A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

Sun, Zhenglong; Machlev, Ram; Wang, Qianchao; Belikov, Juri; Levron, Yoash; Baimel, Dmitry

doi:10.1016/j.egyai.2023.100274

Cited by 17 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the performance of GA-based random forest regression, neural networks, and long short-term memory was compared in the time series forecasting of stator winding temperatures using historical data. The data used here for a demonstration of insulation health monitoring using physical quantities were acquired from a public data set presented by [30] from real laboratory experiments. The data were then preprocessed according to the requirements of the utilized estimation algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…However, the neural network technique demands a large amount of data for training and testing. The deep learning classification of leakage currents between phases and phase-to-neutral is studied using LSTM in [30].…”

Section: Prediction Of Stator Winding Leakage Currentmentioning

confidence: 99%

“…The main contribution of this article involves RF optimization for estimation problem with continuous data. The stator winding temperature and leakage current data were obtained from a laboratory setup as in [30]. GA was used to intricately generate the best parameters, which were then used to fit the training data.…”

Section: Hyperparameter Tuning In Random Forest Regressionmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring of Stator Winding Insulation Degradation through Estimation of Stator Winding Temperature and Leakage Current

Szamel,

Oloo

2024

Machines

View full text Add to dashboard Cite

Switched Reluctance Motors (SRMs), Permanent Magnet Synchronous Motors (PMSMs), and induction motors may experience failures due to insulation-related breakdowns. The SRM rotor is of a non-salient nature and made of solid steel material. There are no windings on the rotor. However, the stator is composed of windings that are intricately insulated from each other using materials such as enamel wire, polymer films, mica tapes, epoxy resin, varnishes, or insulating tapes. The dielectric strength of the insulation may fail over time due to several environmental factors and processes. Dielectric breakdown of the winding insulation can be caused by rapid switching of the winding current, the presence of contaminants, and thermal aging. For reliable and efficient operation of the SRMs and other electrical machines, it is necessary to take into account the physics of the winding insulation and perform appropriate diagnostics and estimations that can monitor the integrity of the insulation. This article presents the estimation problem using a Genetic Algorithm (GA)-optimized Random Forest Regressor. Empirical properties and measurable quantities in the historical data are utilized to derive temperature and leakage current estimation. The developed model is then combined with a moving average function to increase the accuracy of prediction of the stator winding temperature and leakage current. The performance of the model is compared with that of the Feedforward Neural Network and Long Short-Term Memory over the same winding temperature and leakage current historical data. The performance metrics are based on computation of the Mean Square Error and Mean Absolute Error.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Prediction Of Stator Winding Leakage Currentmentioning

confidence: 99%

Section: Hyperparameter Tuning In Random Forest Regressionmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring of Stator Winding Insulation Degradation through Estimation of Stator Winding Temperature and Leakage Current

Szamel,

Oloo

2024

Machines

View full text Add to dashboard Cite

show abstract

“…Also, the study [20] showed that LSTM classification also yielded good performance in case studies of detection and diagnosis of motor electrical disorders. Due to the good performance of this classifier, we suggest that this classifier can be used by society as a benchmark for the development of new and improved motor electrical fault classification algorithms.…”

Section: Introductionmentioning

confidence: 95%

LSTM and Bi-LSTM Models For Identifying Natural Disasters Reports From Social Media

Yunida,

Faisal,

Muliadi

et al. 2023

j.electron.electromedical.eng.med.inform

View full text Add to dashboard Cite

Natural disaster events are occurrences that cause significant losses, primarily resulting in environmental and property damage and in the worst cases, even loss of life. In some cases of natural disasters, social media has been utilized as the fastest information bridge to inform many people, especially through platforms like Twitter. To provide accurate categorization of information, the field of text mining can be leveraged. This study implements a combination of the word2vec and LSTM methods and the combination of word2vec and Bi-LSTM to determine which method is the most accurate for use in the case study of news related to disaster events. The utility of word2vec lies in its feature extraction method, transforming textual data into vector form for processing in the classification stage. On the other hand, the LSTM and Bi-LSTM methods are used as classification techniques to categorize the vectorized data resulting from the extraction process. The experimental results show an accuracy of 70.67% for the combination of word2vec and LSTM and an accuracy of 72.17% for the combination of word2vec and Bi-LSTM. This indicates an improvement of 1.5% achieved by combining the word2vec and Bi-LSTM methods. This research is significant in identifying the comparative performance of each combination method, word2vec + LSTM and word2vec + Bi-LSTM, to determine the best-performing combination in the process of classifying data related to earthquake natural disasters. The study also offers insights into various parameters present in the word2vec, LSTM, and Bi-LSTM methods that researchers can determine.

show abstract

“…Deep learning algorithms have powerful capabilities in data processing and information mining [15]. The latest research shows that CNN can achieve deep potential feature information mining through a large number of sample trainings [16,17]. The application of CNN to the field of voiceprint has achieved certain results [18,19].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Flow Sediment Content of Hydropower Unit Based on Voiceprint Signal

Xiao,

Zeng,

et al. 2024

Energies

View full text Add to dashboard Cite

The hydropower turbine parts running in the sand-bearing flow will experience surface wear, leading to a decline in the hydropower unit’s stability, mechanical performance, and efficiency. A voiceprint signal-based method is proposed for extracting the flow sediment content feature of the hydropower unit. Firstly, the operating voiceprint information of the hydropower unit is obtained, and the signal is decomposed by the Ensemble Empirical Mode Decomposition (EEMD) algorithm, and a series of intrinsic mode functions (IMFs) are obtained. Combined with correlation analysis, more sensitive IMF components are extracted and input into a convolutional neural network (CNN) for training, and the multi-dimensional output of the fully connected layer of CNN is used as the feature vector. The k-means clustering algorithm is used to calculate the eigenvector clustering center of the hydropower unit with a clean flow state and a high sediment content state, and the characteristic index of the hydropower unit sediment content is constructed based on the Euclidean distance method. We define this characteristic index as SI, and the change in the SI value can reflect the degree of sediment content in the flow of the unit. A higher SI value indicates a lower sediment content, while a lower SI value suggests a higher sediment content. Combined with the sediment voiceprint data of the test bench, when the water flow changed from clear water to high sediment flow (1.492 × 105 mg/L), the SI value decreased from 1 to 0.06, and when the water flow with high sediment content returned to clear water, the SI value returned to 1. The experiment proves the effectiveness of the method. The extracted feature index can be used to detect the flow sediment content of the hydropower unit and give early warning in time, so as to improve the maintenance level of the hydropower unit.

show abstract

A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

Cited by 17 publications

References 38 publications

Monitoring of Stator Winding Insulation Degradation through Estimation of Stator Winding Temperature and Leakage Current

Monitoring of Stator Winding Insulation Degradation through Estimation of Stator Winding Temperature and Leakage Current

LSTM and Bi-LSTM Models For Identifying Natural Disasters Reports From Social Media

Feature Extraction of Flow Sediment Content of Hydropower Unit Based on Voiceprint Signal

Contact Info

Product

Resources

About