Thermographic fault diagnosis of electrical faults of commutator and induction motors

Głowacz, Adam

doi:10.1016/j.engappai.2023.105962

Cited by 69 publications

(10 citation statements)

References 27 publications

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“…Therefore, we propose a local attention-guided Swin-transformer for thermal infrared sports object detection (LAGSwin) to address these limitations. In addition, our proposed thermal infrared moving target detection framework (LAGSwin) can be practically applied to thermal infrared imaging fault diagnosis [ 24 ] and other thermal infrared image target detection [ 25 ] tasks.…”

Section: Related Workmentioning

confidence: 99%

LAGSwin: Local attention guided Swin-transformer for thermal infrared sports object detection

Meng,

Si,

Mao

et al. 2024

PLoS ONE

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Compared with visible light images, thermal infrared images have poor resolution, low contrast, signal-to-noise ratio, blurred visual effects, and less information. Thermal infrared sports target detection methods relying on traditional convolutional networks capture the rich semantics in high-level features but blur the spatial details. The differences in physical information content and spatial distribution of high and low features are ignored, resulting in a mismatch between the region of interest and the target. To address these issues, we propose a local attention-guided Swin-transformer thermal infrared sports object detection method (LAGSwin) to encode sports objects’ spatial transformation and orientation information. On the one hand, Swin-transformer guided by local attention is adopted to enrich the semantic knowledge of low-level features by embedding local focus from high-level features and generating high-quality anchors while increasing the embedding of contextual information. On the other hand, an active rotation filter is employed to encode orientation information, resulting in orientation-sensitive and invariant features to reduce the inconsistency between classification and localization regression. A bidirectional criss-cross fusion strategy is adopted in the feature fusion stage to enable better interaction and embedding features of different resolutions. At last, the evaluation and verification of multiple open-source sports target datasets prove that the proposed LAGSwin detection framework has good robustness and generalization ability.

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Section: Related Workmentioning

confidence: 99%

LAGSwin: Local attention guided Swin-transformer for thermal infrared sports object detection

Meng,

Si,

Mao

et al. 2024

PLoS ONE

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“…The following diagnostic signals can be used for fault detection: Stator current [1,2], vibration signal [1,2], acoustic signal [2], magnetic flux [3,4], and thermal image [5,6]. Fault diagnosis based on acoustic signal analysis has been developed in the literature.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Fault diagnosis of electrical faults of three phase induction motors using acoustic analysis

Lucas,

Lui,

Brumercik

et al. 2024

Bulletin of the Polish Academy of Sciences Technical Sciences

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Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: Rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are low-cost. Changes in the acoustic signal are often observed for faults in induction motors. In this paper, the authors present a fault diagnosis technique for three-phase induction motors (TPIM) using acoustic analysis. The authors analyzed acoustic signals for three conditions of the TPIM: healthy TPIM, TPIM with twobroken bars, and TPIM with a faulty ring of the squirrel cage. Acoustic analysis was performed using Fast Fourier Transform(FFT), a new feature extraction method called MoD-7 (Maxima of differences between the conditions), and deep neural networks: GoogLeNet, and ResNet-50. The results of the analysis of acoustic signals were equal to 100% for the three analyzed conditions. The proposed technique is excellent for acoustic signals. The described technique can be used for electric motor fault diagnosis applications.

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“…17,18 Some studies investigated varied fault diagnosis issues on rotating machinery and components such as induction motors, gear sets, and bearings. [19][20][21][22][23][24][25][26][27] The reciprocating compressor with complex structure operates in a complicated motion. Its mechanism converts rotary motion into linear reciprocating motion.…”

Section: Introductionmentioning

confidence: 99%

“…17,18 Some studies investigated varied fault diagnosis issues on rotating machinery and components such as induction motors, gear sets, and bearings. 19–27…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of reciprocating compressor using Teager-Kaiser energy operator and envelope spectral feature extraction

Hou,

Pan

2024

Advances in Mechanical Engineering

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This paper proposed and implemented the Teager-Kaiser energy operator (TKEO) and envelope spectral analysis techniques for the fault detection of discharge valves of a reciprocating compressor. Based on the extraction of fault features, the instantaneous frequency and amplitude of the signals due to the discharged valve based on energy identification can be effectively characterized by the TKEO that was used to identify the characteristic fault signals accurately. The synthesized signal is processed by envelope spectral analysis and TKEO, which can extract the characteristic signal and eliminate the noise. The experimental design is verified experimentally through different reciprocating compressor gas valve conditions. The simulation results verify the feasibility of the proposed method. The experimental verification is carried out through the measurement signals of the six-cylinder reciprocating compressor under different valve operating conditions. TKEO can remove background noise to obtain reciprocating compressor fault feature signals. Feature extraction is based on TKEO and envelope spectra for fault detection of reciprocating compressors. It is expected to reduce the errors produced by traditional manual fault diagnosis methods and improve the accuracy and efficiency of fault diagnosis. The research results of vibration fault feature extraction using TKEO can be used as the basis for fault diagnosis of the reciprocating compressor system.

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Thermographic fault diagnosis of electrical faults of commutator and induction motors

Cited by 69 publications

References 27 publications

LAGSwin: Local attention guided Swin-transformer for thermal infrared sports object detection

LAGSwin: Local attention guided Swin-transformer for thermal infrared sports object detection

Fault diagnosis of electrical faults of three phase induction motors using acoustic analysis

Fault diagnosis of reciprocating compressor using Teager-Kaiser energy operator and envelope spectral feature extraction

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