Rotating electrical machines greatly suffer from insulation degradation and failure. The adoption of wide band-gap semiconductors in Variable-Frequency Drives (VFDs) hinder the utilization of traditional techniques such as on-line Partial Discharges (PD) to monitor and diagnose the insulation status and failure. Alternative techniques exploiting the broadband frequency behaviour of machine windings, and the increased high-frequency content of VFD excitation arise as a valid alternative. The present work provides a comprehensive analysis and review of cutting-edge broadband frequency techniques aimed to diagnose winding insulation degradation and faults. First, the broadband nature of machine windings is illustrated by explaining the per-turn equivalent circuit and the physical interpretation of its parameters. Then, the different insulation monitoring and diagnosis methods are thoroughly reviewed. The analysed methods include the Frequency Response Analysis (FRA) applied to rotating electrical machines, the analysis of Common Mode (CM) broadband impedance and parasitic capacitances, and methodologies based on the analysis of transient high-frequency current ringing. Specific focus is placed on the instrumentation, which often represents the main applicability limitations.

The analysis of electrical machine faults during start-up, and variable speed and load conditions offers numerous advantages for fault detection and diagnosis. In this context, diagnosing localized bearing faults through vibration signals remains challenging, particularly in developing physically meaningful, simple, and resampling-free techniques to monitor fault characteristic components throughout machine start-up. This study introduces a straightforward method for qualitatively identifying the time-frequency evolutions of localized bearing faults during the start-up of an inverter-fed machine. The proposed technique utilizes the time-frequency representation of the envelope spectrum, effectively highlighting characteristic fault frequencies during transient operation. The method is tested in an open-source dataset including transient vibration signals. In addition, the work studies the method limitations induced by the mechanical transfer path, when the bearing surroundings are not directly accessible for vibration acquisition. The proposed methodology efficiently identifies incipient localized bearing faults during inverter-fed machine start-up when the fault signature is not highly attenuated.

This article presents and validates a method for early estimation of the high-frequency impedance response for a real permanent magnet synchronous machine. The main contribution of this work lies in the simplicity of the data required to tackle the prediction, which do not require any experimental tuning or 3-D simulations. The required inputs for the method are the 2-D cross section of the machine and the material characteristics of the conductors, insulation, and iron laminations. An approach using the finite element method coupled with a circuit simulator is utilized to obtain the machine common-mode and differential-mode impedances over a broad frequency range. Experimental measurements are performed up to the resonance band to validate the methodology. In addition, the influence of the machine rotor and housing on the impedance curves is experimentally assessed. A machine with concentrated winding is used as a case study machine to test the model.

Bearings are the most vulnerable component in low-voltage induction motors from a maintenance standpoint. Vibration monitoring is the benchmark technique for identifying mechanical faults in rotating machinery, including the diagnosis of bearing defects. The study of different bearing fault phenomena under induction motor transient conditions offers interesting capabilities to enhance classic fault detection techniques. This study analyzes the low-frequency localized bearing fault signatures in both the inner and outer races during the start-up and steady-state operation of inverter-fed and line-started induction motors. For this aim, the classic vibration envelope spectrum technique is explored in the time–frequency domain by using a simple, resampling-free, Short Time Fourier Transform (STFT) and a band-pass filtering stage. The vibration data are acquired in the motor housing in the radial direction for different load points. In addition, two different localized defect sizes are considered to explore the influence of the defect width. The analysis of extracted low-frequency characteristic frequencies conducted in this study demonstrates the feasibility of detecting early-stage localized bearing defects in induction motors across various operating conditions and actuation modes.

Fault diagnosis techniques applied to synchronous motors such as Permanent Magnet Synchronous Machines (PMSMs) and Synchronous Reluctance Machines (SynRMs) are scarcely addressed in the literature, in strong contrast to the attention paid to asynchronous motors. In addition, the most widespread techniques are those based on steady-state condition analysis, and little attention is paid to detection during transient operation. The present paper aims to identify research gaps on the topic and to demonstrate the potential of transient analysis. First, the different diagnostic methodologies in literature are thoroughly analyzed. Then, two laboratory case studies are presented to demonstrate the potential of fault detection under non-stationary conditions for a PMSM and a SynRM. Stator current analysis is performed by building time–frequency maps to analyze the evolution of different fault indicators. The results show clear differences between healthy and faulty conditions during the transient regime.

Misalignment faults in drive systems occur when the motor and load are not properly aligned, leading to deviations in the centerlines of the coupled shafts. These faults can cause significant damage to bearings, shafts, and couplings, making early detection essential. Traditional diagnostic techniques rely on vibration monitoring, which provides insights into both mechanical and electromagnetic fault signatures. However, its main drawback is the need for external sensors, which may not be feasible in certain applications. Alternatively, motor current signature analysis (MCSA) has proven effective in detecting faults without requiring additional sensors. This study investigates misalignment faults in synchronous reluctance motors (SynRMs) by analyzing both vibration and current signals under different load conditions and operating speeds. Fast Fourier transform (FFT) is applied to extract characteristic frequency components linked to misalignment. Experimental results reveal that the amplitudes of rotational frequency harmonics ($1x{f}_r$, $2x{f}_r$, and $3x{f}_r$) increase in the presence of misalignment, with $1x{f}_r$ exhibiting the most stable progression. Additionally, acceleration-based vibration analysis proves to be a more reliable diagnostic tool compared to velocity measurements. These findings highlight the potential of combining current and vibration analysis to enhance misalignment detection in SynRMs, improving predictive maintenance strategies in industrial applications.