Patterns Based on Clarke and Park Transforms of Wavelet Coefficients for Classification of Electrical Machine Faults

Vitor, Avyner L. O.; Scalassara, Paulo Rogério; Goedtel, Alessandro; Endo, Wagner

doi:10.1007/s40313-022-00946-7

Cited by 14 publications

(2 citation statements)

References 40 publications

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“…Existing literature is largely based on the fault detection algorithms based on Clark's and Park's transformation [1], [2], [3], [4]. In such methods, the three phase signals from the motor which are typically current and voltage measurements are transformed from three phase to two phase signals called d-q transformation.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Methodology for CNN Based Fault Identification in Induction Motors – A Case Study for EV’s

Ahmad,

2024

Preprint

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This paper introduces an advanced methodology employing Convolutional Neural Networks (CNNs) for fault detection in induction motors, with a special focus on electric vehicles (EVs). Induction motors are critical to the operational efficiency of EVs, where their performance directly affects vehicle safety, reliability, and range. Traditional fault detection methods often fail to keep pace with the demands of real-time diagnostics in the increasingly competitive EV market. To address this, this paper proposes a novel CNN-based fault detection system that leverages machine learning to perform non-invasive fault analysis through comprehensive feature extraction and classification from motor signal data. The model uses a combination of spatial and temporal data, processed through a hybrid architecture integrating CNNs with Temporal Convolutional Networks (TCNs) for enhanced fault identification accuracy. The testing and analysis of the model was performed on datasets generated from various EV models under different fault conditions, achieving an average accuracy of 92% in detecting and classifying motor faults, significantly outperforming traditional methods. The results highlight the effectiveness of the approach in early fault detection and its potential in reducing maintenance costs and downtime. This study not only contributes to the robust diagnostics of EV induction motors but also aligns with the broader objectives of Industry 4.0 by enhancing the integration of smart technologies in automotive diagnostics.

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Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Methodology for CNN Based Fault Identification in Induction Motors – A Case Study for EV’s

Ahmad,

2024

Preprint

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“…One of the widely employed techniques in FD is Park's vector approach (PVA) [31,32]. Relying on geometrical properties, PVA allows the representation of the IM signals (current, voltage, or flux) in two-dimensional d-q coordination.…”

Section: Introductionmentioning

confidence: 99%

Stator winding fault detection of induction motors using fast Fourier transform on rotor slot harmonics and least square analysis of the Park's vectors

Ghanbari,

Mehraban

2023

IET Electric Power Appl

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An efficient method for the detection of the stator winding faults (SWFs) of induction motors was dealt based on Park's vector of rotor slot harmonics (RSHs). The induced harmonic voltages of the RSHs in the stator windings are the supreme signatures for fault detection, which can be monitored in the motor currents. Using such a signature, unbalanced supply voltage and stator windings fault situations could be segregated straightforwardly. On the other hand, despite some merits, Park's vector analysis‐based methods suffer from low sensitivity and unreliability in the case of low‐severity faults. To have the advantages of both methods while tackling the shortcomings, Park's vector approach and the RSH signatures are combined in the proposed method. The RSHs are extracted using the fast Fourier transform method and the least squared approach is employed for pattern recognition. Different experimental results under various scenarios, even in the case of low severity faults, confirm that the locus of Park's vector is an elliptical shape in SWFs, while it is shapeless in other cases, such as healthy and unbalanced supply voltage cases.

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A generalized method for diagnosing multi-faults in rotating machines using imbalance datasets of different sensor modalities

Mishra,

Choudhary,

Fatima

et al. 2024

Engineering Applications of Artificial Intelligence

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Patterns Based on Clarke and Park Transforms of Wavelet Coefficients for Classification of Electrical Machine Faults

Cited by 14 publications

References 40 publications

A Comprehensive Methodology for CNN Based Fault Identification in Induction Motors – A Case Study for EV’s

A Comprehensive Methodology for CNN Based Fault Identification in Induction Motors – A Case Study for EV’s

Stator winding fault detection of induction motors using fast Fourier transform on rotor slot harmonics and least square analysis of the Park's vectors

A generalized method for diagnosing multi-faults in rotating machines using imbalance datasets of different sensor modalities

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