Electric vehicles (EVs) rely on electric motors (EMs) for drive, offering an eco-friendly alternative to conventional internal combustion engines. However, EMs in EVs are prone to multiple defects, such as bearing faults and load torque fluctuations, induced by electromagnetic interference (EMI), mechanical misalignments, and variable loading conditions arising from dynamic driving environments and controller-induced torque ripple. The resulting external mechanical load on the electric motor, which in turn modulates the stator current, produces distinct fault-related frequency components in the motor stator current spectrum. This study presents a system for remotely monitoring the health of such EMs which are used to drive EVs. A non-invasive fault detection methodology using Motor Current Signature Analysis (MCSA) which has come of age in present day to detect and characterize bearing-related faults and load torque fluctuations is used. The proposed approach is examined and validated on permanent magnet synchronous motors (PMSM), which are predominantly used as drive motors in EVs. A hall effect current sensor in one situation and a current transformer (CT) in another have been used to measure the current waveform of the stator current in the PMSM motors, which is then analyzed using the principles of MCSA. MCSA identifies the fault frequencies associated with bearing defects and torque fluctuations without requiring motor disassembly or additional vibration sensors. By implementing MCSA into a standalone monitoring system, this study demonstrates a reliable means of detecting bearing and load torque-related faults, ultimately improving the durability, efficiency, and operational safety of electric vehicle drivetrains. Future work can explore scaling this approach with cyber-physical system (CPS)-based architectures for real-time monitoring of EVs, enabling centralized analytics and smart decision-making as has been showcased in the present work.

Classification and Diagnosis of Propeller Damages Using FFT-Based Motor Current Signature Analysis of DC-Link Current

In the domain of Industry 4.0 and the Industrial Internet of Things (IIoT), the reliability of predictive maintenance systems relies heavily on the accurate interpretation of multisensor data. While conventional fault diagnosis frameworks often utilize single-modality signals, the fusion of heterogeneous data sources—specifically Audio, Vibration, and Current (AVC)—offers a more comprehensive representation of machinery health states. However, the practical deployment of such multimodal systems is frequently hindered by sensor malfunctions, transmission errors, or environmental noise, leading to missing or corrupted modalities. This paper presents a novel deep learning architecture, the AVC-FusionNet, which employs a robust cross-attention mechanism designed to dynamically weigh the importance of each modality while explicitly handling missing data scenarios. By integrating Motor Current Signature Analysis (MCSA) with acoustic and vibrational features, our approach captures complex inter-signal correlations that unimodal systems overlook. We introduce a specialized Modality Dropout training strategy that simulates sensor failure, forcing the network to learn resilient representations. Extensive experiments on a rigorous synthetic and real-world industrial dataset demonstrate that the proposed method outperforms state-of-the-art fusion techniques, maintaining high classification accuracy even when one or more modalities are entirely absent.

This paper presents a novel system for the fault diagnosis of induction motors, employing the Transient Motor Current Signature Analysis (TMCSA) method. The developed system operates in a laboratory environment and enables the detection of motor faults during transient conditions, specifically during the startup phase. The diagnostic process relies on tracking characteristic patterns in the time–frequency domain, which are extracted from current signals using advanced signal processing techniques, including the Gabor transform, Short-Time Fourier Transform (STFT), Wigner–Ville distribution, and Continuous Wavelet Transform (CWT). These transformations allow precise identification of fault-related components and their evolution over time. Experimental investigations were conducted for two distinct types of faults: a broken rotor bar and mixed eccentricity. The obtained results demonstrate a high accuracy of fault detection and confirm the robustness of the proposed method. Furthermore, the findings indicate its suitability for practical applications in variable-speed drive systems, where conventional steady-state diagnostic methods are often ineffective.

Electric induction motors are fundamental to industry, where reliability and continuous operation are critical. Though robust, they are prone to faults, particularly in demanding environments such as highway tunnels. Non-invasive diagnostic techniques are widely used for condition monitoring, yet most studies occur under controlled laboratory conditions, limiting their applicability in real-world scenarios. This research investigates the feasibility of applying Motor Current Signature Analysis (MCSA) for monitoring highway tunnel axial fan motors, aiming to determine its effectiveness for real-time diagnostics in industrial environments. Measurements were performed under actual operating conditions, highlighting practical challenges. Data acquisition was implemented remotely from electrical cabins feeding tunnel services, reducing installation complexity and costs compared to in-tunnel measurements. This approach enabled monitoring of all motors in a tunnel using minimal hardware (a single acquisition system equipped with Rogowski sensors) making the solution cost-effective and suitable for periodic measurements. Frequency domain analysis focused on harmonics associated with rotor bar defects and eccentricity, selected for their slow degradation and diagnostic relevance. The magnitude of these harmonics was tracked over time and compared across motors of the same model. Since most of the time the ventilators are de-energized, the periodic measurements can be seen almost as a real-time monitoring, at least for the faults considered, with much lower costs. Results were validated against maintenance reports, confirming bearing faults with eccentricity in two motors, while suspected rotor porosity remained unverified, as expected at low severity. Findings demonstrate that MCSA can provide operational insights for fault detection in tunnel environments, supporting predictive maintenance strategies. A key outcome of this study was selecting and implementing an effective measurement setup for industrial applications, while preparing the base for future machine learning integration to estimate Remaining Useful Life.

Permanent Magnet Synchronous Generators (PMSGs) have become increasingly important in industrial applications such as wind turbine systems due to their high efficiency and power density. However, their operational reliability can be affected by asymmetries such as static eccentricity (SE) and load current unbalance (UnB), which exhibit similar spectral features and are therefore difficult to differentiate using conventional techniques such as Motor Current Signature Analysis (MCSA). Stray flux analysis provides an alternative diagnostic approach, yet single-point measurements often lack the sensitivity required for accurate fault discrimination. This study introduces a diagnostic methodology based on the Space Vector Stray Flux (SVSF) for identifying static eccentricity (SE) and load current unbalance (UnB) faults in PMSG-based systems. The SVSF is derived from three external stray flux sensors placed 120° electrical degrees apart and analyzed through symmetrical component decomposition, focusing on the +5fs positive-sequence harmonic. Two-dimensional Finite Element Analysis (FEA) conducted on a 36-slot/12-pole PMSG model shows that the amplitude of the +5fs harmonic increases markedly under static eccentricity, while it remains nearly unchanged under load current unbalance. To validate the simulation findings, comprehensive experiments have been conducted on a dedicated test rig equipped with high-sensitivity fluxgate sensors. The experimental results confirm the robustness of the proposed SVSF method against practical constraints such as sensor placement asymmetry, 3D axial flux effects, and electromagnetic interference (EMI). The identified harmonic thus serves as a distinct and reliable indicator for differentiating static eccentricity from load current unbalance faults. The proposed SVSF-based approach significantly enhances the accuracy and robustness of fault detection and provides a practical tool for condition monitoring in PMSG.

The operational continuity of electric motors is essential for industrial productivity, as unexpected failures result in economic losses and safety risks. This study proposes a predictive diagnostic system based exclusively on Motor Current Signature Analysis (MCSA) with on-device inference using TinyML, targeting resource-constrained environments. The design includes current signal acquisition through a non-invasive transducer, analog conditioning, preprocessing via root mean square calculation in overlapping windows and normalization, and the training of a lightweight one-dimensional convolutional neural network optimized for microcontroller execution. The prototype was evaluated using a class-balanced dataset, applying standard classification metrics and resource usage profiling. The results show perfect discrimination between normal and abnormal conditions associated with power electronics disturbances, with inference times compatible with real-time monitoring and low memory consumption. It is concluded that MCSA, combined with edge inference, is a viable and low-cost alternative for predictive maintenance, particularly in facilities with infrastructure limitations, and that its integration into multivariable systems could expand coverage to mechanical failure modes.

Induction motors serve as an integral part in industrial applications, where unexpected failures lead to huge financial losses. Predictive maintenance (PdM) has emerged as a key approach for early fault detection and machine health monitoring since demand for reliability and efficiency has been increased. This paper provides a structured analysis of data-driven predictive maintenance techniques for electrical machines, combining conclusions from traditional motor fault diagnosis methods and modern machine-learning-based approaches. For detecting motor abnormalities such as broken rotor bars, bearing defects, and stator faults ,traditional techniques such as Motor Current Signature Analysis (MCSA) and Park’s Vector Analysis (PVA) have been widely used. Under different loading conditions, variations in current, voltage, slip, and vibration patterns serves as strong indicators of motor health. Support Vector Machines (SVM) and other supervised learning algorithms have been used for detecting motor faults with high precision.

Bearing health monitoring is essential for ensuring the reliability and operational safety of induction machines, as bearing faults remain among the most frequent failure modes in rotating electrical equipment. This work contributes to condition monitoring by enhancing the robustness of health indicators and developing a supply-frequency-independent health indicator (HI) for bearing fault diagnosis using Motor Current Signature Analysis (MCSA). The objective is to design an HI capable of reliably representing the bearing degradation state under varying operating conditions, particularly when the supply frequency changes. To achieve this, the study briefly examines the key physical mechanisms governing the detectability of bearing-related spectral signatures—including rotational frequency, unbalanced magnetic pull, eddy currents, skin effect, and hydrodynamic forces. The theoretical analysis establishes the overall trend expected under varying supply frequencies and clarifies how these phenomena collectively influence the spectral characteristics of the fault components and the frequency-dependent evolution of their amplitudes. These insights are experimentally validated using induction machines fitted with bearings of two fault severities. Leveraging this physical understanding, a modified regression-based compensation model is introduced to reduce the frequency-dependent variation in the HI. The resulting compensating factor effectively stabilizes the frequency response, producing a more consistent and monotonic degradation trend across the tested conditions. The proposed method is computationally lightweight, does not require run-to-failure data or detailed physical modeling, and is suitable for real-time implementation. By integrating physical insight with data-driven modeling, this work presents a practical and frequency-independent HI framework that can be readily deployed within digital-twin-based condition monitoring architectures for induction machines.

HighlightsWhat are the main findings?PumpSpectra achieved 91.2% accuracy and rapid diagnostics for centrifugal pump faults in industry.It introduces an explainable, rule-based platform with severity grading for major pump faults.What is the implication of the main finding?Enables cost-saving, sensor-minimal predictive maintenance for pump assets.Supports transparent, trusted decisions for industrial maintenance teams.Reliable detection of faults in centrifugal pump systems is challenging in industrial environments due to harsh operating conditions, limited sensor access, and the need for fast, explainable decisions. We developed PumpSpectra, an industrial Motor Current Signature Analysis (MCSA) platform that processes uploaded stator-current CSV files using FFT/STFT with transparent, rule-based models designed to identify mechanical faults including misalignment, bearing defects, and impeller anomalies; field validation demonstrated misalignment detection. In a case study at the El Oued desalination plant (Algeria; operating points), PumpSpectra achieved 91.2% diagnostic accuracy with a 95% reduction in analysis time compared to manual MCSA post-processing, and a false-positive rate of 3.8% at 0.1 Hz resolution. These results suggest that current-only, explainable analytics can support predictive maintenance programs by accelerating fault triage, improving traceability of decisions, and reducing avoided maintenance costs in pump-driven industrial assets.

Abstract In machine tool turning, accurate identification of surface roughness is crucial for product quality stability. Conventional sensor-based methods that capture images, vibrations, and acoustic emission signals are costly and vulnerable to environmental interference. By contrast, motor current signature analysis is cost-effective, operationally stable, and reliable. However, high-frequency noise, power line interference, and harmonic components in current signals hinder surface roughness identification and limit the recognition accuracy. To address these challenges, this study introduces a novel methodology for identifying surface roughness in machine tool turning operations based on spindle motor current signals. The approach begins with preprocessing the raw current signals through wavelet packet denoising, which effectively suppresses the noise interference. Subsequently, feature reconstruction (FR) techniques were employed to transform the denoised signals into the frequency domain, enabling a deeper exploration of the inherent relationships between the current signal characteristics and surface roughness patterns. Building on this foundation, the power spectral density (PSD) function was utilized to further refine the envelope feature spectrum, thereby selecting the optimal frequency bands that best represented the surface roughness attributes. Concurrently, Pearson correlation coefficients were incorporated to analyze the correlation between these frequency bands, validating the precision and reliability of the extracted features. Finally, the optimal feature spectrum was integrated with a convolutional neural network (CNN) architecture to establish an intelligent recognition model capable of predicting surface roughness in turning processes. The experimental findings demonstrated that the envelope feature spectrum optimization technique, which was based on PSD analysis, extended the capabilities of conventional FR methods and significantly enhanced the CNN-based identification performance. The proposed model achieved 98.61% classification accuracy, an F1-score of 0.964 63, and a mean deviation distance of 0.16 during validation testing, thereby confirming the method’s technical efficacy and measurement accuracy.

Recent trends in research on rotating machinery diagnosis focus on contactless diagnostic technologies. In this paper, novel higher order spectral technologies, based on spectral moduli, are proposed. The proposed technologies estimate statistical dependencies between moduli of harmonics of bearing defect frequencies. Moduli of harmonics of bearing defect frequencies, which appear due to bearing faults, are statistically dependent. The Third Order Modulus (TOM) is a novel higher order spectral signal processing technology developed for rotating machinery diagnostics. The paper presents mathematical expressions for new technologies as well as a detailed description of the signal processing algorithm of motor current for bearings diagnostics. The TOM technology is comprehensively validated via experimental trials for motor bearing diagnosis via motor current signature analysis. Results of experimental trials clearly show that the TOM technology is highly effective for diagnosis of bearing defects. Estimates of the total probabilities of correct diagnosis provided by the TOM technology are 100%. The TOM technology is experimentally compared with the classic bicoherence (CB) technology using eight bearings: four pristine bearings and four damaged bearings with two damage types. Comparison has shown that the TOM technology is more effective than the CB technology.

Motor current analytical models and signature analysis for rotate vector reducer fault diagnosis

Motor Current Signature Analysis (MCSA) faces considerable challenges in diagnosing mechanical faults in motors, particularly in accurately detecting misalignment in rotating machinery. Traditional vibration-based methods often suffer from high hardware costs and difficulties associated with sensor installation. To address these limitations, this study proposes an intelligent diagnosis method for radial misalignment in Permanent Magnet Synchronous Motors (PMSMs) based on Swin-BiGRU multimodal fusion using current signals. First, Adaptive Variational Mode Decomposition (AVMD) is applied to the collected three-phase current signals to eliminate high-frequency noise. Considering the inherent difficulty of fault feature extraction in current signals, Markov Transition Fields (MTF) are used to transform one-dimensional time-series current data into time-frequency images, thereby highlighting subtle or weak fault signatures. The proposed framework employs a dual-branch network: one branch utilizes the Swin Transformer to extract deep features from MTF images, while the other adopts a Bidirectional Gated Recurrent Unit (BiGRU) with Global Attention (GATT) to model the original current time-series. After feature extraction, a bidirectional Cross-Attention mechanism is introduced to enable efficient interaction and enhancement between the multimodal features, improving both diagnostic accuracy and robustness. To validate the proposed method, ablation studies and comparative experiments were conducted before and after denoising. Experimental results demonstrate that under radial misalignment conditions ranging from 0.5mm to 1.5mm, the proposed method achieves an average diagnostic accuracy of 99.375%. Even without the AVMD denoising step, the method maintains a high accuracy of 98.125%, outperforming other benchmark methods. In industrial settings, this method can be integrated into automated equipment.

Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions.

Induction motors are integral to many industrial applications due to their reliability, simplicity, and efficiency. However, they are susceptible to various faults, including bearing issues and broken rotor bars (BRB), which can lead to unplanned downtime, performance degradation, and significant financial losses. Traditional methods, such as Motor Current Signature Analysis (MCSA), have been widely used for fault detection in induction motors, primarily due to their non-invasive nature and effectiveness in identifying faults through stator current analysis. However, MCSA has limitations in detecting complex, multiple fault conditions. Stray Flux Signature Analysis (SFSA) has emerged as a promising alternative, offering the ability to detect faults by measuring magnetic flux variations external to motor housing. This method provides valuable insights into the motor’s internal electromagnetic and mechanical conditions, making it particularly useful for detecting rotor-related faults such as broken rotor bars and eccentricity. Despite its potential advantages, SFSA has not been as extensively researched as MCSA, particularly in the context of multiple fault detection, such as combined BRB and bearing faults. An analysis of publications in the Web of Science reveals that MCSA is a more established method with a larger body of research, while SFSA remains underexplored, with limited studies on its application to multiple fault scenarios. This indicates a significant gap in the research and suggests that further investigation into SFSA could provide a more comprehensive and reliable solution for fault detection in induction motors, especially in multi-fault scenarios.

This article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis of system vibrations, motor current signature analysis, fishbone diagrams, fault trees, multi-agent systems, image recognition, and machine learning techniques. However, there is a noticeable gap in comprehensive studies that specifically address classification of the multiple types of system components failures, class imbalance in the dataset, and the need to reduce data transmitted over the elevator’s internal bus. The developed diagnostic system measures the drive system’s parameters, processes them to reduce data, and classifies 11 device failures. This was achieved by constructing a test bench with a prototype cabin door drive system, identifying the most frequent system faults, developing a data preprocessing method that aggregates every driving cycle to one sample, reducing the transmitted data by 300 times, and using machine learning for modeling. A comparative analysis of the fault detection performance of seven different machine learning algorithms was conducted. An optimal cross-validation method and hyperparameter optimization techniques were employed to fine-tune each model, achieving a recall of over 97% and an F1 score approximately 97%. Finally, the developed data preparation method was implemented in the cabin door drive controller.

Industrial belt failures pose significant challenges to manufacturing operations, often resulting in costly downtime and maintenance interventions. This study presents a comprehensive approach to belt failure analysis, leveraging advanced monitoring and diagnostic techniques. Through the integration of motor current signature analysis (MCSA) and machine learning algorithms, particularly long short-term memory (LSTM) networks, this study aims to predict and detect belt degradation in real time. The methodology involves the collection and pre-processing of raw spectral data from industrial assets, followed by the training and optimization of predictive models. The effectiveness of the approach is demonstrated through extensive testing against real-world data, showcasing its ability to accurately forecast belt failures and enable proactive maintenance strategies. The results obtained from the testing phase reveal a high level of accuracy in predicting belt failures, with the developed models consistently outperforming traditional methods. The incorporation of LSTM networks and swarm intelligence algorithms led to a significant improvement in predictive capabilities, allowing for the early detection of degradation patterns and timely intervention. By harnessing the power of data-driven predictive analytics, the research offers a promising pathway towards enhancing operational efficiency and minimizing unplanned downtime in industrial settings. This study not only contributes to the field of predictive maintenance but also underscores the transformative potential of advanced monitoring technologies in optimizing asset reliability and performance.

Three-phase induction motors are widely adopted in industrial systems due to their robustness, ease of maintenance, and simple operation. However, they are prone to various types of faults, notably stator winding faults. Previous research indicates that 20–40% of three-phase induction motor failures are stator-related, with inter-turn short circuits as a leading cause. These faults can pose significant risks to both the motor and connected equipment. Therefore, the early detection of inter-turn short circuit (ITSC) faults is essential to prevent system breakdowns and improve the safety and reliability of industrial operations. This paper presents a comparative investigation of two distinct diagnostic methodologies for the detection of ITSC faults in induction motors. The first methodology is based on a Motor Current Signature Analysis (MCSA) utilizing the short-time Fourier transform (STFT) for the real-time monitoring of fault-related harmonics. The second methodology is centered around the monitoring of the zero-sequence voltage (ZSV). The findings from several experimental tests performed on a 1.1 kW three-phase induction motor across a range of operating conditions highlight the superior performance of the ZSV method with respect to the MCSA-based STFT method in terms of reliability, rapidity, and precision for the diagnosis of ITSC faults.

Maintenance is essential in ensuring smooth and reliable operation of equipment in the cement plant. Predictive maintenance stands to be cost effective, ensure quality product and plant safety compared to corrective and preventive maintenance. Induction motor plays a crucial role in operation of kiln in the setup of the cement factory. This studyused machine learning models to predict the maintenance conditions of induction motor main drive based on three historical datasets of some of its components.The dataset consist of motor current signature analysis that is made up of rotor current measurements as its variables. The study tested five machine learning models, namely, decision tree, k-nearest neighbours (kNN), support vector machine (SVM), gradient boost tree (GBT), and random forest (RF) to ensure outstanding outcome.A 25:75 ratio holdout validation was used in the study. It has been found that four of the models could accurately predict condition of the induction motor main drive. However, the kNN model performed the best due to its ability to handle nonlinear relationships.It has accuracy of 89.47%, precision of 87.82 %, recall of 87.82% and f-score of 87.82% for the rotor cable dataset 1, while GBT has the least performance among the prediction models with accuracy of 68.42%, precision of 68.42%, recall of 50% and f-score of 57.78%. The performance for the other datasets shows similar trendto the one obtained in the rotor cable dataset with kNN having the best performance and GBT has the least performance among the prediction models. Therefore, GBT model...