Prediction of Rotor Slot Size Variation Through Vibration Signal of Three Phase Induction Motor Using Machine Learning

Kumar, J. Anish; Gowthambigai, M.; Shanker, N. R.; Jasper, J.

doi:10.1007/s42417-023-00859-y

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…GRU-RNN classifier, tested on dataset [15]. The merits and demerits of various regression algorithms are used for prediction is explained in the articles [16][17][18][19][20]. Ensemble learning approach for cancer classification, utilizes particle swarm optimization for breast cancer prediction and the performance is analyzed [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

Gini,

Padmakala

2024

Preprint

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Gene data set collected from a diverse population gene expression profiles, genetic variations, and clinical attributes for earlier detection cancer. Time Series Forecasting (TSF) techniques are applied and exploits temporal dependencies within the gene data, enables the prediction of breast cancer and progression. The proposed methodology such as Particle Swarm Optimization-Long Short Term Memory (PSO & LSTM) and Cat Swarm Optimization -Long Short Term Memory (CSO & LSTM) combines with gene data augmentation and analyse the temporal patterns breast cancer genes. Receiver Operating Characteristic (ROC) curve is used for evaluation the proposed models predictive performance. The proposed methods are validated in traditional dataset and collected gene data sets, from National Center for Biotechnology Information (NCBI). The results are compared with existing classification model and evaluated the effectiveness of the TSF methods such as of CSO-LSTM and PSO-LSTM in prediction of breast cancer diseases. The proposed methods contribute to early detection by leveraging time series forecasting techniques. The proposed model improves the accuracy of and reliability of breast cancer prediction, which enables health professional with more information and potentially enhances the patient outcomes

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

Gini,

Padmakala

2024

Preprint

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“…They can aid a lot in extracting valuable information from the motor signals. They have also been presented in existing literature by a significant number of research papers [9], [10], [11], [12]. The fault detection accuracy and response time is also improved in frequency domain analysis of induction motors [13], [14].…”

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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Smart Anesthesia Level Monitor During Surgery in the Intensive Care Unit

Selvi,

Shijitha,

Mani

et al. 2024

Lecture Notes in Networks and Systems

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Prediction of Rotor Slot Size Variation Through Vibration Signal of Three Phase Induction Motor Using Machine Learning

Cited by 5 publications

References 34 publications

Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

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

Smart Anesthesia Level Monitor During Surgery in the Intensive Care Unit

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