Diagnosis and distinguishment of open-switch and current sensor faults in PMSM drives using improved regularized extreme learning machine

Xiao, Li; Zhang, Liyi; Yan, Zhi; Li, Yanqin; Su, Xiaoqin; Song, Wenqiang

doi:10.1016/j.ymssp.2022.108866

Cited by 14 publications

(4 citation statements)

References 31 publications

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“…The used model and trained system can extract hidden features of the signals and detect the fault type even in the incipient stages and its severity based on the historical data used for training. Since data-driven methods are based regardless of the system model, signal and load, they have better robustness and generalization capability in varying system operating conditions, which is a noticeable advantage of this technique [105]. This technique can be divided into statistical-based and artificial intelligence-based.…”

Section: Data-driven Fdd Methods For Electric Motor Drivementioning

confidence: 99%

“…However, they need higher computa- Extreme learning machine (ELM), different from SVM, is useful for multi-classification purposes and takes advantage of higher training speed. Optimal initial weights and thresholds can be derived just by applying the least square one time, which increases the training speed significantly [105].…”

Section: Data-driven Fdd Methods For Electric Motor Drivementioning

confidence: 99%

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Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

2023

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Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.

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Section: Data-driven Fdd Methods For Electric Motor Drivementioning

confidence: 99%

Section: Data-driven Fdd Methods For Electric Motor Drivementioning

confidence: 99%

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

2023

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“…Ai [12] used a Temporal Convolutional Network for diagnosing Hypersonic Air Vehicle sensors. Xiao [13] used regularized extreme machine learning to detect sensors at different circuit positions. Niu [14] used principal component analysis and improved the Petri net to analyze the speed data of high-speed trains, and the intermittent fault or time-varying fault of the speed sensor was diagnosed.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Vibration Sensors Based on Triage Loss Function-Improved XGBoost

Fan,

Li,

Peng

et al. 2023

Electronics

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Vibration sensors are prone to bias, drift, and other failures. To avoid misjudgments in state monitoring systems and potential safety accidents caused by vibration sensor failures, it is significant to diagnose the faults of vibration sensors. Existing methods for vibration sensor fault diagnosis are primarily based on Deep Learning, but Extreme Gradient Boosting stands out due to its excellent interpretability, and compared to other ensemble learning algorithms, it boasts superior accuracy and efficiency. Therefore, a vibration sensor fault diagnosis method based on Extreme Gradient Boosting is proposed to diagnose seven common types of faults in vibration sensors. To prevent the model from being overwhelmed by simple negative cases during training, a new loss function named Triage Loss is designed to improve the classifier’s performance. The vibration sensor fault diagnosis has confirmed the efficacy and practicality of the suggested approach. The experimental results indicate that the training of the model done using Triage Loss outperforms the training model done using the default loss function, with a maximum improvement of 5.4% accuracy, 5.45% in the F1-score, and 9.87% in the mean Average Precision under different fault rates.

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“…Xiao et al employed regularized extreme learning machine (RELM) to distinguish fault types and identify faulty components. At the same time, LU decomposition was used to solve the output matrix of rELM, so as to shorten the training time of RELM [14] . Yang Ju generates an extreme learning machine classifier with large differences by randomly assigning hidden layer input weights and biases [15] .…”

Section: Introductionmentioning

confidence: 99%

Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble

Zhang

Zhou

et al. 2023

j. of Comput. sci. res.

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Precipitation is a significant index to measure the degree of drought and flood in a region, which directly reflects the local natural changes and ecological environment. It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy. In order to accurately predict precipitation, a new precipitation prediction model based on extreme learning machine ensemble (ELME) is proposed. The integrated model is based on the extreme learning machine (ELM) with different kernel functions and supporting parameters, and the submodel with the minimum root mean square error (RMSE) is found to fit the test data. Due to the complex mechanism and factors affecting precipitation change, the data have strong uncertainty and significant nonlinear variation characteristics. The mean generating function (MGF) is used to generate the continuation factor matrix, and the principal component analysis technique is employed to reduce the dimension of the continuation matrix, and the effective data features are extracted. Finally, the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June, July and August, and a comparative experiment is carried out by using ELM, long-term and short-term memory neural network (LSTM) and back propagation neural network based on genetic algorithm (GA-BP). The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models, and it has high stability and reliability, which provides a reliable method for precipitation prediction.

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Diagnosis and distinguishment of open-switch and current sensor faults in PMSM drives using improved regularized extreme learning machine

Cited by 14 publications

References 31 publications

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

Fault Diagnosis of Vibration Sensors Based on Triage Loss Function-Improved XGBoost

Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble

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