Low Latency Bearing Fault Detection of Direct-Drive Wind Turbines Using Stator Current

Nath, Samrat; Wu, Jingxian; Zhao, Yue; Wang, Qiao

doi:10.1109/access.2020.2977632

Cited by 20 publications

(10 citation statements)

References 38 publications

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“…The in-line status monitoring technique was used to analyze the stator-current fault components of the tested induction motor for identification and fault severity in the study by Corne, B et al [ 83 ]. A new, multi-candidate, low-delay detection algorithm for online detection of various possible bearing faults has been reported [ 84 ]. However, if real-time monitoring is combined with an algorithm, the number of possible operations of the machine will be reduced.…”

Section: Detection Methods Based On One-dimensional Signalsmentioning

confidence: 99%

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Yan

Yang

et al. 2022

Sensors

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As a precision mechanical component to reduce friction between components, the rolling bearing is widely used in many fields because of its slight friction loss, strong bearing capacity, high precision, low power consumption, and high mechanical efficiency. This paper reviews several excellent kinds of study and their relevance to the fault detection of rolling bearings. We summarize the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings. The fault signal types are divided into one-dimensional and two-dimensional images, which account for 40.14% and 31.69%, respectively, and their classification is clarified and discussed. We counted the proportions of various methods in the references cited in this paper. Among them, the method of one-dimensional signal detection with external sensors accounted for 3.52%, the method of one-dimensional signal detection with internal sensors accounted for 36.62%, and the method of two-dimensional signal detection with external sensors accounted for 19.72%. The method of two-dimensional signal detection with internal sensors accounted for 11.97%. Among these methods, the highest detection rate is 100%, and the lowest detection rate is more than 70%. The similarities between the different methods are compared. The research results summarized in this paper show that with the progress of the times, a variety of new and better research methods have emerged, which have sped up the detection and diagnosis of rolling bearing faults. For example, the technology using artificial intelligence is still developing rapidly, such as artificial neural networks, convolutional neural networks, and machine learning. Although there are still defects, such methods can quickly discover a fault and its cause, enrich the database, and accumulate experience. More and more advanced techniques are applied in this field, and the detection method has better robustness and superiority.

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Section: Detection Methods Based On One-dimensional Signalsmentioning

confidence: 99%

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Yan

Yang

et al. 2022

Sensors

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“…This novel technology challenges the conventional approach of treating deep neural networks as black boxes, providing a deeper understanding of the underlying mechanisms involved in fault diagnosis. [9] analyses in realtime the statistical patterns of stator currents produced by direct-drive wind turbines (WT), and they observe the low latency detection of bearing failures in WTs. The statistical distribution of WT stator current amplitude at frequencies is found to be influenced by the presence of a fault.…”

Section: Review Of the Related Workmentioning

confidence: 99%

Data-Driven Intelligent Condition Adaptation of Feature Extraction for Bearing Fault Detection Using Deep Responsible Active Learning

Mahesh,

Chandrasekaran,

Ram

et al. 2024

IEEE Access

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The detection of faulty bearings is an essential step in guaranteeing the safe and efficient operation of rotating machinery. Bearings, which also transmit the loads and pressures generated by the machinery, support the rotating shafts. A common method for bearing fault diagnostics is using signal processing techniques. In terms of accuracy, dependability, and sensitivity to various fault types and severity levels, these techniques do, however, have significant limits. To address these limitations, practitioners often integrate signal processing with advanced techniques like machine learning and data analytics to enhance diagnostic accuracy, reliability, and overall effectiveness in bearing fault detection and predictive maintenance. Exploration of various models has demonstrated enhanced results in managing nonlinear data to a certain degree; however, these models face challenges when dealing with intricately complex patterns. Moreover, CNNs can automatically learn relevant features from raw sensor data, capturing intricate patterns and relationships in the data without the need for manual feature engineering. CNNs can be optimized for scalability and real-time processing, essential for applications requiring quick decisions and computationally less expensive for large datasets compared to other models. An optimized one-dimensional Convolutional Neural Network (1D CNN) using different kernel sizes is proposed for predicting and finding bearing problems to overcome these constraints. This method creates a feature vector by applying many filters of various sizes to the input signal. Using the created feature vector, the input signal can be divided into many categories, such as healthy or unhealthy. In comparison to other methods, the proposed technology performs better and offers a high accuracy of 99.52% in bearing fault identification. The 1D CNN model with multiple kernel sizes excels in preserving data structure during dimensionality reduction, as confirmed by comparing t-Distributed Stochastic Neighbor Embedding plots. Particularly, the optimized 1D CNN with multiple kernel sizes accurately classifies faults with minimal errors, showcasing its fault classification proficiency compared with the other state-of-the-art methods. The visualization underscores the methodology's efficacy in discerning intricate fault patterns within the data.

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“…Researchers and scholars have proposed various time-frequency domain analysis methods to address similar issues. Commonly employed techniques include the short-time Fourier transform (STFT) [2], wavelet transform (WT) [3], and empirical mode decomposition (EMD) [4]. Zhou et al [5] and Chen et al [6] applied the STFT to retain the fault characteristics from time-frequency diagrams based on original vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Failure Identification Method of Sound Signal of Belt Conveyor Rollers under Strong Noise Environment

Ban,

Liu,

Yang

et al. 2023

Electronics

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Accurately extracting faulty sound signals from belt conveyor rollers within the high-noise environment of coal mine operations presents a formidable challenge. To address this issue, this study introduces an innovative fault diagnosis method that merges the variational modal de-composition (VMD) model with the Swin Transformer deep learning network model. First, the study employed the adaptive VMD method to eliminate intense noise from the original signal of the rollers, while also assessing the reconstruction accuracy of the VMD signal across different modal components. Subsequently, we delved into the impact of the parameter structure of the Swin Transformer network model on the fault diagnosis accuracy. Finally, the accuracy of the method was validated using a sound test dataset from the rollers. The results indicated that optimizing the K-value of the VMD method effectively reduced the noise in the reconstructed signal, and the Swin Transformer excelled in extracting both local and global features. Specifically, on the conveyor roller sound dataset, it was shown that, after the VMD reconstruction of the signal so that the highest Pearson correlation coefficient corresponded to a modal component of 3 and adjusting the parameters of the Swin Transformer coding layer, the combination of the VMD+Swin-S model achieved an accuracy of 99.36%, while the VMD+Swin-T model achieved an accuracy of 98.6%. Meanwhile, the accuracy of the VMD+Swin-S model was higher than that of the VMD + CNN model combination, with 95.4% accuracy, and the VMD+ViT model, with 97.68% accuracy. In the example application experiments, compared with other models the VMD+Swin-S model achieved the highest accuracy rate at all three speeds, with 98.67%, 98.32%, and 97.65%, respectively. Overall, this approach demonstrated high accuracy and robustness, rendering it an optimal choice for diagnosing conveyor belt roller faults within environments characterized by strong noise.

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Low Latency Bearing Fault Detection of Direct-Drive Wind Turbines Using Stator Current

Cited by 20 publications

References 38 publications

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Data-Driven Intelligent Condition Adaptation of Feature Extraction for Bearing Fault Detection Using Deep Responsible Active Learning

Failure Identification Method of Sound Signal of Belt Conveyor Rollers under Strong Noise Environment

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