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.