Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning

Wang, Chiao-Sheng; Kao, I-Hsi; Perng, Jau-Woei

doi:10.3390/s21113608

Cited by 30 publications

(13 citation statements)

References 41 publications

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“…One of the most popular fault diagnosis techniques based on motor current analysis is Motor Current Signature Analysis (MCSA). Fast Fourier Transform (FFT) is also a powerful and simple MCSA technique [20]. The effectiveness of the application of this method for the detection of inter-turn short circuits was confirmed among others in [8] and [21].…”

Section: Introductionmentioning

confidence: 90%

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Pietrzak

Wolkiewicz

2021

Electronics

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The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types of faults, including stator winding faults. Stator winding faults are mainly inter-turn short circuits and are among the most common faults in electric motors. In this paper, the possibility of using the spectral analysis of symmetrical current components to extract fault symptoms and the machine-learning-based K-Nearest Neighbors (KNN) algorithm for the detection and classification of the PMSM stator winding fault is presented. The impact of the key parameters of this classifier on the effectiveness of stator winding fault detection and classification is presented and discussed in detail, which has not been researched in the literature so far. The proposed solution was verified experimentally using a 2.5 kW PMSM, the construction of which was specially prepared for carrying out controlled inter-turn short circuits.

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Section: Introductionmentioning

confidence: 90%

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Pietrzak

Wolkiewicz

2021

Electronics

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“…In [19], a detection method for DFs and BFs in a PMSM was studied; it used a Visual Geometry Group-16 network with signal-to-image conversion. Similarly, a 1-D convolutional neural network (CNN) composed of multi-scale feature extraction modules has been proposed for detecting DFs and BFs [20]. In [21], a CNNbased diagnostic method for DFs and mixed damages using raw stator current signals was proposed.…”

Section: Introductionmentioning

confidence: 99%

Severity Estimation for Interturn Short-Circuit and Demagnetization Faults through Self-Attention Network

Lee

Jeong

Kim

et al. 2022

Sensors

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This study presents a novel interturn short-circuit fault (ISCF) and demagnetization fault (DF) diagnosis strategy based on a self-attention-based severity estimation network (SASEN). We analyze the effects of the ISCF and DF in a permanent-magnet synchronous machine and select appropriate inputs for estimating the fault severities, i.e., a positive-sequence voltage and current and negative-sequence voltage and current. The chosen inputs are fed into the SASEN to estimate fault indicators for quantifying the fault severities of the ISCF and DF. The SASEN comprises an encoder and decoder based on a self-attention module. The self-attention mechanism enhances the high-dimensional feature extraction and regression ability of the network by concentrating on specific sequence representations, thereby supporting the estimation of the fault severities. The proposed strategy can diagnose a hybrid fault in which the ISCF and DF occur simultaneously and does not require the exact model and parameters essential for the existing method for estimating the fault severity. The effectiveness and feasibility of the proposed fault diagnosis strategy are demonstrated through experimental results based on various fault cases and load torque conditions.

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“…Currently, the combination of arti cial intelligence and visual communication design has produced a number of intelligent applications [14][15][16][17], such as intelligent logo design, intelligent colour matching, and intelligent image search. In the case of magazine media, for example, intelligent automatic layout design has been implemented through an a priori design framework.…”

Section: Introductionmentioning

confidence: 99%

A Study of Artificial Intelligence-Based Poster Layout Design in Visual Communication

Huo

Wang

2022

Scientific Programming

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In visual communication design, the basic design elements include four types of text, graphics, colour, and layout. While the first three can be called visual elements, layout design is the functional arrangement of visual elements. Layout design is as much about making the viewer receive visual information as it is about making them feel attractive. The combination of artificial intelligence and layout design has now become a popular direction in the field of visual communication design. However, the automatic layout design process achieved through an a priori design framework still requires human involvement and is a semi-intelligent application. To solve the above problems, this study proposes a poster layout design method based on artificial intelligence. The layout composition method consists of a learner and a generator. Firstly, the learner uses the spatial transformation network to learn the classification of layout composition elements and form the initial layout design templates for different composition cases. Secondly, the generator optimises the initial template based on the LeNet architecture using the golden ratio and trilateration parameters to produce multiple optimised templates. The templates are then stored in a library of corresponding templates according to their composition and framing style. The experimental results show that the proposed poster layout composition method achieves a higher accuracy rate than existing methods.

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Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning

Cited by 30 publications

References 41 publications

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Severity Estimation for Interturn Short-Circuit and Demagnetization Faults through Self-Attention Network

A Study of Artificial Intelligence-Based Poster Layout Design in Visual Communication

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