. Two approaches are usually available to define this relationship. The former is based on airgap variations, while the latter is based on torque perturbation. This paper is focused on the airgap variation model. The ball bearing fault is modeled by contact mechanics. External vibrations often occur in many industrial applications where externally induced vibrations of suitable amplitude cause cyclic radial loading on the machine shaft. The model is validated by experiments, owing to a dedicated test setup, where an external vibration source (shaker) was employed, together with ball bearing alterations in order to decrease the stiffness of the support along the radial direction. To maximize the effects of externally induced vibrations, the frequency chosen was near the flexural resonance of the rotor (determined by finite-element method analysis). The direction of the external vibration is radial with respect to the axis of the electric machine under test. During tests, both stator phase currents and vibration of the machine were sampled. The test setup allowed one to vary the machine speed and load, vibration amplitude, and bearing stiffness (damage level). Radial effects are usually visible only in the case of large failures that result in significant airgap variations, as confirmed by experiments.

Electric linear motors are spreading in industrial automation because they allow for direct drive applications with very high dynamic performances, high reliability, and high flexibility in trajectory generation. The moving part of the motor is linked to the fixed part by means of linear bearings. As in many other electric machines, bearings represent one of the most vulnerable parts because they are prone to wear and contamination. In the case of linear roller bearings, this issue is even more critical as the rail cannot be easily fully enclosed and protected from environmental contamination, unlike the radial rotating bearing counterpart. This paper presents a diagnostic method based on vibration analysis to identify which signature is related to a specific fault.

Internal permanent-magnet synchronous machines are spreading in industrial production. They feature high torque density and extended speed range that are key issues in many fields of applications, however their cogging torque is typically quite high. Many methods and design guidelines for cogging torque reduction exist in literature and this paper compares them. For this purpose, the different design guidelines are applied to a common reference machine in order to assess their effectiveness. Computer finite element analysis (FEA) are carried out for each case in order to compare the cogging torque reduction capability of the different techniques. The side effects of these techniques, such as back-EMF and rated torque profile distortions, will be taken into account. The paper contribution is to compare the various cogging torque reduction techniques and magnetic geometries on a common reference machine to identify the most effective ones.

This paper focuses on modeling and experimental validation of a diagnostic fault classification procedure for interturn fault detection in permanent-magnet (PM) multiphase machines designed for fault-tolerant electric drives. The diagnostic procedure is based on the symmetrical component theory and relies upon the combined space vector vector D that gathers information from the two original space vectors obtained with different reference frames. The diagnostic index effectiveness and robustness were also investigated against other fault types such as rotor eccentricities and magnet damage to assess its discrimination capability. The proposed procedure was experimentally evaluated for the interturn fault case on a five-phase PM machine. Experiments were carried out at different speed and load levels, with increasing numbers of short-circuited turns. Both simulation and experimental results demonstrated the feasibility of the proposed diagnostic method.