Health monitoring of induction motors through embedded systems-simulation of broker rotor bar fault and abnormal gear teeth fault

Gandhi, Priyanka; Turk, Neelam; Dahiya, Ratna

doi:10.1016/j.micpro.2020.103077

Cited by 14 publications

(5 citation statements)

References 25 publications

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“…They developed two models based on PSO (particle swarm optimization) and MPSO (modified PSO) and tested these models in their simulation and experimental systems. In their study, they concluded that the increase in fault density became noticeable from the frequency characteristic [12]. On the other hand, Rangel-Magdaleno et al used 3mm 5mm 7mm and 10mm drilled rotors to diagnose different levels of broken rotor failure.…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors

DİŞLİ

Gedikpınar

Şengür

2023

Turkish Journal of Science and Technology

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Modern industrial drives use induction motors (IM) due to their starting and running torque requirements and four-quadrant operation. The voltages and currents of each of the three phases as well as the acceleration and velocity signals may be used to identify failures in the rotor bars of the motor. In the past, traditional signal processing-based feature extraction approaches and machine learning algorithms have been used for the diagnosis of the number of broken rotor bars for a failed IM. In this paper, a new scheme is investigated for the determination of the number of broken rotor bars. Specifically, the deep learning approaches are examined for the mentioned purposes. To this end, transfer learning approaches on the convolutional neural network (CNN) are presented. For denoising, initially, a bandpass filter is employed, and then the signals are converted to time-frequency images by using the continuous wavelet transform (CWT). The obtained images are used for the fine-tuning of the pre-trained ResNet18 model and the deep feature extraction and classification with the support vector machine (SVM) classifier. The classification accuracy is used for performance evaluation metrics. Moreover, the experiments show that the highest accuracy score of 100% is obtained with the deep features that are extracted from the mechanical vibration signal and current signal. On the other hand, a performance comparison with the published approaches is also carried out. Finally, the comparisons show that the proposed method produces better accuracy scores than the compared methods.

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Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors

DİŞLİ

Gedikpınar

Şengür

2023

Turkish Journal of Science and Technology

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“…This approach also suffers from the difficulty of building an accurate diagnostic model. Gandhi et al [14] proposed a different method for detecting the rotor tooth failure and tooth profile abnormal fault of induction motor. However, the full simulation approach to verify gear anomaly lacks practical engineering background support.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis in asynchronous motors based on an optimal deep bidirectional long short-term memory networks

Xu,

Li,

Liu

et al. 2023

Meas. Sci. Technol.

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Fault diagnosis of asynchronous motors has become a pressing need in the metallurgical industry. Due to the complex structure of asynchronous motors, fault types and fault characteristics are diverse, with strong nonlinear relationships between them, which leads to the difficulty of fault diagnosis. To efficiently and accurately diagnose various motor faults, we propose a fault diagnosis method based on an optimal deep bidirectional long short-term memory neural network. First, the three-phase current, multidimensional vibrational signal, and acoustic signal of the asynchronous motor are collected and construct diverse and robust data sample set to enhance the generalization ability of the model. Next, a modified 3D logistics-sine complex chaotic map (3D LSCCM) is constructed to improve the global and local search capabilities of the pigeon swarm optimization algorithm (PIO). Then, we construct a deep bidirectional long short-term memory network (Bid-LSTM) with attention mechanism to mine high-value fault characteristic information. Meanwhile, the optimal hyper-parameters of the deep ABid-LSTM are explored using the modified PIO to improve the training performance of the model. Finally, the fault data samples of asynchronous motor are induced to train and test the proposed framework. By fusing diverse data samples, the proposed method outperforms conventional deep Bid-LSTM and achieves fault diagnosis accuracy of 99.13%. It provides a novel diagnostic strategy for motor fault diagnosis.

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“…Mild steel has been used in different industries owing to their ease of fabrication, strength and low cost (Boulhaoua et al, 2021). It is referred to as low carbon steel with a range of carbon of between 0.05-0.25 percent (Gandhi et al, 2020). It is a combination of iron and ferrite .…”

Section: Introductionmentioning

confidence: 99%

Microstructural evaluations of mild steel and HT250 grey cast iron in ethanol environment

Salawu,

Shittu,

Afolalu

et al. 2023

Nig. J. Technol. Dev.

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The study focused on the response of mild steel and HT250 grey cast iron in varying ethanol concentration. First, a robust review of both materials’ behaviour under different environment was established using microstructural evolutions. Secondly, 30 × 30 × 10 mm dimension of both materials were employed in the study by immersing them in varying ethanol concentrations of BG-43 %, ESG- 42 %, CG-43 %, SQD- 42 % using distilled water as control. The microstructural behaviour showed that every of the sample responded according to the medium of interaction (ethanol). The degree of surface damage also depends on the ethanol concentration and the heavy presence of Fe and oxygen especially in that of grey cast iron. Corrosion cracks and deposits can be attributed to the reaction between the surface and the ethanol. Thus, environment of application is important in material selection. Results suggests that the microstructural changes provide to the material scientist the qualitative corrosion information of these materials in different environment. Conclusively, the study has enlightened the design engineers and material specialist on the need for proper material selection during engineering design.

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Health monitoring of induction motors through embedded systems-simulation of broker rotor bar fault and abnormal gear teeth fault

Cited by 14 publications

References 25 publications

Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors

Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors

Fault diagnosis in asynchronous motors based on an optimal deep bidirectional long short-term memory networks

Microstructural evaluations of mild steel and HT250 grey cast iron in ethanol environment

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