STFT-based induction motor stray flux analysis for the monitoring of cutting tool wearing in CNC machines

Zamudio-Ramírez, Israel; Osornio-Ríos, Roque Alfredo; Díaz-Saldaña, Geovanni; Trejo-Hernández, Miguel; Antonino-Daviu, Jose A.

doi:10.1109/iecon43393.2020.9254795

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…The choice of window size determines the level of time and frequency resolution in the STFT analysis. Smaller windows yield a more precise time resolution but less accurate frequency resolution, whereas larger windows offer an improved frequency resolution at the cost of time resolution [29]. The STFT computes the FFT of a function within a symmetric window function, denoted as W(t).…”

Section: Fundamentals Of Pmsms and Stft Analysis For Evaluating Impac...mentioning

confidence: 99%

Classifying the Percentage of Broken Magnets in Permanent Magnet Synchronous Motors Using Combined Short-Time Fourier Transform and a Pre-Trained Convolutional Neural Network

Ghafouri Matanagh,

Ozturk,

Goktas

et al. 2024

Energies

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In critical applications of electrical machines, ensuring validity and safety is paramount to prevent system failures with potentially hazardous consequences. The integration of machine learning (ML) technologies plays a crucial role in monitoring system performance and averting failures. Among various motor types, permanent magnet synchronous motors (PMSMs) are widely favored for their versatile speed range, enhanced power density, and ease of control, finding applications in both industrial settings and electric vehicles. This study focuses on the detection and classification of the percentage of broken magnets in PMSMs using a pre-trained AlexNet convolutional neural network (CNN) model. The dataset was generated by combining finite element methods (FEMs) and short-time Fourier transform (STFT) applied to stator phase currents, which exhibited significant variations due to diverse broken magnet structures. Leveraging transfer learning, the pre-trained AlexNet model underwent adjustments, including the elimination and rearrangement of the final three layers and the introduction of new layers tailored for electrical machine applications. The resulting pre-trained CNN model achieved a remarkable performance, boasting a 99.94% training accuracy and 0.0004% training loss in the simulation dataset, utilizing a PMSM with 4% magnet damage for experimental validation. The model’s effectiveness was further affirmed by an impressive 99.95% area under the receiver operating characteristic (ROC) curve in the experimental dataset. These results underscore the efficacy and robustness of the proposed pre-trained CNN method in detecting and classifying the percentage of broken magnets, even with a limited dataset.

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Section: Fundamentals Of Pmsms and Stft Analysis For Evaluating Impac...mentioning

confidence: 99%

Classifying the Percentage of Broken Magnets in Permanent Magnet Synchronous Motors Using Combined Short-Time Fourier Transform and a Pre-Trained Convolutional Neural Network

Ghafouri Matanagh,

Ozturk,

Goktas

et al. 2024

Energies

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“…The size of the window defines the time and frequency resolution of the STFT analysis. The shorter the window, the better the resolution in the time domain and the worse in the frequency domain [ 41 ].…”

Section: Short-time Fourier Transform Theoretical Basismentioning

confidence: 99%

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Based on Stator Current Signal Processing and Machine Learning Algorithms

Pietrzak

Wolkiewicz

2023

Sensors

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Reliable fault diagnosis and condition monitoring are essential for permanent magnet synchronous motor (PMSM) drive systems with high-reliability requirements. PMSMs can be subject to various types of damage during operation. Magnetic damage is a unique fault of PMSM and concerns the permanent magnet (PM) of the rotor. PM damage may be mechanical in nature or be related to the phenomenon of demagnetization. This article presents a machine learning (ML) based demagnetization fault diagnosis method for PMSM drives. The time-frequency domain analysis based on short-time Fourier transform (STFT) is applied in the process of PM fault feature extraction from the stator phase current signal. Moreover, two ML-based models are verified and compared in the process of the automatic fault detection of demagnetization fault. These models are k-nearest neighbors (KNN) and multiLayer perceptron (MLP). The influence of the input vector elements, key parameters and structures of the models used on their effectiveness is extensively analyzed. The results of the experimental verification confirm the very high effectiveness of the proposed method.

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“…To achieve this, the analyzed signal is divided in the time domain through temporary windows of the same width, and subsequently frequency content of each of these windows is obtained using the FFT. The size of the time window defines the resolution of time and frequency [30]. An additional advantage of STFT is its suitability for the analysis of nonstationary signals [45].…”

Section: Short-time Fourier Transformmentioning

confidence: 99%

“…The STFT is one of the most popular time-frequency analyses that have been used in the diagnostics of electric motor failures. However, its application in the past has been mainly studied for induction motors [30,31].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Stator Current Data-Driven PMSM Stator Winding Fault Diagnosis

Pietrzak

Wolkiewicz

2022

Sensors

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Permanent magnet synchronous motors (PMSMs) have become one of the most important components of modern drive systems. Therefore, fault diagnosis and condition monitoring of these machines have been the subject of many studies in recent years. This article presents an intelligent stator current-data driven PMSM stator winding fault detection and classification method. Short-time Fourier transform is applied in the process of fault feature extraction from the stator phase current symmetrical components signal. Automation of the fault detection and classification process is carried out with the use of three selected machine learning algorithms: support vector machine, naïve Bayes classifier and multilayer perceptron. The concept and online verification of the original intelligent fault diagnosis system with the potential of a real industrial deployment are demonstrated. Experimental results are presented to evaluate the effectiveness of the proposed methodology.

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STFT-based induction motor stray flux analysis for the monitoring of cutting tool wearing in CNC machines

Cited by 4 publications

References 19 publications

Classifying the Percentage of Broken Magnets in Permanent Magnet Synchronous Motors Using Combined Short-Time Fourier Transform and a Pre-Trained Convolutional Neural Network

Classifying the Percentage of Broken Magnets in Permanent Magnet Synchronous Motors Using Combined Short-Time Fourier Transform and a Pre-Trained Convolutional Neural Network

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Based on Stator Current Signal Processing and Machine Learning Algorithms

Machine Learning-Based Stator Current Data-Driven PMSM Stator Winding Fault Diagnosis

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