Healthy Motor Research Articles

Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis

Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF). The dataset was generated from three-phase signals of an induction motor controlled by a Direct Torque Controller under various operating conditions (20–1500 rpm with 0–100% load), resulting in 4251 images. The model, based on a Visual Geometry Group (VGG) architecture with 19 layers, achieved an overall accuracy of 98%, with specific accuracies of 99% for RAF, 100% for BRB, 100% for RBF, and 95% for BBF. A new model interpretability was assessed using explainability techniques, which allowed for the identification of specific learning patterns. This analysis introduces a new approach by demonstrating how different convolutional blocks capture particular features: the first convolutional block captures signal shape, while the second identifies background features. Additionally, distinct convolutional layers were associated with each fault type: layer 9 for RAF, layer 13 for BRB, layer 16 for RBF, and layer 14 for BBF. This methodology offers a scalable solution for predictive maintenance in induction motors, effectively combining signal processing, computer vision, and explainability techniques.

Broken Rotor Bar Fault Detection in Induction Motor Based on Spectral Analysis

Three-phase induction motors are among the most frequently used motors in industrial areas due to their simple structure, low cost, power specifications, etc. Electric energy consumption from these motors accounts for 68% of the energy used by all motors. The faults that occur in these motors over time decrease motor efficiency and result in significant energy consumption. In this study, a new method based on spectral subtraction (SS) was suggested for determining broken rotor bar (BRB) faults in these motors. The traditional method of motor current signature analysis via Fast Fourier Transform (FFT) is hard to use to diagnose BRB faults because the sideband characteristics used as a fault indicator are also seen in the healthy state at low amplitude levels. In the proposed fault detection method, the FFT of both healthy case and faulty case current signals were calculated and then the SS signal is obtained by subtracting the FFT of the healthy motor from the faulty motor for each time step. In the residual SS signal, fault detection was performed by examining the amplitude levels of the harmonic component of the BRB fault. Experimental results indicate that BRB faults can be successfully detected in squirrel-cage rotor induction motors using the suggested method.

Detection of Broken Bars in Induction Motors Operating with Closed-Loop Speed Control

Rotor bar breakage in induction motors is often detected by analysing the signatures in the stator current. However, due to the alteration of the current spectrum, traditional methods may fail when inverter-fed motors operate with closed-loop control using a cascade structure to regulate the speed. In this paper, the potential of zero-sequence voltage analysis to detect this fault is investigated, and a new index to quantify the severity of the fault based on this signal is proposed. Signals from motors operating under different control strategies and signals from motors powered from the mains are considered to verify the robustness of the proposed fault severity index. As a result, in all the analysed conditions the value of the proposed index for the healthy motor is found to be approximately 0.010, while for the faulty machine it is between 0.110 and 0.252.

Clinical assessment and striatal dopaminergic activity in healthy controls and patients with Parkinson's disease: a Bayesian approach.

We aimed to evaluate correlations between striatal dopamine transporter (DAT) uptake and clinical assessments in both patients with Parkinson's disease (PD) and healthy controls. This study enrolled 193 healthy controls, and 581 patients with PD. They underwent various clinical assessments and 123I-FP-CIT SPECT scans. After reconstruction, attenuation correction, and normalization of SPECT images, counts were measured from the bilateral caudate and putamen, and the occipital cortex for reference. Count densities for each region were extracted and used to calculate striatal binding ratios (SBRs) for each striatal region. SBR is calculated as (target region/reference region)-1. After logarithmic transformation of striatal SBRs, we analyzed the effects of clinical assessments on striatal SBRs using Bayesian hierarchical modeling. MDS-UPDRS total score, part I, part II, part III, Epworth Sleepiness Scale, REM sleep behavior disorder screening questionnaire, SCOPA-AUT total score were negatively associated with striatal SBR in patients with PD. Also, HVLT recognition discrimination was positively associated with striatal SBR in both healthy controls and patients with PD. In healthy control, MDS-UPDRS part II, MOCA, SCOPA-AUT total score were positively associated with striatal SBR. We demonstrated that motor symptom, sleep disturbance, autonomic symptom, and cognition of patients with PD were associated with striatal dopaminergic activity. In healthy controls, motor symptoms, autonomic symptom, and cognition were associated with striatal dopaminergic activity, some of which showing the opposite direction with patients with PD. This result might provide new insight to underlying mechanism of dopamine system with motor and non-motor assessments.

Wearable Technology and Its Influence on Motor Development and Biomechanical Analysis.

The convergence among biomechanics, motor development, and wearable technology redefines our understanding of human movement. These technologies allow for the continuous monitoring of motor development and the state of motor abilities from infancy to old age, enabling early and personalized interventions to promote healthy motor skills. For athletes, they offer valuable insights to optimize technique and prevent injuries, while in old age, they help maintain mobility and prevent falls. Integration with artificial intelligence further extends these capabilities, enabling sophisticated data analysis. Wearable technology is transforming the way we approach motor development and maintenance of motor skills, offering unprecedented possibilities for improving health, performance, and quality of life at every stage of life. The promising future of these technologies paves the way for an era of more personalized and effective healthcare, driven by innovation and interdisciplinary collaboration.

Cortical beta oscillations help synchronise muscles during static posture holding in healthy motor control

How cortical oscillations are involved in the coordination of functionally coupled muscles and how this is modulated by different movement contexts (static vs dynamic) remains unclear. Here, this is investigated by recording high-density electroencephalography (EEG) and electromyography (EMG) from different forearm muscles while healthy participants (n = 20) performed movement tasks (static and dynamic posture holding, and reaching) with their dominant hand. When dynamic perturbation was applied, beta band (15–35 Hz) activities in the motor cortex contralateral to the performing hand reduced during the holding phase, comparative to when there was no perturbation. During static posture holding, transient periods of increased cortical beta oscillations (beta bursts) were associated with greater corticomuscular coherence and increased phase synchrony between muscles (intermuscular coherence) in the beta frequency band compared to the no-burst period. This effect was not present when resisting dynamic perturbation. The results suggest that cortical beta bursts assist synchronisation of different muscles during static posture holding in healthy motor control, contributing to the maintenance and stabilisation of functional muscle groups. Theoretically, increased cortical beta oscillations could lead to exaggerated synchronisation in different muscles making the initialisation of movements more difficult, as observed in Parkinson's disease.

Inflammasome-Inhibiting Nanoligomers Are Neuroprotective against Space-Induced Pathology in Healthy and Diseased Three-Dimensional Human Motor and Prefrontal Cortex Brain Organoids.

The microgravity and space environment has been linked to deficits in neuromuscular and cognitive capabilities, hypothesized to occur due to accelerated aging and neurodegeneration in space. While the specific mechanisms are still being investigated, spaceflight-associated neuropathology is an important health risk to astronauts and space tourists and is being actively investigated for the development of appropriate countermeasures. However, such space-induced neuropathology offers an opportunity for accelerated screening of therapeutic targets and lead molecules for treating neurodegenerative diseases. Here, we show a proof-of-concept high-throughput target screening (on Earth), target validation, and mitigation of microgravity-induced neuropathology using our Nanoligomer platform, onboard the 43-day SpaceX CRS-29 mission to the International Space Station. First, comparing 3D healthy and diseased prefrontal cortex (PFC, for cognition) and motor neuron (MN, for neuromuscular function) organoids, we assessed space-induced pathology using biomarkers relevant to Alzheimer's disease (AD), frontotemporal dementia (FTD), and amyotrophic lateral sclerosis (ALS). Both healthy and diseased PFC and MN organoids showed significantly enhanced neurodegeneration in space, as measured through relevant disease biomarkers, when compared to their respective Earth controls. Second, we tested the top two lead molecules, NI112 that targeted NF-κB and NI113 that targeted IL-6. We observed that these Nanoligomers significantly mitigate the AD, FTD, and ALS relevant biomarkers like amyloid beta-42 (Aβ42), phosphorylated tau (pTau), Kallikrein (KLK-6), Tar DNA-binding protein 43 (TDP-43), and others. Moreover, the 43-day Nanoligomer treatment of these brain organoids did not appear to cause any observable toxicity or safety issues in the target organoid tissue, suggesting good tolerability for these molecules in the brain at physiologically relevant doses. Together, these results show significant potential for both the development and translation of NI112 and NI113 molecules as potential neuroprotective countermeasures for safer space travel and demonstrate the usefulness of the space environment for rapid, high-throughput screening of targets and lead molecules for clinical translation. We assert that the use of microgravity in drug development and screening may ultimately benefit millions of patients suffering from debilitating neurodegenerative diseases on Earth.

Oral Motor Development in Children Aged 0-12 Months: A Review of Functional and Therapeutic Aspects

This study examines oral motor development in children aged 0-12 months with a focus on functional and therapeutic aspects. The purpose of the study is to understand the pattern of oral motor development, influencing factors, and evaluate the effectiveness of various therapeutic methods applied. The qualitative research method was used by collecting data through in-depth interviews, participatory observations, and document analysis at school. The results of the study showed that the initial suction reflex and stimulation from the environment played an important role in oral motor development. Oral muscle exercises and the use of technology such as oral vibrators have been shown to be effective in strengthening the oral muscles, improving chewing and speaking skills in children. However, there is controversy regarding the use of therapeutic tools, with some parents and therapy professionals preferring the natural approach. This study emphasizes the importance of parental education and their active involvement in the therapy process for optimal outcomes. The implications of this study are the need for a holistic approach and timely interventions to support healthy oral motor development, as well as the importance of flexibility in choosing a therapy method that suits the specific needs of the child.

Broken Rotor Bar Detection Based on Steady-State Stray Flux Signals Using Triaxial Sensor with Random Positioning.

This paper investigates the detection of broken rotor bar in squirrel cage induction motors using a novel approach of randomly positioning a triaxial sensor over the motor surface. This study is conducted on two motors under laboratory conditions, where one motor is kept in a healthy state, and the other is subjected to a broken rotor bar (BRB) fault. The induced electromotive force of the triaxial coils, recorded over ten days with 100 measurements per day, is statistically analyzed. Normality tests and graphical interpretation methods are used to evaluate the data distribution. Parametric and non-parametric approaches are used to analyze the data. Both approaches show that the measurement method is valid and consistent over time and statistically distinguishes healthy motors from those with BRB defects when a reference or threshold value is specified. While the comparison between healthy motors shows a discrepancy, the quantitative analysis shows a smaller estimated difference in mean values between healthy motors than comparing healthy and BRB motors.

Downregulation of the silent potassium channel Kv8.1 increases motor neuron vulnerability in amyotrophic lateral sclerosis.

While voltage-gated potassium channels have critical roles in controlling neuronal excitability, they also have non-ion-conducting functions. Kv8.1, encoded by the KCNV1 gene, is a 'silent' ion channel subunit whose biological role is complex since Kv8.1 subunits do not form functional homotetramers but assemble with Kv2 to modify its ion channel properties. We profiled changes in ion channel expression in amyotrophic lateral sclerosis patient-derived motor neurons carrying a superoxide dismutase 1(A4V) mutation to identify what drives their hyperexcitability. A major change identified was a substantial reduction of KCNV1/Kv8.1 expression, which was also observed in patient-derived neurons with C9orf72 expansion. We then studied the effect of reducing KCNV1/Kv8.1 expression in healthy motor neurons and found it did not change neuronal firing but increased vulnerability to cell death. A transcriptomic analysis revealed dysregulated metabolism and lipid/protein transport pathways in KCNV1/Kv8.1-deficient motor neurons. The increased neuronal vulnerability produced by the loss of KCNV1/Kv8.1 was rescued by knocking down Kv2.2, suggesting a potential Kv2.2-dependent downstream mechanism in cell death. Our study reveals, therefore, unsuspected and distinct roles of Kv8.1 and Kv2.2 in amyotrophic lateral sclerosis-related neurodegeneration.

Monitoring Changes in TMS-Evoked EEG and EMG Activity During 1 Hz rTMS of the Healthy Motor Cortex.

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique capable of inducing neuroplasticity as measured by changes in peripheral muscle electromyography (EMG) or electroencephalography (EEG) from pre-to-post stimulation. However, temporal courses of neuromodulation during ongoing rTMS are unclear. Monitoring cortical dynamics via TMS-evoked responses using EMG (motor-evoked potentials; MEPs) and EEG (transcranial-evoked potentials; TEPs) during rTMS might provide further essential insights into its mode of action - temporal course of potential modulations. The objective of this study was to first evaluate the validity of online rTMS-EEG and rTMS-EMG analyses, and second to scrutinize the temporal changes of TEPs and MEPs during rTMS. As rTMS is subject to high inter-individual effect variability, we aimed for single-subject analyses of EEG changes during rTMS. Ten healthy human participants were stimulated with 1,000 pulses of 1 Hz rTMS over the motor cortex, while EEG and EMG were recorded continuously. Validity of MEPs and TEPs measured during rTMS was assessed in sensor and source space. Electrophysiological changes during rTMS were evaluated with model fitting approaches on a group- and single-subject level. TEPs and MEPs appearance during rTMS was consistent with past findings of single pulse experiments. Heterogeneous temporal progressions, fluctuations or saturation effects of brain activity were observed during rTMS depending on the TEP component. Overall, global brain activity increased over the course of stimulation. Single-subject analysis revealed inter-individual temporal courses of global brain activity. The present findings are in favor of dose-response considerations and attempts in personalization of rTMS protocols.

Entropy-Based Methods for Motor Fault Detection: A Review.

In the signal analysis context, the entropy concept can characterize signal properties for detecting anomalies or non-representative behaviors in fiscal systems. In motor fault detection theory, entropy can measure disorder or uncertainty, aiding in detecting and classifying faults or abnormal operation conditions. This is especially relevant in industrial processes, where early motor fault detection can prevent progressive damage, operational interruptions, or potentially dangerous situations. The study of motor fault detection based on entropy theory holds significant academic relevance too, effectively bridging theoretical frameworks with industrial exigencies. As industrial sectors progress, applying entropy-based methodologies becomes indispensable for ensuring machinery integrity based on control and monitoring systems. This academic endeavor enhances the understanding of signal processing methodologies and accelerates progress in artificial intelligence and other modern knowledge areas. A wide variety of entropy-based methods have been employed for motor fault detection. This process involves assessing the complexity of measured signals from electrical motors, such as vibrations or stator currents, to form feature vectors. These vectors are then fed into artificial-intelligence-based classifiers to distinguish between healthy and faulty motor signals. This paper discusses some recent references to entropy methods and a summary of the most relevant results reported for fault detection over the last 10 years.

Impaired performance of rapid grip in people with Parkinson's disease and motor segmentation

Bradykinesia, or slow movement, is a defining symptom of Parkinson's disease (PD), but the underlying neuromechanical deficits that lead to this slowness remain unclear. People with PD often have impaired rates of motor output accompanied by disruptions in neuromuscular excitation, causing abnormal, segmented, force-time curves. Previous investigations using single-joint models indicate that agonist electromyogram (EMG) silent periods cause motor segmentation. It is unknown whether motor segmentation is evident in more anatomically complex and ecologically important tasks, such as handgrip tasks. Aim 1 was to determine how handgrip rates of force change compare between people with PD and healthy young and older adults. Aim 2 was to determine whether motor segmentation is present in handgrip force and EMG measures in people with PD. Subjects performed rapid isometric handgrip pulses to 20–60% of their maximal voluntary contraction force while EMG was collected from the grip flexors and extensors. Dependent variables included the time to 90% peak force, the peak rate of force development, the duration above 90% of peak force, the number of segments in the force-time curve, the number of EMG bursts, time to relaxation from 90% of peak force, and the peak rate of force relaxation. People with PD had longer durations and lower rates of force change than young and older adults. Six of 22 people with PD had motor segmentation. People with PD had more EMG bursts compared to healthy adults and the number of EMG bursts covaried with the number of segments. Thus, control of rapid movement in Parkinson's disease can be studied using isometric handgrip. People with PD have impaired rate control compared to healthy adults and motor segmentation can be studied in handgrip.

Mixed Eccentricity Fault Detection for Induction Motors Based on Time Synchronous Averaging of Vibration Signals

This paper presents a method based on vibration signals to diagnose rotor mixed eccentricity faults in three-phase squirrel cage induction motors (SCIMs). Rotor eccentricity is a typical fault in SCIMs that significantly affects the performance of electrical machines and results in unwanted vibrations. The time synchronous averaging (TSA) signal, which consists of the harmonics components generated by the fault, is obtained via the characteristic frequency of the eccentricity fault. TSA is decomposed into two signals: TSA-regular and TSA-difference, which are respectively similar to approximate and details of wavelet transform. Based on the fault index, which is the maximum FFT value of the TSA-difference signal, the healthy and faulty motors can be distinguished. The proposed approach is evaluated on an experimental test rig equipped with a 1.5 kW SCIM supplied directly from the power grid under various load conditions. The results illustrate the effectiveness of the proposed approach in detecting the rotor mixed eccentricity faults.

A Fresh Cadaver Study on the Innervation of Brachioradialis and Extensor Carpi Radialis Longus Muscles

Distal nerve transfers have revolutionized peripheral nerve surgery by allowing the transfer of healthy motor nerves to paralyzed ones without causing additional morbidity. Radial nerve branches to the brachialis (Ba), brachioradialis (Br), and extensor carpi radialis longus (ECRL) muscles have not been investigated in fresh cadavers. The radial nerve and its branches were dissected in 34 upper limbs from 17 fresh cadavers. Measurements were taken to determine the number, origin, length, and diameter of the branches. Myelinated fiber counts were obtained through histological analysis. The first branch of the radial nerve at the elbow was to the Ba muscle, followed by the branches to the Br and ECRL muscles. The Ba and Br muscles consistently received single innervation. The ECRL muscle showed varying innervation patterns, with one, two, or three branches. The branches to the Br muscles originated from the anterior side of the radial nerve, whereas the branches to the Ba and ECRL muscles originated from the posterior side. The average myelinated fiber counts favored the nerve to Br muscle over that to the ECRL muscle, with counts of 542 versus 350 and 568 versus 302 observed in hematoxylin and eosin and neurofilament staining, respectively. This study provides detailed anatomical insights into the motor branches of the radial nerve to the Ba, Br, and ECRL muscles. Understanding the anatomy of the radial nerve branches at the elbow is of utmost importance when devising a reconstructive strategy for upper limb paralysis. These findings can guide surgeons in selecting appropriate donor or recipient nerves for nerve transfer in cases of high tetraplegia and lower-type brachial plexus injuries.

The ALS/FTD-related C9orf72 hexanucleotide repeat expansion forms RNA condensates through multimolecular G-quadruplexes

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are neurodegenerative diseases that exist on a clinico-pathogenetic spectrum, designated ALS/FTD. The most common genetic cause of ALS/FTD is expansion of the intronic hexanucleotide repeat (GGGGCC)n in C9orf72. Here, we investigate the formation of nucleic acid secondary structures in these expansion repeats, and their role in generating condensates characteristic of ALS/FTD. We observe significant aggregation of the hexanucleotide sequence (GGGGCC)n, which we associate to the formation of multimolecular G-quadruplexes (mG4s) by using a range of biophysical techniques. Exposing the condensates to G4-unfolding conditions leads to prompt disassembly, highlighting the key role of mG4-formation in the condensation process. We further validate the biological relevance of our findings by detecting an increased prevalence of G4-structures in C9orf72 mutant human motor neurons when compared to healthy motor neurons by staining with a G4-selective fluorescent probe, revealing signal in putative condensates. Our findings strongly suggest that RNA G-rich repetitive sequences can form protein-free condensates sustained by multimolecular G-quadruplexes, highlighting their potential relevance as therapeutic targets for C9orf72 mutation-related ALS/FTD.

Machine learning-based fault diagnosis for three-phase induction motors in ventilation systems

ABSTRACTInduction motors are crucial in industrial drive systems and nuclear and radiological facilities due to their durability and ease of maintenance. Even though the systems are reliable, they have several faults. One of these faults is the instability of the facility’s electrical network. This may lead to catastrophic consequences such as instability in HVAC and ventilation systems, which affects operational safety. Air instability in the HVAC system is a critical concern in the radio pharmaceutical industry. Controlled environments are vital for safety and regulatory compliance. Maintaining air quality and stability is crucial to protect radioactive materials and ensure personnel safety. A healthy and faulty motor condition dataset proposed and used in the study was generated based on a simulated actual supply of electric data. A fault diagnosis is necessary to increase reliability and minimise risks. This paper proposes a simple, reliable, and economical machine-learning-based fault classifier for induction motors. The method analyzes stator currents, motor speed, torque, and three-phase voltage supply to classify some faults resulting from high amplitude and zeros of voltage. A case study of an air handling unit with a 2 hp, 4-pole, 50 Hz three-phase induction motor was modelled and tested with the proposed dataset using various classifiers, including decision trees, logistic regression, discriminant analysis, Naive Bayes, Ensemble, and K-Nearest Neighbor (KNN).

The Influence of Different Inter-Trial Intervals on the Quantification of Intracortical Facilitation in the Primary Motor Cortex.

Intracortical facilitation (ICF) is a paired-pulse transcranial magnetic stimulation (TMS) measurement used to quantify interneuron activity in the primary motor cortex (M1) in healthy populations and motor disorders. Due to the prevalence of the technique, most of the stimulation parameters to optimize ICF quantification have been established. However, the underappreciated methodological issue of the time between ICF trials (inter-trial interval; ITI) has been unstandardized, and different ITIs have never been compared in a paired-pulse TMS study. This is important because single-pulse TMS studies have found motor evoked potential (MEP) amplitude reductions over time during TMS trial blocks for short, but not long ITIs. The primary purpose was to determine the influence of different ITIs on the measurement of ICF. Twenty adults completed one experimental session that involved 4 separate ICF trial blocks with each utilizing a different ITI (4, 6, 8, and 10 s). Two-way ANOVAs indicated no significant ITI main effects for test MEP amplitudes, condition-test MEP amplitudes, and therefore ICF. Accordingly, all ITIs studied provided nearly identical ICF values when averaged over entire trial blocks. Therefore, it is recommended that ITIs of 4-6 s be utilized for ICF quantification to optimize participant comfort and experiment time efficiency.

C9orf72-ALS human iPSC microglia are pro-inflammatory and toxic to co-cultured motor neurons via MMP9

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive motor neuron loss, with additional pathophysiological involvement of non-neuronal cells such as microglia. The commonest ALS-associated genetic variant is a hexanucleotide repeat expansion (HRE) mutation in C9orf72. Here, we study its consequences for microglial function using human iPSC-derived microglia. By RNA-sequencing, we identify enrichment of pathways associated with immune cell activation and cyto-/chemokines in C9orf72 HRE mutant microglia versus healthy controls, most prominently after LPS priming. Specifically, LPS-primed C9orf72 HRE mutant microglia show consistently increased expression and release of matrix metalloproteinase-9 (MMP9). LPS-primed C9orf72 HRE mutant microglia are toxic to co-cultured healthy motor neurons, which is ameliorated by concomitant application of an MMP9 inhibitor. Finally, we identify release of dipeptidyl peptidase-4 (DPP4) as a marker for MMP9-dependent microglial dysregulation in co-culture. These results demonstrate cellular dysfunction of C9orf72 HRE mutant microglia, and a non-cell-autonomous role in driving C9orf72-ALS pathophysiology in motor neurons through MMP9 signaling.

Stator Inter-Turn Short-Circuits Fault Diagnostics in Three-Phase Line-Start Permanent Magnet Synchronous Motors Fed by Unbalanced Voltages

This paper addresses the diagnostics of stator faults in three-phase line-start permanent magnet synchronous motors. More traditional fault diagnostic methodologies are unable to properly diagnose stator inter-turn short-circuit faults in three-phase motors under unbalanced supply voltage conditions, since both conditions impact the fault indicators used for inter-turn short-circuit fault diagnostics in a similar way. In this paper, the relation between the symmetrical components of the three-phase quantities and the harmonic components of the Extended Park’s Vector Approach (EPVA) is established. It is proved that the negative component and the EPVA harmonic component at a frequency twice the supply frequency are directly related to the fault occurrence. It is also proved that the healthy motor negative impedance is constant and not load-dependent. Based on this, the negative impedance/admittance is indirectly analyzed, through the combined use of the Extended Park´s Vector Approach of both voltage and current, and is explored for the fault diagnostics. Experimental results, obtained for different load torques, unbalanced supply voltage values, and fault severity levels, show that inter-turn short-circuit fault diagnostics can be achieved even under unbalanced supply voltage conditions based on the analysis of the motor admittance, at a frequency twice the supply frequency, that is the negative sequence admittance.